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Blog Telecom BFSI Enterprise · Mar 10, 2026

AI is becoming the new Enterprise Infrastructure, just as Databases once did

Enterprise technology rarely evolves gradually. It shifts in layers. Thirty years ago, databases transformed how organisations operated. Today, AI is beginning to do the same; not as another software capability, but as the operational infrastructure enterprises will increasingly run on. To understand what is happening now, we need to remember why databases mattered in the first place. They were not adopted for analytics. They were adopted for reliability. Banks needed transaction certainty. Telco needed billing accuracy. Retail needed inventory integrity. Databases created something enterprises previously lacked - a trusted operational reality. Once systems agreed on the truth, automation became possible, and scale followed. Today’s AI moment is similar in magnitude but different in purpose. From Information Infrastructure to Decision Infrastructure Most enterprises are no longer constrained by lack of data. They are constrained by the ability to act on it coherently and at speed. Dashboards are abundant. Insights are plentiful. Decision execution is fragmented. Different teams optimise different objectives - growth, cost, experience, risk etc. often at the expense of each other. The organisation functions, but it does not behave as a coordinated system. This is the gap AI is starting to fill. Early AI improved prediction: who may churn, which product to recommend, and when demand may change. Useful, but still advisory. Humans remained responsible for deciding and executing actions. The next phase changes that structure. AI is moving from producing recommendations to continuously determining and orchestrating actions across workflows. In other words, it is becoming a decision layer, not just an intelligence layer. From Insights to Decisions to Outcomes: Redefining AI Maturity for Subscription Enterprises The Parallel with the Database Era Databases synchronize enterprise data. AI synchronises enterprise actions. Previously, enterprise failure stemmed from organisations operating on multiple versions of the truth - different systems reported different realities. Today, failure comes from teams agreeing on the numbers but acting on them differently. Here is an example of what happens in an enterprise without a shared decision mechanism: Marketing pushes aggressive acquisition offers to grow users Customer care restricts offers to reduce complaints and complexity Finance limits discounts to protect margins Product promotes premium features to increase usage Individually, every team is doing the right thing for its own KPI. However, collectively - they create conflicting customer experiences and unstable economics. The organisation is aligned on data, but misaligned on actions. As a result, performance does not scale with effort and more activity does not produce better outcomes. A continuously learning AI layer aligns actions to a shared objective in real time, balancing trade-offs dynamically, something process design alone cannot achieve. Just as databases allowed enterprises to trust their data, AI allows enterprises to trust their decisions and actions. Why Chat bots and Co-pilots Are Not the End State Much of the current discussion around AI centres on interfaces - assistants, co-pilots and generative tools. These improve productivity but do not fundamentally change how organisations operate. The database revolution was not created by spreadsheets. The AI revolution will not be created by chat interfaces. Transformation occurs when AI stops helping employees do work and starts ensuring the right work happens across the enterprise. That is infrastructure. Learn why Traditional CVM Is Not Enough; The Future Belongs to Agentic CVM The Operating Model Shift Enterprise technology has historically evolved in layers, each redefining how organisations operate. The first wave was Systems of Record. These systems created a trusted digital memory for enterprises. Core platforms such as ERP, CRM, and billing systems ensured that transactions, customers, and financial data were accurately captured and stored. The objective was reliability and control - establishing a single source of truth for operations. The next evolution brought Systems of Workflow. Enterprises began connecting processes across departments through workflow engines, automation tools, and integrated applications. Work could now move digitally across teams - approvals, service requests, marketing campaigns, product launches. Efficiency improved, but decisions still depended heavily on human interpretation of reports and dashboards. Today, a third layer is beginning to emerge: Systems of Decision. In this model, AI does not simply analyse information - it continuously evaluates options, prioritises actions, and orchestrates decisions across business workflows. Applications still execute processes, but AI becomes the coordinating intelligence that determines what should happen next to achieve a specific business outcome. We refer to this layer as Systems of Decision, not Systems of Intelligence, because intelligence alone does not create enterprise value. What drives outcomes are goal-directed decisions and the actions that follow them. This represents a fundamental shift in how enterprises operate. from automation to autonomy from insights to outcomes from tools to operating principles Organisations adopting this early will not merely operate faster - they will operate coherently. How a Unified Decision Layer Improves Marketplace Efficiency? The impact of a unified decision layer becomes most visible in enterprise marketplaces, where many stakeholders act simultaneously but rarely in coordination. Consider a telecom marketplace offering streaming, gaming and financial add-ons. Traditional campaigns push bundles to large segments, partners demand visibility and customers see multiple offers regardless of intent. Conversion drops and communication increases, forcing even more campaigns to compensate. With a unified AI decision layer, the platform stops broadcasting promotions and starts allocating opportunity. It continuously determines which service should be shown to which customer, at what moment and under what commercial balance. Fewer offers are displayed, yet conversion and retention improve simultaneously. Transform Telecom Services Marketplace Growth & Efficiency A similar shift appears in digital banking ecosystems. Instead of competing product promotions, the bank prioritises a single contextual financial action, balancing risk, profitability and customer value in real time. The institution moves from selling products to guiding financial journeys. A marketplace is ultimately a network of decisions. When decisions are isolated, scale amplifies inefficiency. And when decisions are coordinated, scale amplifies value. The Strategic Consequence Enterprises that embraced databases early became scalable. Enterprises that embrace AI infrastructure early will become adaptive. Competitive advantage is moving from process efficiency to decision quality at scale. The question is no longer whether companies will deploy AI. It is whether AI will remain a feature inside software or become the system coordinating how the business runs. History suggests the latter will define the next generation of market leaders. Also read: https://flytxt.ai/blog/why-agentic-ai-is-missing-link-in-bfsi/ https://flytxt.ai/blog/agentic-ai-use-cases-in-bsfi-and-telecom/ https://flytxt.ai/blog/it-was-business-as-usual-for-flytxt-amidst-the-recent-global-outage-robust-resilient-architecture-pays-off/

Blog Telecom · Nov 20, 2025

Traditional CVM Is Not Enough; The Future Belongs to Agentic CVM

Customer Value Management (CVM) has long driven growth for subscription and usage-based businesses. But today’s customers move faster than the systems designed to understand them. Journeys evolve across multiple touchpoints within minutes, not weeks, and expectations for relevance are higher than ever. One study shows that 70% of customers now expect real-time, personalized interactions, not delayed campaigns. Traditional CVM simply wasn’t built for this world. This is why the next leap forward is Agentic CVM, where autonomous AI agents think, act, and learn continuously to maximize customer lifetime value. Why CVM Teams Are Struggling Today Even with modern analytics and Generative AI tools, CVM teams still struggle with slow decision-making and rigid systems while anchoring the whole planning and execution. Human-led decisions lead to delayed actions, for example, a customer browsing a data pack twice on the app might receive the relevant offer only two days later, by which time they may have already bought from a competitor. Rule-based segmentation cannot keep up with shifting intent; for example - a credit card offer being pushed to “high balance” customers on Monday becomes irrelevant by Wednesday. Data remains scattered across CRM, billing, and campaign tools, creating broken journeys - such as a customer who logs a complaint still receiving upsell offers because channels don’t communicate. And with global privacy laws tightening, many enterprises cannot freely move sensitive data into centralized models, making traditional machine learning slow and shallow. Thus traditional CVM operates on a Plan → Design → Execute → Optimize model suited for monthly pre-planned and scheduled campaigns. However, in the given dynamically changing Telecom marketplace, outcomes are far from desired - declining ROI, irrelevant outreach, and stagnant lifetime value. Agentic CVM: CVM with the ‘Brain’ Inside Agentic CVM introduces a fundamentally different approach. Instead of relying on periodic rule updates or human-driven campaign planning, it uses AI agents that make real-time decisions, autonomously orchestrate actions, and continuously learn from every customer outcome to adapt planning and execution on the go. The fundamental building blocks of agentic CVM are: AI-Driven Decisioning Every engagement is powered by AI that predicts intent, understands context, and decides the best action instantly. When a customer’s app activity drops sharply in the morning, the system can send a contextual WhatsApp nudge immediately, and not after a weekly review. Self-Learning & Autonomous Execution Agentic CVM learns from every outcome. If a customer ignores a renewal offer, the system automatically recalibrates discount sensitivity and channel preference without waiting for a marketer to rewrite the rule. This keeps engagement relevant, timely, and more human-like. Outcome-Directed Optimizations Instead of optimizing campaign performance, Agentic CVM optimizes business KPIs. Teams set goals like “reduce churn by 8%,” and AI dynamically adjusts actions, sequences, incentives, and channels to achieve that target. Summarizing: What Makes Agentic CVM Different Area Traditional Agentic Decisioning Rule-based AI-driven Execution Human-directed Autonomous Adaptation Iterative Self-adaptive Learning Periodic Continuous Governance Opaque Transparent Agentic CVM is not another MarTech add-on; it is a decision-intelligence layer that is deeply embedded into the CVM workflow, continuously influencing every decision and orchestrating every action - directing the workflow towards desired business outcomes. Agentic CVM powered by Flytxt AI Flytxt Agentic AI core blends predictive analytics, prescriptive modeling, and causal reasoning. Instead of merely seeing correlations or generating texts as in predictive or Gen AI tools, it understands why specific actions drive outcomes. For example, it can distinguish between a customer reducing data usage due to disinterest versus a poor network experience. Since it knows the cause-and-effect relationship better, it can trigger the right action; in this case, the retention offers. Federated learning allows Agentic CVM to learn from distributed data sources without moving sensitive data, making it ideal for privacy-first markets. Through autonomous orchestration, the system synchronizes SMS, app, WhatsApp, email, IVR, and care channels so all touchpoints act in harmony. Every decision remains explainable and auditable. Conclusion: Agentic CVM is a ‘living’ system Agentic CVM is always on. Hence, it consistently delivers higher revenue, better retention, faster execution, and more efficient operations. The intelligence compounds every interaction, making the system smarter and more aligned with business value. Agentic CVM changes everything. It senses customer intent as it happens, decides the best action instantly, and executes autonomously, continuously learning and improving. It transforms CVM from a human-anchored process into a self-optimizing business-outcome engine. The future of customer value belongs to enterprises that adopt this autonomous, intelligent model - where every customer interaction becomes smarter, faster, and more valuable than the last.

Blog Telecom · Oct 24, 2025

From Insights to Decisions to Outcomes: Redefining AI Maturity for Subscription Enterprises

The way subscription businesses grow is changing. It’s no longer about how much data they collect, but how intelligently they interpret it, decide upon it, and act on it. As enterprises across telecom, fintech, and media embed AI deeper into their operations, one question keeps surfacing in boardrooms: Are we using AI to inform decisions, or to drive outcomes? This is where a shift in maturity thinking becomes essential. AI Maturity Is Not a Measure of Adoption - It’s a Measure of Use of Intelligence Many organisations equate AI maturity with tool sophistication or model complexity. But in reality, AI maturity is defined by how intelligently an enterprise converts intelligence into outcomes. The most advanced subscription businesses are not the ones running the most complex algorithms. They are the ones where AI understands context, guides decisions in real time, and acts autonomously to deliver measurable improvements in customer experience (CX) and outcomes. At Flytxt, we describe this journey through three connected stages - Insights, Decisions, and Outcomes - that mark the evolution from intelligence to impact. 1. Insights: When AI Learns to Understand This is where intelligence begins. AI starts to make sense of signals - not just capturing data, but learning from it. It begins to answer why customers behave as they do and what those behaviours mean for the business. At this stage: Models identify key experience drivers such as churn triggers or engagement peaks. Contextual reasoning connects customer patterns with business outcomes. Teams start moving from reactive reporting to proactive understanding. Focus: Understanding intent and causality, not just activity. Outcome: Intelligence that helps teams prioritise what truly drives customer experience. Learn how AI-Driven Cross-Selling Boosts Telco Group’s Fixed and Broadband Revenue by 20% across 4 markets 2. Decisions: When Intelligence Becomes Action The next maturity step is to move from understanding to orchestration - where AI becomes a decisioning partner. Predictive models start recommending the next best actions or offers, while embedded Generative AI capabilities create micro-segments, tailor messages, and design campaign variants that reflect the customer’s context, tone, and timing. Human teams now collaborate with AI - validating, fine-tuning, and deploying strategies through systems like Niya-X Marketing Expert or the Value Orchestration CanvasTM. At this stage, AI becomes part of the team - not just a source of insight, but a co-creator of strategy and execution. Focus: Integrating intelligence into daily workflows across marketing, product, and care. Outcome: Faster, context-aware decisions that elevate both efficiency and experience. Unlocking the Potential of Generative AI for Subscription Businesses Learn how Telco Optimizes B2B Lead Generation through Data-Driven Insights and Omni-Channel Engagement 3. Outcomes: When Intelligence Acts Autonomously True maturity emerges when AI closes the loop - deciding, acting, and optimising on its own. Agentic AI, powered by federated learning and adaptive reasoning, orchestrates customer experiences across channels and products in real time. It learns from every interaction, continually improving its ability to predict, personalise, and perform. Generative capabilities are now native to this system - autonomously creating and refining content, offers, and messages to sustain engagement. This is no longer AI as a tool or assistant. This is AI as a living, learning layer of the business aligned to business goals or priorities, governed by intent, and optimised for impact/outcomes. Focus: Moving from assistive intelligence to autonomous outcome-directed orchestration. Outcome: Consistent, anticipatory experiences that deliver measurable business outcomes. Also Learn how An Asian MVNO upgraded postpaid contracts of customers to increase overall Customer Lifetime Value by 2% Meet Agentic AI Niya-X, a multi-agent system for subscription businesses to create autonomous outcome-directed CX workflows; your autonomous decision-making partner. The Three Axes of AI Maturity Axis Definition Shift Observed in Mature Enterprises Data Readiness Integration, timeliness, and context of customer data From data stores to federated hybrid learning architectures AI Integration Depth of AI in workflows and decision cycles From offline analytics to embedded intelligence Autonomy Degree to which AI can act on intent From human-triggered to AI-orchestrated actions Together, these three axes define how far an enterprise has moved - from observing the customer to understanding, deciding, and acting in the customer’s best interest automatically. The Enterprise in the Intelligence Economy: Where Data, Decisions, and Outcomes Converge Flytxt’s Outcome-Directed AI is built precisely for this convergence. It combines massively trained intelligence with federated learning to help subscription enterprises derive insights that understand context, make decisions that are highly contextual, and deliver outcomes that improve experience and business performance autonomously. In doing so, it transforms AI from a decision-support tool into an auto-pilot, one that collaborates, learns, and acts to maximise value across every workflow. Closing the Loop The path from insights to outcomes is not linear, it’s a continuum. Each decision strengthens understanding; each outcome refines intelligence. This continuous learning loop is what defines the future of enterprise AI - one where intelligence doesn’t just describe the business, but runs it. Insights. Decisions. Outcomes. Three words now define the evolution of intelligent enterprises, and that is the foundation of Flytxt’s Enterprise AI philosophy. Telco’s Digital Transformation 2.0: Why Agentic AI is the Way Forward Also Read: https://flytxt.ai/blog/future-proof-your-customer-success-in-the-privacy-first-era/ https://flytxt.ai/blog/agentic-ai-the-next-frontier-in-ai-driven-customer-engagement/

Blog Telecom · Sep 1, 2025

Telco’s Digital Transformation 2.0: Why Agentic AI is the Way Forward

The telecom industry has always been a backbone of digital connectivity. The first wave of digital transformation modernized networks, digitized customer touchpoints, and automated back-office processes. These steps delivered efficiency and cost savings, but they did not fundamentally solve the industry’s growth challenge. Now, the industry is entering Digital Transformation 2.0, a phase defined not by efficiency but by value creation at scale. Customers expect personalized, context-aware experiences, enterprises demand more than connectivity, and competition is intensifying from MVNOs, OTT players, and big tech firms. To thrive in this next phase, Telco need more than traditional AI or one-off digitization initiatives. They need an intelligence layer that can continuously create, capture, and scale value across millions of customer interactions. This is where Agentic AI comes in. But the Telco marketplace is uniquely complex, and current approaches fall short. What Makes the Telco Marketplace Hard to Navigate? Intense Competition and Low Loyalty Telco customers are known for low switching costs. A few dropped calls, frequent data slowdowns, or a competitor offering cheaper unlimited plans are often enough to make them churn. Traditional mass marketing no longer works. Sending thousands of “data top-up” offers in bulk does not pack the same punch when users expect context-aware, real-time offers. At the same time, the rise of MVNOs, OTT players and big tech firms offering alternative connectivity, communication and media services have further squeezed traditional Telco revenues. Operators can no longer rely on legacy marketing tactics to protect loyalty. Declining Traditional Revenues Voice and SMS revenues have dropped dramatically—80% for voice and 94% for SMS over the past decade (GSMA Mobile Economy Report). While data and new services offer promise, McKinsey notes Telco often lack the credibility and ecosystem scale needed to capitalize fully (McKinsey Telecom Outlook). The Service Experience Challenge Customer service is one of the biggest cost and loyalty pressure points for Telco. High call volumes, complex queries, and long resolution times drive up operational expenses and leave customers frustrated. Rising Average Handle Time (AHT) not only eats into margins but also damages customer satisfaction. In many markets, poor service experience is among the top reasons customers switch operators. Traditional approaches that rely heavily on call centers are struggling to keep up with the scale and speed of customer needs in a digital-first world. The Personalization Imperative Customers now expect Telco to act like digital-first brands, delivering real-time, tailored experiences. Without personalization, Telco lose emotional connection. Furthermore, offering super-bundles could increase loyalty—79% of telecom customers say they’d be more loyal to a brand offering such packages (Mobilise Global report). Why AI-Assisted Decision Making Isn’t Enough Most Telco have invested in AI or GenAI. These systems predict churn, recommend next-best offers, or highlight fraud patterns. They provide better visibility, but they stop short of execution. AI delivers insights but not actions. It may predict 100,000 prepaid customers are about to churn after a tariff change, but no human team can manually act fast enough to save them. AI models are static. They need retraining and often fail to adapt quickly to shifting customer behaviors. AI-assisted workflows are reactive. By the time an agent reviews the AI’s suggestion, the customer may have already switched providers or vented frustration on social media. Human dependency slows execution. Dashboards and alerts require staff interpretation. Customers expect instant action, not callbacks hours later. As Deloitte highlights, telecom operators often struggle with fragmented data, slow execution cycles, and gaps between AI recommendations and operational action (Deloitte Telecom Industry Outlook). In short, AI-assisted decision-making improves visibility but keeps customer service reactive, not proactive. Why Agentic AI is the Way Forward Agentic AI closes the gap between insights and action. It does not just recommend what should be done; it executes autonomously in real time, learns from outcomes, and continuously adapts. Here is how Agentic AI transforms Telco’s customer operations: Customer Dynamics and Churn Prevention If a customer shows signs of churn, Agentic AI does more than flag the risk. It designs a personalized retention plan, activates the most effective outreach, and adapts based on the customer’s response. It can: Send a tailored top-up offer instantly when a prepaid user runs out of data. Proactively engage churn-prone customers via SMS or app notifications before they switch. Route high-value customers to the best-trained agents and equip them with the exact next-best offer. Real-Time Campaign and Service Optimization Agentic AI continuously monitors live responses and adjusts offers and campaigns on the fly. This ensures campaigns are never static and are always optimized for maximum impact. Telco move from running broad, scheduled campaigns to orchestrating real-time, dynamic engagements. Proactive Self-Service and Call Center Efficiency Agentic AI detects issues such as failed transactions or billing errors in real time and proactively offers self-service solutions. It can: Route calls to the right agent enriched with AI-driven context. Automate simple resolutions, freeing agents for complex, high-value cases. Reduce Average Handle Time (AHT) while improving first-call resolution. Autonomous Product and Offer Design Telco often need to launch new product bundles fast, such as data + OTT subscriptions or family plans. Agentic AI enables: Dynamic design and launch of product bundles in real time. Identification of micro-segments for upsell and cross-sell opportunities. Continuous testing and optimization to unlock new revenue streams and increase ARPU. Hyper-Personalization at Scale Agentic AI uses predictive intelligence to tailor every offer, message, and experience to the individual. It: Autonomously launches campaigns across preferred channels like app, SMS, email, or call. Learns from every customer interaction and fine-tunes personalization strategies in real time. Turns every engagement into an opportunity to improve loyalty and revenue. From Surviving to Thriving in Telco Telco can no longer rely on the gains of AI-only assistance. The path forward is Digital Transformation 2.0, backed by Agentic AI that drives scalable growth. With Agentic AI, Telco can: Turn reactive processes into proactive customer engagement. Replace generic offers with millions of hyper-personalized experiences. Shift from declining ARPU into sustained, scalable value creation. Agentic AI is not just the next step, it is the foundation of Telco’s future. Also Read: https://flytxt.ai/blog/why-agentic-ai-is-missing-link-in-bfsi/ https://flytxt.ai/blog/agentic-ai-the-next-frontier-in-ai-driven-customer-engagement/

Blog BFSI · Aug 21, 2025

Can Banks and Insurers Really Keep Up? Why Agentic AI is the Missing Link in the BFSI Marketplace

Have you ever wondered why so many banks and insurance companies struggle to expand customer relationships, even while launching new apps, products, and offers every other month? The BFSI world is a tough marketplace. Customers and their financial needs are changing faster than ever, Fintech startups are eating away at traditional players, and regulations keep shifting the goalposts. So here’s the big question: How do you make the right decisions involving customers, products, and business interests when all three are moving targets? The truth is, decisions about what product to offer, how to price it, or when to reach out to a customer are no longer routine. They directly affect revenue, customer experience, and even long-term survival. And increasingly, those decisions are too complex for humans or even traditional AI to handle alone. What Makes the BFSI Marketplace So Hard to Navigate? Customers Who Change Their Minds Overnight Think about your own behavior with banks or apps. Do you expect instant answers on chat? Do you want personalized offers instead of generic emails? You’re not alone. 62% of banking customers are open to AI-driven personalized alerts, such as fee avoidance, and 42% would accept product recommendations from an AI agent (J.D. Power). Banks that fail to personalize risk losing those same customers to Fintech firms that will. Automate omni-channel customer engagement with Flytxt Agentic AI. A Jungle of Products Savings accounts, credit cards, micro-insurance, wealth management, digital wallets. The list keeps growing, and BFSI firms now manage dozens of product lines. But the real challenge is: Which products should be cross-sold together? When is the right time to pitch a loan vs. a credit card? Here’s where the problem deepens. A McKinsey study found that only 7% of banks are fully exploiting critical analytics tools in practice. Most decisions are still being made without comprehensive, real-time insights (McKinsey). The Fintech Tsunami Everywhere you look, Fintech firms are gaining ground. Their advantage lies in simplicity and speed. They roll out products faster, deliver sleek digital experiences, and focus on underserved segments. Traditional banks have size and a history of customer relationships on their side. But without smarter, faster decision-making, even these advantages fade. Unique SaaS solutions for Fintechs to maximise Customer Lifetime Value Business Goals That Keep Shifting In BFSI, business priorities do not stand still. One year, the focus is growth, the next it is profitability, and then compliance takes center stage. Each shift directly affects how banks and insurers deal with customers and products: When margins tighten, the emphasis is on cross-selling more products to existing customers and protecting high-value relationships. When compliance rules change, leaders must quickly adjust how products are structured, priced, and marketed, while still keeping the experience simple for customers. When growth returns to the top of the agenda, the pressure is on to launch new products quickly and win adoption before competitors or Fintech challengers do. A Citi report predicts that AI could add $170 billion in profit to the global banking sector over the next five years, raising profits from $1.4 trillion in 2023 to nearly $2 trillion by 2028 (Financial News London). The takeaway is clear: without AI-driven decisioning, banks will struggle to keep business priorities, product strategies, and customer expectations aligned. Unique SaaS solutions for Insurance Companies to maximise Customer Lifetime Value Why Decision-Making Assisted by AI Isn’t Enough Many BFSI firms already use AI to support their decisions. On paper, it sounds like the problem is solved. AI highlights churn risks, forecasts product demand, or recommends which customers to target. But in reality, even AI-assisted decision-making still leaves critical gaps: AI stops at insights. It tells you what might happen, for example, which customer is likely to close an account, but it does not take the next step to design or trigger the retention action. Models do not adapt fast enough. Traditional AI models often need to be retrained manually, which means they lag behind fast-moving shifts in customer behavior or product uptake. Execution depends on humans. Dashboards and reports still require teams to interpret, debate, and act, slowing down the response. By the time action is taken, the customer opportunity may be gone. Alignment with broader goals is fragmented. AI might optimize for a single metric like reducing churn but miss the bigger context such as profitability, compliance, or growth priorities. In other words, AI-assisted decision-making improves visibility but does not close the strategy-to-execution loop. Leaders still face the same challenge: knowing what should be done but struggling to actually do it at speed and scale. As Deloitte points out, most banks cite data accuracy, usability, and access as their biggest hurdles. This is a clear sign that traditional AI has not yet solved the execution problem (Deloitte Banking Analytics Survey 2024). Unique SaaS solutions for Retail Banking to maximise Customer Lifetime Value The Missing Link: Agentic AI This is where the next leap comes in: Agentic AI. Unlike traditional AI, Agentic AI does not stop at telling you what might happen. It helps make sure the right actions are actually carried out. Agentic AI can solve some of the toughest complexities in BFSI: Customer dynamics: If a customer shows signals of churn, Agentic AI will not just flag it. It will create a tailored retention strategy, trigger the right outreach, and adjust based on how the customer responds. Product complexity: With dozens of offerings, banks often struggle to match the right product to the right customer at the right moment. Agentic AI can simulate product bundling, test pricing scenarios in real time, and automatically adjust campaigns to improve uptake. Competitive speed: Fintech firms move fast. Agentic AI enables banks to match that pace by adapting tactics instantly, whether it is launching a new digital wallet campaign or optimizing cross-sell for wealth management products. Shifting business goals: If the focus shifts from growth to profitability, Agentic AI can redirect strategies to push higher-margin products or lower servicing costs without waiting for human teams to reset models. Unique SaaS solutions for Securities firms to maximise Customer Lifetime Value Think of it this way: traditional AI might tell you that a customer is about to leave. Agentic AI will not only spot it, but also design the right retention strategy, personalize the outreach, trigger it instantly, and then learn from the result. That is the difference between AI that informs and AI that acts. Are you looking for solution to drive customer value management across your whole base or a specific business line? From Surviving to Thriving in BFSI: with Agentic AI The BFSI marketplace will only grow more complex. Customers will demand more, product portfolios will expand, Fintech firms will push harder, and business goals will keep shifting. Accelerate the design and launch of new products with Flytxt Product Expert, while also optimising the performance of your existing product catalog.  So, here is the real choice for leaders: Keep making decisions the old way, with partial insights and delayed execution. Rely on traditional AI that predicts but does not act. Or embrace Agentic AI, the new class of AI that collaborates, executes, and drives measurable results. The future of BFSI belongs to those who adopt Agentic AI. Because in a world where every decision impacts customer experience and revenue growth, you do not just need AI that thinks, you need AI that does. Check out Maximising Customer Lifetime Value for Financial Service Providers Unique SaaS solutions for Fintechs to maximise Customer Lifetime Value (more…)

Blog Telecom · Aug 20, 2025

Transforming Telecom Marketing: The Rise of Agentic AI and Next-Gen CX

“AI in telecom refers to the integration of AI technologies such as Machine learning, Conversational AI, Generative AI, and emerging Agentic AI into telecom operations and services.” Agentic AI has become a game-changer for telecom marketing through intelligent automation, hyper-personalisation, and real-time decision-making. With Next-Gen Customer Experience (CX) platforms, telecoms anticipate needs and prevent issues autonomously, transforming reactive service models to proactive, data-driven ecosystems that elevate loyalty, productivity, and growth. AI-driven transformational impact on Telecom CX The telecom industry is evolving rapidly, and AI is powering it. The worldwide AI telecommunication market in 2024 was valued at around 2.7 billion USD, and it is expected to grow at around 32.6% CAGR till 2034, reaching roughly 45 billion USD. AI transforms CX in the telecom industry through enhanced automation, personalisation, decision-making, and proactive service models. Telecom providers are increasingly utilizing AI for various purposes, including network management, immediate support, and highly personalized engagement. The strategic application of AI leads to enhanced customer satisfaction, improved retention rates, and optimized costs. The Agentic AI drives transformative telecom CX through autonomous, data-driven agents predicting user personas, engagement journeys, churn risk, and next-best actions in real-time. The platform has a dedicated AI for sales, care, and retention, enabling super-personalized interactions, churn prediction, and proactive prevention of churn, context-based product recommendation at every touchpoint. Leveraging Flytxt’s Federated Learning Engine that learns from trillions of real-world interactions while ensuring data privacy enables Telecoms to rapidly adjust offers, address concerns, and maximize the value of their customer base at scale. Digital personalisation creates dynamic experiences Digital personalisation creates dynamic experiences in telecom by leveraging customer data and AI to deliver tailored interactions at every touchpoint. Telecom providers use advanced analytics to design personalized offers, customized plans, and real-time recommendations, such as special deals for movie lovers or travel perks for frequent flyers. Hyper-personalisation engages users with content, support, and promotions uniquely relevant to their preferences and behavior, leading to higher satisfaction, increased loyalty, and reduced churn. This data-driven approach enables telecoms to stand out in a crowded market while optimizing conversion and customer lifetime value. Flytxt enables telecom operators to dynamically adapt messaging and services, increasing engagement, reducing churn, and maximizing lifetime value. This contextual, AI-powered personalisation empowers self-care adoption and proactive support while driving marketing agility and operational efficiency. Learn how Telco Optimizes B2B Lead Generation through Data-Driven Insights and Omni-Channel Engagement Omnichannel CVM gets proactive and contextual In telecom, Omnichannel Customer Value Management (CVM) is becoming proactive and contextual by leveraging AI-driven unified data, real-time insights, and lifecycle-based engagement strategies designed specifically for customer behavior and journeys. This enables timely and personalized interventions, including proactive retention offers and travel-related data packages, which are communicated through customers' preferred channels, such as mobile apps, SMS, etc. AI-based next-best-action systems are at work to determine which offers or services will be shown, thus maximizing the long-term value of customers. By measuring and optimizing outcomes constantly, telecom companies can lower the churn rate, become loyal. Flytxt’s Omnichannel CVM solution is designed for proactive and contextual engagement with subscribers through digital touch points that combine AI, analytics, and marketing automation to clean up customer data and orchestrate AI-driven campaigns. It continuously monitors the activity of the customers and events in their lifecycle to proactively detect upsell, churn risks, or cross-sell opportunities. Targeted campaigns like payment reminders or context-specific offers can be initiated by telecoms on selected customer channels like apps, wallets, and social media. With capabilities like CLTV maximization, segmentation, and real-time event-triggered engagement, Flytxt helps open up multichannel customer enablement, drive up service uptake, and proactively address customers' personal needs, helping telcos to engage with their customers. Telecom CVM is autonomous and self-adaptive Telecom CVM is becoming autonomous and self-adaptive, leveraging advanced AI and machine learning to continuously analyze customer data and behavior in real time. Unlike traditional rule-based campaigns, autonomous CVM systems make real-time decisions, identify micro-segments, and serve personalized offers with no human intervention. They learn from every customer interaction, adapting to changing behaviors, preferences, and market conditions. This self-evolving intelligence transforms CVM from static campaigns into continuous, context-aware conversations across channels. Autonomous CVM Interactive, dynamic, and proactive, enables telecom operators to maximize customer lifetime value whilst improving retention with every interaction, providing relevance, speed, and real business impact. Flytxt’s Telecom CVM platform NEON-dX is self-adaptive and fully automated, leveraging AI and real-time analytics to improve customer-facing engagements. It autonomously understands customer behavior, predicts needs, and dynamically adapts campaigns and offers across multiple digital channels. It is a system that constantly learns from data to make automated, optimized decisions for upsell, cross-sell, retention, and personalized interactions. It allows for event-based actions and context-aware interactions, guaranteeing timely personal offers to the right customers. The Future: Intelligent Self-Thriving CX Marketplace The future of CX is evolving toward an intelligent self-thriving marketplace, where AI-driven systems autonomously optimize interactions, personalize engagements based on how customers react in those moments, or preferences they have shared in the past, to significantly impact business results without constant human oversight. An Intelligent CX Marketplace continuously learns from every interaction that occurs at each channel and touchpoint. It predicts customer needs, suggests an offer based on the needs, and evolves lead management in real-time using Agentic AI and CVM, which self-optimizes. This takes transactional, reactive engagement to proactive and outcome-driven interactions, while benefiting customers and operators. Flytxt’s Intelligent Self-Thriving CX Marketplace will use massively trained Agentic AI, such as its Niya-X platform, to self-drive and continually improve the customer experience flows with little human intervention. Its AI-based Care, Sales, and Retention Experts ensure that telecoms, subscription companies can predict churn, personalize offers, and give frontline staff real-time context about their customers. Federated Learning Engine by Flytxt continuously improves AI accuracy by learning across markets and use cases, boosting customer lifetime value and digital service adoption. This marketplace enables telcos to reduce churn, enhance sales conversions, and deliver hyper-personalized, seamless customer journeys while maximizing operational efficiency. Also Read: https://flytxt.ai/blog/personalization-gets-hyper-with-machine-learning https://flytxt.ai/blog/from-insights-to-decisions-to-outcomes-redefining-ai-maturity-for-subscription-enterprises https://flytxt.ai/blog/telcos-digital-transformation-2-why-agentic-ai-is-the-way-forward https://flytxt.ai/blog/agentic-ai-the-next-frontier-in-ai-driven-customer-engagement

Blog BFSI Telecom · May 24, 2025

Agentic AI use cases in BSFI and Telecom

What is Agentic AI? Agentic AI refers to the ability of artificial intelligence platforms to act autonomously or semi-autonomously, in a way that is guided by established objectives, understanding of data inputs, and knowledge of their environment. These systems can perceive their surroundings, learn from experience, and take actions to achieve objectives without needing constant human intervention. Unlike AI, which is limited in scope and application, Agentic AI has decision-making that enables it to operate in uncertain or dynamic environments. In industry, speed, accuracy, and trust are supremely important, where AI is no longer a luxury- it’s a necessity. AI agents might be what the industry wants. Leveraging advanced machine learning, AI can help reduce challenges of fraud detection, enhance customer service with personalized insights and operations, all while reducing costs and building customer trust. Agentic AI For Banking And Financial Services AI for banking and financial services has traditionally depended on in-person interaction to build trust and assist customers in making complex decisions. In a matter of years, the need for digital experiences that meet customer expectations has never been greater. The BFSI is transformed by an agentic AI that can decide and act on its own to accomplish a particular objective, revolutionizing operations like reducing costs, errors, and response time, improving customer experience, and product innovation. While early AI applications in telecom customer experience focused on automation like automating responses and routine tasks, modern AI, especially agentic AI goes beyond simple automation. Additionally, Agentic AI co-creates strategies and builds tactics, autonomously makes decisions, takes actions, and executes independently, learns from real-time behavior, and fine-tunes offers at the right time, preventing churn. Unlike traditional automation, AI agents are proactive, goal-driven, and capable of adapting to network conditions and user needs with minimal human intervention. Use cases of Agentic AI for Banking and Financial Services Autonomously manage hyper-personalized customer service: AI Agents handle customer inquiries 24/7, troubleshoot issues, provide financial advice, and manage service requests. It reduces call center load, improves response times with less confusion, and enhances customer satisfaction. Claims processing in Insurance: AI agents can process end-to-end claim assessment and validation of fraud checks and disbursement of funds. They interact with customers through chatbots and voice recognition to gather required information and minimize turnaround time. Can Banks and Insurers Really Keep Up? Why Agentic AI is the Missing Link in the BFSI Marketplace - Flytxt | Personalized customer engagement: Agentic AI is transforming banking and finance with hyper-personalized customer experiences, churn prediction, omnichannel campaign orchestration, enabling faster, safer, and more efficient business. Check out Flytxt’s Omni-channel CVM Solution for Retail Banks | Flytxt Cross-acquisition and cross-sell: Agentic AI enables proactive cross-acquisition and cross-sell by sensing customer intent, interpreting marketing signals, and delivering hyper-personalized, real-time recommendations. It drives tailored offers during live interactions, maximizing revenue through timely upsell, retention, and enhanced customer loyalty in subscription-based business and telecom sectors. Use cases of Agentic AI for Telecom CX : Proactive Customer Service: Telecom providers mostly deal with customer services such as technical support, inquiries, billing, and account management. As Agentic AI uses, without human intervention, these problems will be solved. It reduces operational costs, provides 24/7 support, and improves customer satisfaction. Read all about Omni-channel Customer Service - Flytxt | Churn Prediction and Retention: Customer churn remains one of the most persistent challenges in the telecom sector, directly impacting revenue and growth. Traditional retention efforts often rely on generic, broad-based campaigns that fail to engage customers meaningfully. Agentic AI, however, offers a powerful new paradigm by autonomously anticipating churn risks and delivering highly personalized, timely retention interventions. Check out our insightful blog on Predictive customer churn modelling in Telecom industry with high accuracy Flytxt | Hyper Personalization and Proactive Engagement: Agentic AI in telecom leverages hyper-personalization: the AI autonomously processes customer data to create, launch, and supply personalized offers and services in real-time. It communicates with customers in advance through context-aware recommendations and dynamic communications, making customers happy and increasing conversion rate without humans. Check out this video on Hyper-personalion. AI-driven Hyper-Personalisation in action! Operational Efficiency and Automation: Agentic AI automates decision-making across the telecom value chain, streamlining operations and reducing manual interventions. It supports autonomous interaction, campaign execution, and adaptive interventions to deliver speed, accuracy, and scale - while improving customer satisfaction levels at the same time, significantly delivering measurable efficiency and business performance improvements. Future of Agentic AI in BFSI and Telecom Agentic AI has exceptional potential in the BFSI and Telecommunication industry, improving operational efficiency, customer experience, and satisfaction, and decision-making. In BFSI, it improves fraud detection, credit assessment, and loan processing, and claims, while in telecommunication, it strengthens optimization of the network, customer services, and predictive maintenance.

Blog Enterprise · Apr 25, 2025

Agentic AI: The Next Frontier in AI-Driven Customer Engagement

Artificial Intelligence has evolved from simple rule-based systems to highly sophisticated models capable of real-time decision-making. One of the most promising developments in this space is Agentic AI, an approach where AI systems act as autonomous agents, making decisions and executing tasks with minimal human intervention. Unlike traditional AI, which primarily supports decision-making, Agentic AI takes action, adapts dynamically to changing contexts, and continuously optimizes outcomes. This transformation is particularly relevant in customer engagement, where real-time, personalized interactions are crucial for success. A leading example of how Agentic AI is revolutionizing this field is Flytxt, a company specializing in AI-driven Customer Value Management (CVM) solutions. Flytxt has successfully incorporated Agentic AI principles into its platform, enabling telecom operators and other service providers to automate and optimize customer engagement strategies at an unprecedented scale. Understanding Agentic AI Agentic AI refers to AI systems that act with a high degree of autonomy. Instead of merely providing insights, these systems make decisions and take actions based on predefined objectives and real-time data inputs. They can: Perceive the environment by continuously analyzing data. Plan actions based on dynamic conditions and business goals. Act by executing marketing campaigns, adjusting pricing, or modifying service offerings. Learn from feedback loops to improve future decisions. Unlike traditional automation, which follows predefined workflows, Agentic AI is flexible and adaptive. It does not rely solely on historical patterns but continuously optimizes its strategies through reinforcement learning and predictive analytics. Key Benefits of Agentic AI in Customer Engagement 1. Hyper-Personalization at Scale Traditional marketing segmentation often relies on broad customer categories, but Agentic AI enables true one-to-one personalization. By analyzing real-time behavioral data, AI agents can determine the best offer, communication channel, and timing for each customer individually. Flytxt’s AI-driven CVM platform leverages this capability by continuously refining customer profiles. Instead of using static segments, the system dynamically adjusts engagement strategies based on live interactions. This results in highly relevant offers, increased conversion rates, and improved customer satisfaction. 2. Autonomous Campaign Execution Most marketing and customer engagement strategies still require human oversight, from campaign creation to execution and optimization. Agentic AI eliminates this dependency by autonomously running multi-channel campaigns, monitoring outcomes, and adjusting parameters in real time. Flytxt’s platform exemplifies this by automating the entire campaign lifecycle. AI agents: Identify customer needs based on contextual triggers (e.g., data usage patterns, call behavior). Launch targeted campaigns via SMS, email, app notifications, or social media. Continuously track performance and modify strategies without human intervention. This ensures that telecom operators can engage millions of customers effectively without requiring large marketing teams. 3. Proactive Customer Retention Churn prediction is a well-established AI use case, but Agentic AI goes a step further. Instead of merely identifying at-risk customers, it proactively prevents churn by executing retention actions before dissatisfaction escalates. For example, Flytxt’s AI identifies early warning signals—such as reduced usage, increased customer complaints, or competitive price sensitivity—and autonomously triggers retention offers, loyalty incentives, or customer support interventions. The system continuously learns from customer responses, refining its approach to maximize retention success. 4. Continuous Optimization Through Reinforcement Learning Agentic AI does not operate on static rules; it learns and evolves. Through reinforcement learning, AI agents experiment with different engagement strategies, measuring outcomes, and improving future decisions. Flytxt’s AI models analyze thousands of customer interactions daily, testing different message tones, offer structures, and timing variations. Over time, the system identifies the most effective strategies for different customer personas, ensuring higher engagement and ROI. 5. Real-Time Decision Making In a fast-paced digital environment, customer engagement decisions must be made in real time. Traditional analytics often suffer from delays, leading to missed opportunities. Agentic AI enables instantaneous responses to customer actions. For instance, if a telecom customer suddenly exceeds their data limit, Flytxt’s AI can: Instantly detect the usage spike. Predict whether the customer is likely to buy a top-up package. Push a personalized offer via the customer’s preferred channel. Modify the offer dynamically based on real-time response. This level of agility ensures that businesses can capture revenue opportunities as they arise. The Future of Agentic AI in Customer Engagement The shift toward Agentic AI represents a fundamental transformation in how businesses interact with customers. As AI systems become more autonomous and intelligent, they will: Enable fully automated customer journeys, reducing reliance on human decision-making. Provide emotionally aware interactions, recognizing sentiment and adjusting communication accordingly. Drive cross-industry adoption, with sectors like finance, e-commerce, and hospitality leveraging AI-driven personalization at scale. For companies like Flytxt, this evolution opens up new possibilities for delivering seamless, hyper-personalized experiences while maximizing business growth. Conclusion Agentic AI is not just an incremental improvement—it’s a paradigm shift. By enabling AI systems to act, adapt, and optimize autonomously, businesses can achieve unprecedented levels of efficiency and customer satisfaction. Flytxt’s pioneering work in this space demonstrates the tangible benefits of Agentic AI in real-world customer engagement, setting the stage for the next era of AI-driven interactions. As companies embrace this technology, those that leverage Agentic AI effectively will gain a competitive edge, ensuring they stay ahead in the race for customer attention and loyalty. Disclaimer: This blog was originally published as a LinkedIn article in Stephan’s monthly LinkedIn newsletter, Game Changer AI.

Blog Enterprise · Jan 7, 2025

Top 5 AI Trends to Impact CX in 2025

Artificial Intelligence (AI) continues to revolutionise how businesses interact with their customers by creating seamless, personalised, and efficient experiences. As we step into 2025, advancements in AI promise even greater opportunities to elevate customer experiences (CX) by automating processes, improving customer engagement, and providing real-time insights that help businesses anticipate customer needs. Here are the top five AI trends poised to transform the way businesses connect with their audiences: 1. Rise of Agentic AI Agentic AI, or autonomous AI systems capable of initiating and executing multi-step tasks without human intervention, will rise significantly in 2025. Agentic AI systems will act as independent problem-solvers, significantly reducing the effort required to achieve tasks and powering proactive and context-aware customer engagement. From dynamic decision-making to executing complex workflows, agentic AI will empower businesses to deliver next-level customer experiences encompassing all touch points and journeys. Impact: Enhanced operational efficiency, hyper-personalised customer interactions, and a significant reduction in time to respond to any explicit or implicit customer need. 2. First-Party Data Reigns Supreme Two drivers are impacting the move to first-party data. The first factor is the ongoing tightening of data privacy and related regulations. The second is the realisation that hybrid, synthetic, and augmented data, typically used for training AI, actually leads to what’s called “model collapse” over time, reducing AI accuracy substantially and leading to diminished or negative ROI.  As businesses strive to improve the accuracy and impact of their AI initiatives, first-party data will emerge as the backbone of AI training. Businesses will focus on collecting and leveraging consented, high-quality data directly from their customers. This shift will improve the accuracy of AI models, driving better insights and engagement strategies. Impact: Increased trust, massively improved AI accuracy, with personalised customer experiences based on authentic historical data. 3. Personalisation is Immersive and Ubiquitous In 2025, “personalisation” will steadily move towards “hyper-personalisation.” With the advancement of AI technologies, such as Agentic AI, hyper-personalisation will become more attainable and will transcend digital platforms to become a pervasive aspect of customer interactions. Authentic first-party data, improved accuracy, and the ability to incorporate data from every touchpoint of a customer's journey through AI-enabled systems will provide highly immersive experiences, such as augmented reality shopping tailored to individual preferences or real-time adaptation of digital interfaces based on user behavior. Impact: Deepened customer loyalty, heightened satisfaction, and a consistent personalised experience across all touchpoints. 4. AI-Orchestrated Connected Ecosystems AI will play a pivotal role in orchestrating ecosystems where businesses, partners, and customers seamlessly interact. These ecosystems will leverage AI to unify disparate systems, enabling fluid collaboration and real-time information sharing. Connected ecosystems will break down silos of information, not just between various corporate departments but also between partner organisations. Customers will be able to achieve a unified experience as coordinated agents work together to address complex needs. Privacy will be a cornerstone of these ecosystems, ensuring that all data exchanges occur securely and in compliance with regulations. By embedding privacy into the ecosystem design, businesses will not only protect sensitive information but also build trust with their customers. Impact: Streamlined operations, improved customer experiences, enhanced privacy safeguards, and a more connected and efficient value chain. 5. AI Must Promise More Value In 2025, customers will expect AI to deliver not just efficiency but also tangible value. Most analysts report that up to 85% of AI projects fail to deliver expected results (Gartner,Forbes,HBR, and others). Part of this is related to AI data accuracy as mentioned above, and can be addressed via first-party, domain-specific, and well-trained data. But technology alone doesn’t solve the value equation. Delivering value with AI is also related to understanding how to structure AI projects, such as setting clear goals, ensuring and tracking measurability, and properly focusing on concrete AI for CX use cases. Discovering and implementing the right use cases that can potentially deliver short-term as well as long-term transformative business outcomes will be key to justifying your investment in AI. Impact: Stronger value proposition and results for AI-driven initiatives, quick ROI for AI initiatives, short-term & long-term impact, and a higher degree of confidence and trust in AI Final Thoughts As AI technologies advance, the opportunities for elevating customer engagement are boundless. Businesses that embrace these trends early will not only differentiate themselves but also create lasting relationships with their customers, driving sustained value creation for the whole connected ecosystem. A final key lies in leveraging these innovations responsibly and ethically, ensuring trust and transparency remain at the forefront. AI, despite its power to automate and assist, should still be human-centric. Are you ready to harness the power of AI to transform your customer engagement strategy in 2025? Let’s shape the future together!

Blog Enterprise · Sep 24, 2024

The Rise of AI Assistants: Revolutionising Personalisation and Customer Loyalty

This article was originally published in Stefan's newsletter Game Changer AI on LinkedIn. Artificial Intelligence (AI) has become an integral part of our everyday lives, and nowhere is its impact more profound than in the rise of AI assistants. While we’ve seen isolated AI assistants helping users with isolated specific tasks, the future lies in the development of ecosystems of AI assistants—a network of interconnected AI agents working in harmony to manage daily activities and streamline interactions. These ecosystems not only promise to bring unparalleled organization and convenience to end users but also open up powerful new sales channels for businesses. Companies like Flytxt and Perfect-iD are at the forefront of this revolution, pushing the boundaries of AI to create value for both consumers and enterprises. In this article, we explore how an ecosystem of AI assistants can help users organize their lives while providing businesses with a cutting-edge way to engage customers and drive sales. 1. Personal Life Management: From Chaos to Order Imagine having not just one AI assistant, but an entire ecosystem of AI assistants working together to manage every aspect of your day. These assistants would be specialized in different areas—some focused on managing your calendar, others on organizing your finances, optimizing your shopping, handling travel arrangements, or even curating your social media feed. Such an interconnected system of AI would work seamlessly to anticipate your needs and make decisions on your behalf, allowing you to spend more time on things that matter most to you. For instance, consider how AI could manage a busy professional's daily routine. One AI assistant could optimize your schedule by balancing work meetings, family responsibilities, and personal activities. It could communicate with other AIs to ensure your commute is as efficient as possible, alerting you to traffic delays and offering alternative routes. Meanwhile, another AI could automatically reorder groceries based on your consumption patterns, while yet another manages your finances by paying bills, suggesting investment opportunities, or even negotiating better deals on subscriptions. The beauty of such an ecosystem lies in collaboration. These AI assistants would not be siloed, but rather work in tandem, sharing information and insights to create a personalized experience for the user. This kind of cross-functional integration could transform how people organize their lives, making it easier to juggle multiple responsibilities while still having time for leisure and self-care. 2. Enhanced Personalization and Anticipation AI's ability to learn from user behaviour makes personalization one of its most powerful assets. When multiple AI assistants are connected in an ecosystem, the level of personalization can become even more granular. For example, these AIs could analyze your habits, preferences, and patterns to proactively suggest activities, products, or services that align with your needs and desires. This goes beyond mere task automation. For instance, the AI assistants could learn that you prefer running in the morning and recommend the best running routes based on weather conditions, traffic, and your previous running experiences. Or perhaps it could suggest restaurants that match your dietary preferences or recommend gifts for an upcoming anniversary based on your partner’s favorite brands. Flytxt, a leader in AI-driven analytics, exemplifies how businesses can leverage this kind of technology to create personalized experiences for their customers. By using AI to analyze customer data, Flytxt helps companies predict customer needs, enabling highly tailored interactions. In an AI assistant ecosystem, such insights could be used to automatically recommend products or services to users at the precise moment they’re needed, helping users navigate their daily lives more effectively while simultaneously creating opportunities for businesses to drive engagement. 3. Improving Customer Loyalty As AI assistants become more sophisticated and connected, they are not just limited to managing personal tasks—they also hold the potential to become powerful engagement channels for businesses. The rise of e-commerce, along with advancements in AI, is paving the way for a future where AI assistants will do much more than suggest products—they will facilitate entire purchasing journeys, from discovery to checkout, in a completely seamless manner. Here’s how it could work: Imagine you’re planning a vacation. Your AI travel assistant could collaborate with your personal finance AI to find the best travel deals that fit your budget, suggest destinations based on your past preferences, and book your flights and accommodations. At the same time, another AI assistant, integrated with your shopping preferences, might recommend travel gear, such as luggage or clothing suited to the climate of your destination, and place the order for you automatically. Companies that provide such AI-driven services will have an unprecedented opportunity to engage with customers at the right moment, in the right context. This is where Flytxt and Perfect-iD come into play. Flytxt’s AI-powered customer analytics allows companies to predict and respond to individual customer needs, offering targeted recommendations at precisely the right time. This kind of real-time, contextual interaction transforms AI from a tool that simply assists users into an active participant in the purchasing process. Perfect-iD, known for its AI-driven identity verification solutions, could further enhance this experience by ensuring secure and frictionless transactions. As more interactions move to digital platforms, ensuring the security of user data becomes paramount. Perfect-iD’s solutions would enable AI ecosystems to authenticate users seamlessly, facilitating safe and secure payments, account management, and access to personalized services—all within the AI ecosystem itself. 4. Redefining Customer Journeys AI ecosystems hold the potential to redefine how companies approach customer journeys. Traditionally, customer journeys have been linear, starting with awareness, moving to consideration, and ending with a purchase. However, with the rise of AI ecosystems, these journeys could become more dynamic, hyper-personalized, and continuous. For example, AI assistants could provide businesses with valuable data on customer preferences, purchase history, and real-time behaviour, allowing brands to engage customers in ways that feel natural and non-intrusive. Instead of bombarding users with generic ads or promotions, businesses can use AI to provide highly relevant, timely offers directly through the AI assistants. In this new paradigm, AI becomes a trusted intermediary between the customer and the brand. Customers don’t need to browse websites or apps; the AI assistants already know what they want, when they need it, and how to deliver it. Brands can tap into this system to offer their products and services in a way that feels organic and integrated into the customer’s life. 5. Building Trust and Enhancing Security One of the key concerns for consumers in the digital age is trust—particularly when it comes to sharing personal data with AI systems. In an AI ecosystem where assistants have access to a user’s schedule, purchasing habits, and personal preferences, data privacy and security become critical concerns. This is where Perfect-iD’s expertise in secure identity management comes in. Perfect-iD’s AI-driven identity verification tools ensure that users can interact with AI systems safely and securely. Whether it's verifying identities for financial transactions or managing sensitive data, Perfect-iD’s solutions provide the trust infrastructure necessary for AI ecosystems to thrive. By building trust with users AI ecosystems can provide the peace of mind needed to fully integrate these assistants into daily life. When users know that their data is protected and that AI systems are working with their best interests in mind, they are more likely to embrace these systems fully, allowing businesses to benefit from deeper customer engagement and loyalty. Conclusion: A New Frontier for AI and Business The development of an ecosystem of AI assistants represents a new frontier in both personal productivity and business engagement. These interconnected AI agents promise to make daily life more organized and manageable for users while also creating unprecedented opportunities for companies to engage with customers on a personal, intuitive level. As we continue to push the boundaries of AI technology, the possibilities for innovation are limitless. AI ecosystems have the potential to transform how we live, work, and interact with brands, offering a glimpse into a future where technology not only serves us but anticipates our every need. The future of AI is not just about automating tasks—it’s about creating a more personalized, secure, and efficient world where AI assistants work together to enrich our lives while helping businesses thrive.

Video Telecom · Aug 6, 2024

Leveraging AI to reduce data dormancy among Telco subscribers

Video Enterprise Telecom BFSI · May 13, 2024

Generative AI: Speeding Up Innovation and Productivity

Blog Enterprise · May 9, 2024

Unlocking the Potential of Generative AI for Subscription Businesses

Picture this scenario. John, a Product Manager at a large Telco, has to optimise tariff plans for subscription-based products. Despite his experience and diligent efforts, he faces challenges - the complexities of market dynamics, evolving consumer preferences, and real-time competitive pressure. In addition, striking the right balance between attractive plans that resonate with customers and maximising profitability for the company can be a cumbersome process. Typically, it takes months of creative thinking and data analysis for John and his team to produce the right plans or products. Now, what if John has an AI that can mitigate these pain points, automate price optimisations, and finetune the benefits associated with new product designs for customers to ensure a higher expected value in the marketplace? This is where a Generative AI (GenAI) solution comes into play - one that can create novel product innovations and assist John’s team in meeting their business goals faster. How is GenAI different? Subscription businesses have traditionally deployed discriminative AI to meet their business KPIs. Discriminative approaches use existing observed data to assign a finite set of labels to the data. For example, Telcos use such AI techniques to analyse large sets of customer data, predict churn, and segment customers based on demographics, usage patterns, etc., to create hyper-personalised experiences. In general, discriminative AI is beneficial for predicting a repeat event or grouping data by similarity. On the other hand, GenAI is driven by Large Language Models trained with many volumes of data sets. Unlike discriminative AI, GenAI focuses on generating new original data in the form of images, videos, or text responses that are not chosen from existing data. It is this capability that assists Telco product managers like John to come up with out-of-the-world digital product designs faster and imagine what would have been missed otherwise. In general, GenAI is best suited to problems that involve the exploration of new designs for plans, programs, processes, etc., going beyond the remit of discriminative AI. GenAI can boost several business KPIs Given the creative capabilities and massive potential of GenAI, digital businesses can boost several KPIs such as revenue, profitability, and customer lifetime value. Here is a list of various use cases of GenAI in the subscription business: New Product Design: GenAI can automate the design of high-performing digital products with automated price optimisation, benefit tuning, and quick experimentation. This capability helps product managers optimise tariff plans and quickly create new products. Promotional Content Generation: GenAI can create tailored offer promotion content that resonates with the target audience. This feature assists marketers in maximising customer engagement and uptake rates. CX Program Design: GenAI can design a tailored customer experience program. It helps CX teams design a customer care process to resolve a specific issue faster or an omnichannel campaign to meet marketing goals. Agent Conversation Assistance: GenAI can guide human and chatbot agents to handle complex queries and provide accurate responses in simple language. Network SLA Remediation: GenAI can detect breaches of network SLA and generate remediation or upgrade plans and even compensation offers to proactively address shortcomings in desired network experience. On-demand BI Report Generation: GenAI can produce visual and textual insights, reports, and dashboards in response to user queries in natural language. In conclusion, GenAI offers more than merely analysing data; it is capable of generating new content across text, images, and video. The potential use cases for digital subscription businesses are promising, whether designing novel digital products or enhancing chatbot interactions to seem more human-like. Product managers could maximise a product's anticipated value before launch. Customer service leaders might find the right balance between investment in customer support and improvements to customer satisfaction and value. Marketing and customer experience teams could accelerate experimentation and innovation, thereby improving efficiency and revenue generation. Explanation of artificial intelligence decisions in straightforward natural language could provide a further benefit of transparency. For digital subscription businesses seeking to remain at the forefront, investing in GenAI technology appears the prudent course of action.

Blog Telecom · Feb 12, 2024

How AI Will Shape Telecom CX in 2024: 5 Key Trends

Artificial intelligence (AI) is transforming the telecommunications industry. From network optimization and security to customer analytics and virtual assistance, AI applications are enabling telecom operators to improve their efficiency, performance, and profitability. AI adoption will be a defining factor for telcos over the next five years. The global AI in telecommunication market size was valued at USD 1.45 billion in 2022 and is anticipated to grow at a compound annual growth rate (CAGR) of 28.2% from 2023 to 2030. According to a 2023 survey titled 'State of AI in Telecommunications' conducted by Nvidia, telecom companies see efficiencies driven by AI as the most likely path for returns on investment. The survey also revealed that 73% of respondents reported that the implementation of AI had led to increased revenue in the preceding year and also better customer retention and loyalty. 1. Effortless CX Actions with Generative AI Generative AI shows promise in automating resource-intensive and time-consuming tasks for CX teams. Its applications range from generating promotional content tailored to the target audience and engagement channels, to refining messages, visuals, and offers to align with audience personas and business contexts Moreover, Generative AI can assist product managers in designing high-performing digital products through automated price optimization, benefit tuning, and rapid experimentation. Additionally, Gen AI can facilitate the creation of personalized customer experience programs to achieve marketing objectives, such as crafting tailored customer journeys, or designing omni-channel campaigns, or implementing loyalty programs. Furthermore, Gen AI can also aid care teams by generating appropriate steps to resolve specific customer issues or agent queries, and establishing escalation criteria. 2. 5G + AI: An Opportunity in the Making With the advent of 5G, the telecommunications industry stands on the brink of a new era in connectivity and communication. Leveraging 5G to amplify the impact of AI initiatives, companies can transform their positioning from Telcos to Techcos. 5G has the potential to enable telecom operators to expand their offerings beyond traditional connectivity services and delve into developing advanced technology solutions across diverse sectors like entertainment, gaming, health, and education. When paired with AI, 5G can unlock innovative use cases . For instance, smart homes can be elevated to new levels of intelligence. AI-driven virtual assistants can assist homeowners in efficiently managing household tasks such as temperature control, optimizing power usage and so on. Moreover, 5G has the potential to elevate augmented reality (AR) and virtual reality (VR) applications, which rely on swift connectivity and minimal latency. By integrating AI with 5G, AR and VR experiences can be enriched with personalized content and recommendations tailored to individual user preferences, thereby enhancing engagement and experience. In addition to consumer-centric applications, AI/ML can optimize the performance and efficiency of 5G networks themselves. For instance, AI algorithms can automatically detect breaches in network service level agreements (SLAs) and generate remediation or upgrade plans in real-time, ensuring smoother and more reliable network experiences. 3. The Era of Hyper-Personalisation The era of hyper-personalization propelled by AI represents a groundbreaking shift in how businesses engage with their customers. With the proliferation of data and advancements in AI technologies, companies can now tailor experiences to individual users with unprecedented precision. AI algorithms can analyze vast amounts of real-time data, including user behavior, preferences, and contextual information, to deliver highly personalized content, recommendations, and services. From dynamically adapting website layouts and discerning diverse user personas to crafting personalized product suggestions and individualized omni-channel journeys, AI-powered hyper-personalization empowers businesses to create consistent experiences that feel uniquely relevant and valuable to each user. Even self-care portals equipped with AI capabilities have the potential to enhance user engagement, satisfaction, and overall well-being. They can facilitate providing personalized offers or content, or guiding users on app or portal features to improve digital channel adoption and usage, AI empowered self-care portals can also help in having meaningful conversations with customers. Furthermore, AI can leverage real-time data to adapt and refine its offerings over time, ensuring that the hyper-personalized experience evolves in tandem with the user's changing needs and circumstances As AI gets widely adopted, businesses are gearing up for hyper-personalization, which means tailoring things very specifically to each customer. This is going to change how businesses talk to their customers, making things more personalized and focused on what each person wants 4. From Ground to Cloud: The Behind-the-Scenes Improvements For less data-sensitive use cases, 2024 will likely be the year most telecom companies prioritise cloud-based solutions. With the pace at which AI solutions are developing, companies will need to be nimble in their deployments and have the ability to scale resources up or down. Cloud-native AI solutions are the perfect solution to help telcos achieve this requirement. Telcos are also uniquely positioned to leverage edge computing in conjunction with AI in 2024. Edge computing helps telcos balance the reliance of AI models on cloud access with the privacy standards stipulated by regulations like the General Data Protection Regulation (GDPR). Telecom companies can also reduce latency and improve application response times by processing data locally at the network edge, instead of sending it back and forth to the core network or the cloud. AI can also efficiently allocate network resources across multiple edge sites, greatly increasing network availability and resiliency in case of an outage. Despite the improved security and network resilience offered by edge computing, outages remain a possibility for which telcos need to account. Cloud-based AI analytics and monitoring solutions can help telecom companies monitor, diagnose, and resolve network issues in real-time. 5. The Question of Trust and Security One key concern that customers have about AI applications in telecom is how their personal data is collected, processed, and used. Regulations like GDPR also have strict guidelines on what companies can and cannot do with customer data. It is important for telecom companies to adopt responsible and explainable AI practices that ensure transparency, accountability, and fairness in their AI systems. Responsible AI means that telecom companies follow ethical principles and legal regulations when developing and deploying AI solutions. They also need to monitor and audit their AI systems regularly to identify and mitigate any potential risks or biases. AI solutions that use Explainable AI (XAI) can provide clear reasons for how their AI systems make decisions or recommendations. This will be a key component in proving decisions are made in the best interests of the customer and other stakeholders. 2024 is the Year to Adopt AI AI is a powerful technology that can help telcos transform their CX in 2024 and beyond. Telcos that use AI-powered solutions can offer new and innovative services, improve customer service, optimize network management, create hyper-personalised experiences, and benefit customer privacy positively. Careful and considered AI implementation is a high-impact and future-proof solution for improved customer experience. Besides being a customer-centric move, it also brings benefits to the business in the form of improved efficiency and cost savings. Telcos that adopt AI early and partner with experienced and innovative partners with cloud-based AI solutions stand to gain a competitive edge in 2024.

Blog BFSI · Dec 14, 2023

Banking on AI: The Journey from Transaction to Interaction

Customer experience (CX) is a key differentiator for banks in today's competitive and digital-first market. Customers expect convenient, trusted, intuitive, and most importantly personalized services across channels. According to a McKinsey survey of US retail banking customers, banks with high customer satisfaction scores saw deposits grow 84 percent faster than lower rated banks. Satisfied customers are also more likely to buy additional products and stay loyal to their banks. One of the main challenges that banks face is that customers have different financial goals, risk profiles, and communication preferences. To address this challenge, many banks are turning to Artificial Intelligence (AI). In this post, we explore five ways in which AI is transforming CX in retail banking. 1. Chatbots Get An AI Boost Chatbots have become the preferred tool for first level customer service, with the intention of providing a standardized experience and short waiting times. However, legacy chatbots have their own issues, such as: Difficulty understanding complex or nuanced queries Limited knowledge of local financial regulations Heavily reliant on clean and accurate training manuals on the bank’s products and policies To overcome this limitation, banks are upgrading their chatbots with contextual intelligence, allowing them to handle complex queries and provide accurate responses straight away. Some have also evolved beyond ‘chatbots’ to virtual assistants’. For example, Bank of America's Erica proactively informs customers when they are eligible for the bank’s rewards programme, while Capital One’s Eno alerts customers about unusual charges and sends reminders about trial subscriptions. 2. Seamless Data-Driven Personalization Another way that AI can enhance CX is by creating personalized digital experiences for customers based on their interactions, historic usage patterns and latent needs. AI can analyze customer data from various sources, such as transactions, interactions, and feedback to micro-segment customers into groups based on their needs and behaviours. When a Wells Fargo customer buys a travel ticket, the AI may recommend setting up a ‘travel plan’ to calibrate the customer’s account for transactions in other geographies. Leveraging the financial data banks have on their customers, AI can make meaningful product and service recommendations, and provide personalized tips to help customers manage their finances better. Thus through personalization AI is being used to build trust and relevance. 3. Accelerating Financial Inclusion According to the World Bank, about 1.4 billion adults remain unbanked, meaning they do not have an account at a financial institution or through a mobile money provider. Moreover, many people who have bank accounts do not use them regularly or effectively, due to various barriers such as cost, convenience, trust, or literacy. AI can help banks overcome these barriers by making their digital channels more accessible and user-friendly for customers by reducing customer effort. The introduction of multilingual and voice-based processes powered by Large Language Models (LLMs) can greatly increase interactions in areas with low literacy. Banks can also use AI to comb through alternative data beyond traditional credit scores — ranging from mobile phone usage, social media activity, and audio/text data. 4. Boosting Loyalty With the assistance of AI, banks can design hyper-personalized offers and improved loyalty programs for their customers. Using AI intervention, South Korea-based Hyundai Card was able to forecast customer behaviour and identify customers eligible for additional credit lines at their time of need. AI is also particularly useful in determining the optimal timing, channel, and content of marketing campaigns to increase conversion rates and customer satisfaction. An example of this is demonstrated by Bank of Ameria’s Erica assistant. Besides guiding customers to join the bank’s rewards programme, it also nudges them when they are within $10,000 of the next rewards tier, ensuring customers are aware of available opportunities. An effective consumer product or loyalty programme also gives banks a unique community-building opportunity. By deploying AI on first party-data collected from loyalty programmes, banks can effectively segment customer cohorts and leverage opportunities to give each cohort the benefits they aspire to. Depending on cohort, these can range from better forex rates, premium credit cards, or additional perks at premium airport lounges, fostering the sense of being part of a privileged circle. 5. Building Better Partnerships AI can play a pivotal role in helping banks and their partner organizations — such as insurance companies and investment product providers — offer useful collaborative products. Financial services company BBVA has already worked with Google Cloud to make an AI model capable of predicting income and expense streams. These kind of modelling capabilities allow banks to work with partner organizations and cross-sell the right products at the right time. Getting meaningful recommendations instead of unpersonalised offers increases the likelihood of engagement and conversion, elevating the customer experience. While this data gives banks the ability to offer better products to customers, trust and privacy are important factors to be considered. 79% of customers surveyed by Salesforce claimed to be increasingly protective of their personal data. This data makes a strong case for the use of privacy-preserving AI — which is trained on trillions of anonymous and encrypted data points on customer interactions. Using privacy-preserving AI allows banks and partners to continue to work together to offer relevant services without compromising customer trust and safety. AI Adoption: The Early Mover Advantage AI is transforming CX in retail banking by enabling banks to anticipate customer needs, personalize their offerings, and automate their processes. After years of focusing on a digital-first customer strategy, the next logical step for retail banks in improving customer experience is adopting an AI-first strategy. The personalisation, relevance, and privacy offered by AI-powered solutions is already starting to help customers make better money decisions. In many Western and Asian countries, increasingly Banks are viewing AI as a means to measure, monitor and maximise customer lifetime value (CLTV). Given the pace at which AI is progressing, it is important that banks consider a robust, long-term AI strategy with a deep customer focus. As AI adoption in retail banking increases rapidly, banks that move first stand to greatly enhance CX and gain a competitive edge.

Video Telecom · Dec 13, 2023

Webinar: AI-powered CX Value Assurance for 5G and Beyond

Blog BFSI · Nov 15, 2023

Thriving in the Digital Consent Era: Key Considerations for BFSI

In the ever-evolving landscape of the Banking, Financial Services, and Insurance (BFSI) industry, an enormous shift is underway. To protect consumers from unsolicited commercial messages sent via SMS or voice calls, the Telecom Regulatory Authority of India (TRAI) has issued a directive that calls for service providers to deploy a Digital Consent Acquisition (DCA) facility. The goal is to create a unified platform for telecom subscribers to register their consent digitally for receiving promotional messages from entities like BFSI companies. As the DCA regulations become mandatory, it's clear that the BFSI and other industries are undergoing significant changes in their customer acquisition and upsell/cross-sell approaches. The shift towards DCA reflects the broader trend of digital transformation in the business world, driven by advancements in technology and the changing expectations of consumers. It's crucial for businesses to adapt to these changes to remain compliant and competitive. This means reworking their promotional strategies for acquiring new customers and expanding existing relationships with them. Addressing this challenge will require businesses to adopt innovative and customer-centric approaches. Here are some key considerations: Compliance: Ensuring that your organization complies with the new digital consent acquisition regulations is paramount. This may involve updating your processes, technology, and legal frameworks to meet the requirements while safeguarding customer data and privacy. Customer-Centricity: Focusing on the needs and preferences of customers is essential. Providing a seamless and convenient digital experience for customers to give their consent and manage their preferences is crucial. Data Management: Efficiently managing and utilizing customer data is central to personalized and contextual marketing efforts. Implement data analytics and customer segmentation strategies to tailor your outreach programs effectively. Communication: Communication is key. Clearly inform customers about the changes, the benefits of providing consent, and how their data will be used. Transparency and trust-building are vital. Value Proposition: Ensure that your upsell and cross-sell efforts are not solely focused on your organization's interests but also on delivering value to the customer. Demonstrating how customers will benefit from additional products or services can be a win-win. Technology Adoption: Invest in technology that facilitates the digital consent acquisition process and enables efficient data management. Automation and AI can help in personalizing outreach efforts. Monitoring and Adaptation: Continuously monitor the performance of your customer acquisition and upsell/cross-sell programs. Be ready to adapt and refine your strategies based on customer feedback and changing regulations. Invest in technology that facilitates the digital consent acquisition process and enables efficient data management. Automation and AI can help in personalizing outreach efforts. In conclusion, as technological innovation and new consumer protection acts pave the way for digital transformation in the business world, adaptation becomes imperative like never before. Regulations like DCA demand more than compliance from entities of BFSI and other domains. They necessitate a holistic approach, such as embracing customer-centricity, managing data efficiently, communicating effectively, demonstrating value while continuously monitoring performance, and harnessing automation and AI. Such a magnitude of transformation becomes the cornerstone of fostering lasting relationships with customers in the digital age.

Video Telecom Enterprise BFSI · Oct 31, 2023

Movers & Shakers 2023: Flytxt wins Frost & Sullivan Award for the second time!

Video Telecom Enterprise BFSI · Oct 31, 2023

AI for Marketing: Optimising Retention and Win-Back Programs

Video Telecom Enterprise BFSI · Oct 31, 2023

AI for Care: Improving CX and Driving Self-Care Adoption

Video Telecom · Oct 31, 2023

Flytxt CLICX for SAP CX Telecom

Blog Enterprise · Oct 9, 2023

Future-Proof Your Customer Success in the Privacy-First Era

The world of cookies is coming to an end. As privacy-conscious customers continue to influence policies worldwide, marketers can no longer rely on third-party customer data from cookies. In this new privacy-focused era, the future is privacy-preserving AI. The end of cookies Cookies were a marketer’s best tool to tackle large data sets through automation. Although imperfect, they helped personalise customer experiences and improve targeting. The scalability and consistency of cookies made them a go-to source for audience insights. Despite their many advantages, the reality is that cookies were a poor crutch. Their limited shelf-life undercut marketers' ability to evolve personalisation efforts over time, and customers were increasingly concerned about the way companies used their data. The elimination of third-party cookies paves the way toward a more transparent future. Challenges and opportunities The availability of customer data through more devices and channels has elevated customer engagement and created new ways for companies to differentiate. In most cases, new challenges are also introduced in tandem. With added complexity, customers are more anxious than ever regarding the privacy and security of their personal data. In fact: 41% of customers don’t believe companies care about the security of their data 46% of customers feel they’ve lost control over their own data Trust is now paramount to a CSP’s customer loyalty and long-term revenue Privacy and security are the natural counterbalance in a world where data – and lots of it – fuels elevated experiences. Customers want to know that their data is respected and being used legitimately. When transparently applied for their benefit, the majority of customers are actually comfortable with a data-value exchange. The key to connection: Inspire belief CSP’s must emotively connect with customers, be transparent, and deliver on data-value exchange. Businesses have a lot of ground to cover on trust Unhappy customers are not only at risk of leaving, but also tend to strongly voice their displeasure. Despite more businesses understanding this fact, the statistics show room for improvement: 20% of customers had bad customer care experiences, 25% had neutral ones, and only 55% were positive! While these numbers don’t seem particularly unexpected, the concerning factor is the sentiment trend of customers. When asked about their interactions with customer care support: Nearly half say their frustration levels have grown over the past year, 55% feel increasingly stressed, and 52% state that support interactions leave them exhausted. The worse news: 73% of these customers said they will switch to a competitor after multiple bad experiences—and more than 50% will head for the exit after a single unsatisfactory interaction. Embracing customer empathy, empowering trust, and delivering personalization Larger CSP’s and organisations relied on processing cookies in an attempt to deliver personalisation at scale. With cookies sunsetting and the era of first-party data upon us, it will never again be humanly possible to tackle this challenge. Modern data collection is reliant on integrated OSS and BSS systems, mobile apps, and connected third-party data ecosystems. In this context, developing a behavioral first-party data graph and processing it manually becomes an insurmountable challenge. This is a challenge suited for Privacy-preserving AI. Businesses can lean on its unique strengths to deliver trusted, personalised experiences that leverage customer empathy and build trust. Tomorrow’s leaders will adopt AI-based paradigms for customer success. Leveraging AI for CX will help reduce customer churn, improve customer experiences and care interactions, and boost revenue through increased loyalty and personalised services and offers. Privacy Preserving AI is the only viable tool able to deliver personalised customer experiences and conquer customer happiness, growth, loyalty, and revenue metrics. The ability to collect first party data and subsequently apply models trained on trillions of anonymous, encrypted, and relevant customer interactions is unprecedented. It’s also a difficult challenge to achieve, which is why privacy preservation – and the transparent communication of secure privacy – will be paramount. CSP leaders will no longer have the luxury of ignoring customer sentiment and emotions. Those that adopt specialized AI tools to augment their marketing and business teams and deliver amazing customer experiences will thrive. Those that don’t, will struggle. Sources: 6th Edition State of the Connected Customer from Salesforce

Blog Enterprise · Aug 10, 2023

How Intelligent Self Care helps elevate the Customer Experience

Self-service has become a game-changer in the realm of customer support, offering several perks that traditional customer care channels simply cannot match. According to an HBR study, 81% of customers across industries try to resolve issues themselves before reaching out to a customer service representative. This data — and the vastly improved customer experience offered by self-service portals - make them an integral component of modern businesses. Intelligent self-service: a high reward play for businesses The data clearly indicates that offering a self-serve option is a good move for businesses, provided it effectively assists customers in resolving the issues. Self-care channels that offer generalised responses are no longer sufficient in the current landscape. The paradigm has shifted towards personalised and proactive care, where conversations need to be contextually relevant and meaningful - especially when a customer is already in distress. Picture this scenario: a frustrated customer with an urgent issue tries to seek support through the Interactive Voice Response (IVR) system, only to be bombarded with a long menu of confusing options. Alternatively, they might be left dealing with unhelpful bots that fail to understand their specific needs. Such experiences can be exasperating and, over time, lead to customer dissatisfaction. This scenario is backed up by data from the HBR report. The report reveals that customers who attempt to self-serve, fail, and are forced to pick up the phone are 10% more likely to be disloyal than customers who are able to resolve their issue on the channel of their choice. All these suggest that impeccable self-care is crucial in enhancing the overall customer experience and elevating satisfaction levels. AI for Smart Self-care To deliver a superior Customer Experience, businesses must adopt technology that empowers customers to take control of their support journey. Here are some key AI capabilities that businesses should incorporate for driving self-care adoption and reducing customer effort: Personalisation - Leveraging AI to analyse customer data and behaviour to offer personalised self-care recommendations and solutions. Tailoring the self-help journey to individual preferences enhances engagement and ensures customers receive relevant assistance, increasing the likelihood of successful resolutions. Predictive Analytics - By leveraging historical data and customer behaviour patterns, AI can predict situations where customers might require support and provide proactive self-care solutions. Real-time Assistance - AI possesses the capability to deliver real-time, contextually relevant insights and recommendations to virtual assistants and chatbots. This enables chatbots to guide customers with step-by-step instructions on resolving the issue. Interactive Self-Care - AI can enable interactive self-help tools that assist customers in diagnosing and resolving problems independently. Whether through interactive tutorials or diagnostic assessments, these tools empower customers to take control of their support journey. Continuous Learning & Improvement - AI can continuously learn from customer interactions and feedback to refine responses and recommendations. By evolving over time, it ensures that self-care channels remain relevant and effective. Seamless Omni-channel Experience - AI-enabled insights for self-care can be delivered across websites, mobile apps, social media, and voice assistants. Providing a consistent and seamless experience allows customers to choose their preferred channel and access support effortlessly. Empower customers, enrich business outcomes Smart self-care produces tangible outcomes for businesses — most notably reducing resource requirements and costs by offering customers enhanced automated tools for problem resolution. Flytxt is at the forefront of helping businesses improve Customer Experience through its CX solutions that leverage well-trained, privacy-preserving AI. To understand how our AI-powered cloud solution can resolve common CX issues and enable smarter self-care, watch this video: https://www.youtube.com/watch?v=xurqRfbee-U

Blog Enterprise · Dec 27, 2022

AI for CX and CLTV: Predictions for 2023

Trust and transparency are vital for success The end of website cookies as we know them is coming – since Google announced they will phase out the use of cookies from browsers by 2024. 2023 is a critical time for B2C enterprises to finalize their business strategy for the ‘cookieless’ future. The loss of third-party data means an increased value in first-party data, which requires organizations to build trust within their community of users and data owners. The only way organizations can authentically gain that trust is through providing transparency and control to the use of data, which will be a vital goal for 2023. The post-pandemic shift towards sustainable business Subscription businesses will shift their focus from customer acquisition to sustained customer value creation during 2023 as we continue to head into a post-pandemic world. They realize that Customer and Product Lifetime Values give a more holistic and accurate sense to estimating ROI on CX investments rather than an increase in acquired base or an increase in transaction volume. It is on the premise that any customer experience which does not contribute to long term value creation will ultimately impact business profitability. An emergence of low-code CX The past few years have highlighted the need for enterprises to pivot to meet the ever-shifting landscape of customer needs efficiently. Next year, we’ll see an increase in user-friendly, low- code processes and systems to create a seamless customer experience across a myriad of touchpoints and systems. Vendors will embrace Industry-standard APIs to allow enterprises to integrate their CX ecosystem connecting internal and external systems painlessly. Energy costs will weigh on CX cloud infrastructure decisions Data centers play an essential role in the global IT supply chain. With the current situation in Ukraine, we are already dealing with higher energy costs, which, combined with higher inflation and supply disruption, will lead to an increase in the operating costs for data centers. This will inevitably make enterprises rethink their CX infrastructure strategies and cloud migrations. Embracing central intelligence to orchestrate CX Businesses increasingly recognize that their long term success is related to creating profitable customer relationships. To achieve this, companies need to be able to access data to understand customers deeper and predict future behavior. In 2023, businesses will increase their investments in a central intelligence system to power data-driven digital experiences. Early adopters will successfully optimize CLTV across workflows spanning CX functions like sales, marketing, product management and customer care. (Some of these predictions were covered in Solutions Review and SD Times)

Blog Telecom · Jul 25, 2022

Agility underlines CSPs’ digital aspirations

CSPs around the world are focusing on increasing customers' wallet share by enabling services that go beyond voice and data and fit their lifestyle needs in the fast advancing digital world. To achieve this, maintaining a higher level of Customer Satisfaction is critical. However as per Mckinsey report telecom operators’ average net promoter score (NPS), a key customer-satisfaction metric, has typically been in the 20s, as against the score of 50 for many other digital powerhouses. Digital Transformation: Opportunities and Threats For the past few years Communication service providers are trying to transform themselves into Digital service providers, expanding their business from communications to other adjacent OTT services such as messaging, financial services, entertainment services, IoT services, etc. As per a World Economic Forum report $2+ Trillion is the value that Telcos can unlock with becoming an enabler of Digital Economy. However, CSPs have an intense competition to battle out; not only with their rival telcos but with other OTT, Fintech and media/content service providers like Amazon, Paytm, Whatsapp, etc. As per a Juniper report 15+ % of service revenue gets eroded yearly due to migration of subscribers from operator to OTT services. CSPs to DSPs - the incremental approach CSPs need to embrace the fundamentals of doing digital business by focussing on improving customer experience. They need to bring in agility to their customer-centric operations and migrate many of their systems from legacy to modern cloud-ready, digital-first environment. And then invest in having a high speed network infrastructure and partner ecosystem to create compelling digital services and experiences for their customers. This is easier said than done. Not all CSPs are going in for big bang transformation, but some of them are smartly making this transformation in incremental steps so that they can stay closer to the digital natives till they transition to fully native Digital Service Providers. CSPs are launching disruptive digital attacker brands like GoMo by Eir, Win by Inwi, fizz by Videotron, SMARTY by Hutchison 3G, VOXI by Vodafone UK, Visible by Verizon USA, etc. built on new digital platforms. It gives them flexibility to launch new, differentiated and more personalized offerings quickly and match the CX expectations and standards set by the digital-first enterprises like Amazons and Stripes of the world. CSPs are creating sub-brands or separate entities in an adjacent industry to provide specialized services like mobile finance service, content services, etc to the customers, like jio money, MTN MoMo, wynk by Airtel, etc. Challenges and enablers for supporting CSPs Digital Play “As per Everest Group only 27% of Digital transformation projects are a success” Driving a digital transformation in telcos have its own set of challenges Excessive Complexity -A large number of complex products and plans that are hard to manage and optimize leading to delayed execution. Agility - Launching products & services and getting them to market in the shortest time frame and reaching out to the dynamic digital natives. Omni-channel journey orchestration - Delivering an always on experience across channels and staying relevant to customer's contextual needs and interests. Old school legacy systems - Traditional and outdated systems and technologies that still uses siloed processes and do not support new and advanced use cases. Service Integration - Telcos services such as broadband, fixed-line, broadcasting, etc are treated as separate entities. With creation of more services such as IoT, mobile banking, etc will increase complexity for the telecom to consolidate these services. Key enablers for a successful Digital Transformation Below figure points out the key elements that the telcos should incorporate in their systems to drive a successful digital transformation Conclusion With rapid changes in the internet technologies and the overall telecom landscape Digital transformation has become essential for a telco to fuel their growth and stay relevant. With the right combination of technology and services Telcos have the potential to become an advanced internet technology company. Flytxt through its AI driven solutions is focused on enabling a Telco to leverage AI, advanced analytics and marketing automation to maximize customer and product lifetime value. For more details download our e-book on Digital transformation for Telcos.

Blog Telecom · Apr 14, 2022

Improve business predictability with data-driven experiences in DX4C

This is a blog article published by Oracle on the upcoming data-driven digital experiences in Digital Experience for Communications solution enabled by integration of Flytxt analytics and AI A quality customer experience has never been more critical and today, customers can interact with their communications service provider (CSP) through a myriad of channels. According to recent research, more than half of customers now use mobile apps, chat, or websites to interact with their carriers. Through these multiple customer channels – website, applications, call centers, physical retail stores, and marketing programs – CSPs are collecting an enormous amount of data about their customers but are challenged to use it to impact business outcomes in an efficient manner. Today most CSPs handle acquisition and churn almost independently, but customers churn for a reason – primarily because of service and offering fit. When it comes to designing, launching, and managing offers and products, previously CSPs would simply make an educated guess on how to best optimize customer uptake, offer performance, and overall financial outcomes. But there is a better way. Removing any guesswork from the equation entirely, more and more CSPs are working to collect the abundance of data flowing through their disparate channels to create a “single pane of glass” view into their customer and their preferences. This customer- and product-centric data has the power to answer such questions such as “What do my customers need?” “What is the value for my customers?”, “What benefits or new products can I create for them?”, and “What price can I sell them for?” The immense value of a data-driven experience Having a comprehensive understanding of product-centric and customer-centric data impacts several components of a CSP’s business. If the data remains siloed, CSPs are missing an opportunity to translate insights into revenue. Designed in alignment with TM Forum’s Open Digital Architecture, Oracle’s Digital Experience for Communications (DX4C) enables CSPs to create that single view of data, and its newly launched capabilities drill down even further – providing data-driven experiences unique to specific business personas within the CSP. Marketing, product, commerce, and customer care managers can access actionable intelligence around subscription revenue growth, margin growth, subscriber growth, and churn reduction, as well as access automated assistance specific to improving decision-making within their area of expertise. Product-centric insights Through DX4C’s latest capabilities, product managers have clarity into the performance of each offer. With portfolio performance dashboards in place product managers can access predictive performance analytics that aid in offer design by identifying the likelihood of meeting pre-determined KPIs. Once an offer is deployed, data is collected on its performance, and a product manager can evaluate the offer based on factors such as revenue, margin, customer segment penetration, and churn reduction. If KPIs have not been met, product managers have an opportunity to take remedial actions such as pricing adjustments. With this data-driven intelligence, product managers can dig into metrics including sales by customer segment, by channel, and by campaign mode. Designing and launching new offers will be done in a new way – with a historical knowledge and a foundation that product managers can build upon to optimize performance. Source: Oracle Blog Churn has returned The percentage of customers willing to switch providers has steadily risen since early 2020 with first-time failure rates up to 2019 levels. Accustomed to the instant gratification of the world today, digitally savvy customers are looking for personalized, meaningful, and frictionless support through the channel of their choosing. It has never been more critical to meet and exceed customer expectations. Oracle’s DX4C solution is designed to support CSPs in achieving high levels of customer satisfaction through innovative and engaging customer experiences. To support retention, DX4C provides customer care agents with proactive data on churn propensity of any given customer. These predictions are identified using data from subscriber usage and consumption patterns as well as billing activities. Using the data-driven experiences provided by DX4C, agents can be empowered to make specific offers or recommendations based on the retention goals of the CSP. Agents will have the agility to design and serve personalized offers at scale across multiple customer channels and touchpoints. Source: Oracle Blog Turning insights into revenue Heightened by the prominence of remote work and education, communications service providers play a critical role in the day-to-day lives of their customers. Data-driven experiences can enable providers to truly understand what’s working and what’s not among their customer base. Insights could lead to business decisions being made with more clarity – an offer that isn’t attractive to customers could be revamped, or an element of the marketing strategy that isn’t being received as planned could be pulled. Decision-makers spanning the product, customer, marketing, and commerce sides of the business will be better equipped with actionable intelligence through DX4C’s data-driven experiences.

Blog Telecom · Dec 14, 2020

How can CVM complement CRM to advance the CX goals of Telcos?

The new generation of customers, especially the Millennials demand for personalized experiences and services. All B2C marketers are faced with the challenge of meeting this customer aspiration, where experiences need to be tailored to each individual customer’s persona and momentary needs. Telcos are no exception. With almost 100% mobile penetration, tight price regulations, growth of OTT platforms, and stagnant revenues, the need for Telcos to improve Customer Experience (CX) and loyalty is more than ever. Increased focus on CX A recent NYU study stated that customer churn typically costs around $65 million per month for large tier Telcos in US. On the other hand, according to Forrester’s CX Index scoring system, telcos that increase CX scores by one point generate an additional $3.39 incremental revenue per-customer. Hence Telcos are emphasizing on customer-centric marketing strategies and measures to improve CX across all touch points and front end applications of sales, marketing and customer service. Seeing this market need, Customer Relationship Management (CRM) vendors targeting large and mid-market Telcos like Oracle, SAP, Servicenow and Creatio are bringing together their customer-facing applications all into one umbrella of CX cloud. Some of them are clearly are having a vertical focus too; offering packaged solutions for Telecom, BFSI and other industries. CRM is not enough to ride CX wave CRM solutions are designed to manage specific activities of different stages of customer lifecycle – like generating leads, converting leads to customers and then handling of customer service requests and grievances.  Many CRM vendors claim to offer an integrated suite of applications to automate sales, marketing and service workflows; but again with the goal of acquiring customers and improving sales and service efficiency. Hence they are more transactional in nature and only focus on the current state of the customer.  They neither record customers’ past behavior nor predict their needs. CRM systems provide only a partial view of customer’s demographic, behavioural and other attributes; which is not enough to influence customer relationship and value continuously especially in industries where usage decides value.  More over in this digital world where new touch points are getting created frequently, it becomes necessary for any system to have new touchpoint integration capabilities beyond the traditional ones like email. Last but not the least, CRM systems also lack in creating decisions and actions on the fly depending on the context of a customer, which is absolutely essential for Telcos to achieve their vision of making every moment matter in the digital era.CVM is a critical cog to the CX Marketing wheel While considering which technology is the right option to drive a successful CX strategy Telcos needs to look for some key features that will help you select the right Customer Value Management (CVM) system for your telecom CX program. The system should help in creating a more unified view of customer by bringing in both offline and online data from a myriad of internal and external sources without any coding effort. The system should have the capability of performing usage metrics computation and aggregation for analysing customer’s usage behavior dynamics better. The platform should have advanced AI/ML capabilities to derive actionable intelligence from customer’s CRM data and more importantly usage/transaction data that can feed into not only marketing systems but also other front end applications like sales or customer service like churn propensity scores or next best offer recommendation or product affinity scores. The system should support contextual marketing i.e. it should be able to capture customer signals in real-time and then respond too with a highly relevant marketing action under a second. The system should be able to enable an Omni channel customer engagement journey seamlessly with out of the box support for both traditional and digital touchpoints. Bringing CRM and CVM together To launch a full-fledged successful CX program the Telcos need to partner with vendors who can offer a solution that can unify their customer interactions across front end touch points and applications so that they can provide a consistent personalized experience. Also having a single source of truth regarding customer data and intelligence derived from it is critical to make CX truly unique, differentiated, personalized and more importantly value-driven.

Blog BFSI · Aug 6, 2020

Why Banks are Splashing the Cash on Artificial Intelligence

Artificial intelligence was once the sole domain of sci-fi novels and movies, where supercomputers took over the world and conscious robots interacted on a daily basis with humans – just think of the 1927 classic ‘Metropolis’. Today, though, we live in an AI-enabled era, where artificial intelligence has the potential to impact every facet of our lives, from driverless vehicles to virtual assistants such as Alexa and Siri. But what about banking? How far has an industry once averse to technological change embraced the AI revolution? For many of us, we associate technological advances in financial services with security, such as facial recognition to log in to your banking app. But what if it could do much more than that. What if, for example, AI-driven software was deciding the right interest rate for you or advising you whether or not to buy a new car or make an investment? In fact, artificial intelligence is making a significant impact on financial services in just such areas, and many more, from product selection to investment advice and fraud or market tampering. Financial institutions, like other sectors such as telecoms and retail, are using AI to increase the value of customer interactions, building deeper relationships and, ultimately, increasing revenue and profitability. AI can even give customers contextual financial advice, empowering them to take up relevant services, such as insurance, credit card or a mortgage, based on their stated goals and life events, such as marriage or changing career. This is achieved through a combination of machine learning and predictive analytics combined in human-like interactions. Like most industries, the AI-conscious firms can be split into two categories – those already reaping the benefits of artificial intelligence and those still figuring out what to do with it. The former can see that AI enables the automation of previously manual tasks, completing them in a fraction of the time and on a scale that humans simply can’t match. The result, personnel are freed up to do what AI can’t, such as developing new products and services. Rather than a costly team of people wading through stacks of papers to decide which ‘band’ of customers should be targeted for a particular product or service, for how much, and at what rate, AI can do this for millions of people and taking into account all kinds of available data – creating the exact offer that matches the bank’s risk and reward and the customer’s expectation. In theory, no one would slip through the gap and no information would be ignored. This is a huge competitive advantage in an industry where margins are tight, and competition is fierce, since it allows them to lend in greater volume and at lower risk. And, it is better for the consumer as well since they are receiving the financial services they need, and, of course, do not have to wait for a human underwriter to make the decision. It seems like a win-win situation for banks – cut costs and churn, thereby increasing net revenue. But there are considerations to take in to account as well, especially when it comes to the legalities of financial institutions deploying AI. For example, companies need to ensure that AI processes are not violating data privacy laws, particularly with the playing field shifting thanks to the introduction of GDPR. Most large financial institutions operate across multiple regulatory jurisdictions, so it is vital that they understand how laws such as GDPR will affect their ability to gather, store, and use data. Equally, if institutions are using AI to automate decision making, such as approving loans or investment advice, then they need to make sure they do not breach fair lending laws or market rules. And, of course, there is the prickly issue of jobs. There has been much debate about the impact of AI taking over jobs currently performed by humans, as we are seeing to some extent in the banking industry. An often-quoted 2013 paper by Oxford University academics Carl Frey and Michael Osborne predicted nearly half (47 percent) of jobs in the US were at high risk of being automated. But more recently, this view is being challenged. For example, analysis by OECD, an inter-governmental group of high-income countries, found that only 14 percent of jobs in OECD countries – which includes the US, UK, Canada, and Japan – are “highly automatable”, i.e. their probability of automation is 70 percent or higher. Indeed, analysts Gartner expects artificial intelligence to become a positive job motivator by 2020. And, finally, there’s uncertainty. AI is, after all, still at a relatively early stage, so what happens if an algorithm is flawed or malfunctions. What might the potential impact be? Despite these concerns, one thing is sure – AI is going to be an integral part of the future of the financial services industry. In fact, in a survey of 424 senior executives in financial services by Baker McKenzie, 70% of respondents named either big data and advanced analytics or artificial intelligence/machine learning as the technologies most important to their organisation over the next three years. What’s clear is that the financial services industry is all set to cash in on AI – so, look out for that loan product you didn’t know you needed. Originally published in Global Banking & Finance Review

Blog Enterprise · Jul 8, 2020

How Top-Notch Consulting Firms Can Benefit from Alliance with MarTech Vendors?

Channel program enhances integration of Flytxt’s intelligent MarTech with solution providers, consultant firms and other local resellers. Navigating the fluid martech industry can be fraught with problems for busy marketers who simply have too many other roles to take care of and don’t have time to properly monitor IT developments and opportunities. For that, we have developed a stack of modular MarTech products which consultancy firms or other relevant system integrators can add to their solution portfolio to help the Telco clients implement their marketing technology strategy.  These products are designed to support any channel of interaction, any data source or use cases off the shelf. Flytxt is an ideal partner for technology or consultancy firms that are looking for a niche partner and an expert in the MarTech and have limited scope of solutions for their Telco clients. The company offers referral programs for exploring new opportunities and digital transformation projects as well as for joint participation on RFPs. Flytxt Referral Program also will give partners access to a number of client testimonials, product and sales training resources and continuous project support during all phases. As a part of revenue-shared model, Flytxt has identified the following types of companies to associate for our Partners’ Referral program: Consultancy Firms Diagnostic assessment: We help consultancy firms co-create their Telco clients’ MarTech strategy and roadmap that powers the business growth. This will include competency frameworks, maturity scoring, customer journey mapping and architecture design for clients. Moreover, we are assisting our consultancy partners to help their Telco clients in preparing RFPs. MarTech solutions: As a trusted MarTech expert, we implement marketing solutions from beginning to the end as part of digital transformation projects. We participate in cross-channel marketing optimization, technical setup and configuration, training and on-boarding, marketing operations and requirements scoping. Upsell to the existing markets and accounts: When the consultancy firms wish to scale up and boost their presence on existing Telco accounts, we step in to help them execute multi-channel marketing campaigns that can generate up to 7% revenue uplift. For this, we have customized use cases that can fulfil specific customer’s needs and prove ROI from day one. Regional Partners and SME-sized system integrators  Joint forces on complex RFPs: We use our keen business acumen and strategic mindset to help Telco clients gain a winning edge over their competitors by creating and implementing marketing technology solutions that deliver results. Innovation: Wherever the campaign design represents a serious human challenge in terms of scale and speed, we have the AI-driven technology to help them perform these much faster and bring it to the next level by spotting the opportunities automatically which an ordinary marketer cannot do on his own. Niche specialization: Our specialized services incorporate every aspect of MarTech CVM to help businesses with the entire process, from assessing needs to choosing right MarTech to meet those needs and delivering omni-channel marketing with clear result measurement. We can help with data-driven marketing automation end-to-end including measurement and optimisation. Solution for legacy firms: We can help you review customers' existing marketing technology and consider how effective it is compared to new technologies. Then we’ll help you replace outdated technology and turbocharge existing solutions that are already working but can be improved. A number of global partners have already joined Flytxt-program, including Atos, Qvantel, Wipro, TNO, Celltel and Infotel. A complete list of integration partners is available on demand. To apply to become a partner, click here.

Blog · Jun 11, 2020

Enabling partners to offer Digital BSS with integrated MarTech and AI to anchor Telco Digital Transformation

Flytxt, the market leader in Analytics and AI-driven Marketing Automation for Customer Value Management, launches a partnership program targeting BSS vendors and System Integrators; to enhance their Digital BSS offering with MarTech and AI capabilities. Digital transformation is not optional for Telcos today with shrinking revenue and rising competition from technology giants even for their core connectivity solutions (GoogleFi and Facebook’s Terragraph). On the other hand, they still have their traditional telcos to compete for sustaining and growing the share of market and customer wallet.  CSPs have to embrace digital transformation to protect themselves from the digital disruptors, and improving customer value management is at the heart of it. The recent covid pandemic and the subsequent restrictions have once again proved the role that Telcos play as trusted owners of customer relationships and as enablers of the digital ecosystem connecting consumers and enterprises.  Telcos need to offer a diverse portfolio of digital services, not just a limited portfolio of traditional services. As the traditional channels become obsolete, and customer preference and buying patterns bound to change in the post covid era, telcos need to engage with their customers with acquired insights, through digital channels.  Digital BSS Roll Out: Integrating BSS, MarTech and CRM Transformation partners like BSS vendors and SIs need to realize that Telcos are looking to pursue aggressively a digital-first paradigm in both customer-facing as well as network-facing operations.  However, digital transformation comes with its own challenges too. Customers will expect ‘digital telcos’ to offer them an experience matching that of OTT and other digital service providers. Hence Telcos are rewiring their IT architecture and technology stack to offer a truly personalized, omnichannel and seamless experience to customers over traditional and digital touchpoints. BCG also comments that  "For BSS vendors, lack of out-of-the-box support for digital means lost opportunities”.        BSS Vendors and SI’s are competing aggressively to offer a modern stack -’Digital BSS’ integrating customer-facing applications like CRM, BSS and MarTech into one box that can support Telco’s digital transformation requirements especially from a customer engagement perspective off the shelf.  And it is also important that BSS vendors and other SI transformation partners align their strategies and offerings to different digital transformation approaches of Telcos – complete or incremental transformation. Many have adopted the incremental transformation approach that focuses on adding new systems or replacing certain components which need an immediate overhaul to support digital transformation. MarTech and AI are essential to successful Digital BSS roll outs Telcos and their transformation partners realize that MarTech is one of the components that need an overhaul as legacy marketing automation and campaign management systems are not designed for achieving the scale and speed of marketing required in the digital age.today’s agile marketers are seriously challenged by the industry's big data problem. On one side, there is a tech-market full of innovative solutions that deliver massive data sets, but few marketers have the knowledge to make this data actionable. Telcos and transformation partners look for niche MarTech specialists who can come on board and quickly deploy analytics, AI and automation capabilities to fix their marketing speed and scale problem. They look out for solutions that can support modern marketer’s customer engagement requirements off the shelf like managing digital engagement, orchestrating marketing actions across omnichannel journeys, contextual and personalized decision-making, improving upon marketing KPIs like churn with deeper analytics, real-time actions etc.  To achieve sustainable competitive advantage, B2C marketers must move beyond traditional campaigns to orchestrate always-on, continuous engagement.  Similarly delivering contextually relevant experiences hinge on delivering value to customers in their individual moments of need. Modern MarTech solutions help marketers go beyond “right message, right time” campaigns to provide continuous value and utility with each customer interaction.  Align marketing with all customer-facing functions. customers want consistency and relevance across all touchpoints with a brand, so marketers must align martech investments with broader CX initiatives. MarTech tools should have the ability to integrate marketing automation tools with applications for sales, service, eCommerce, and other operational functions. Why is Flytxt the right partner? Flytxt through its Intelligent MarTech product suite is focused on enabling a CSP to leverage AI, advanced analytics and marketing automation and force multiply the speed and scale of marketing to make use of contextual upsell/cross-sell opportunities across digital journeys. Flytxt’s MarTech products and solutions are designed to seamlessly work with any standard and custom CRM and BSS systems used by CSPs for maximizing the value of each customer interaction.  More than 60 telcos across 50 countries are leveraging Flytxt’s Martech products in private cloud, public cloud or as SaaS to drive customer engagement. The Flytxt Partner Program was created to help BSS and other solution providers integrate Flytxt MarTech and AI modules to their existing solutions quickly and easily, which in turn will offer hundreds of functionality add-ons and digital channel integrations to help improve customer engagement and customer value management. Flytxt' partner framework allows a range of specs that can help scale and extend the value of the existing BSS-stack offers. That way, the BSS and other solution providers will be able to respond to the demanding RFPs and win complex projects. On the other hand, our joint customers will be able to tackle MarTech capabilities so they can more effectively optimize their campaigns for omnichannel success and positive ROI.  A number of global partners have already joined Flytxt’s partner program, including Atos, Qvantel, Wipro, TNO, Celltel, and Infotel. A complete list of integration partners is available on demand.  To become a Flytxt partner click here.  

Blog Telecom · May 18, 2020

Best Practices in Digital Sales for Telcos – Part 2

Adopt Omnichannel Engagement to Create Unified Experience Telcos across the world are shifting their customer engagement strategy from disconnected multi-channel marketing to unified omnichannel experience driven marketing actions. With this shift, telcos are able to provide a consistent and unified experience, whichever journey the customer chooses to take. Multi-channel Vs Omnichannel Multichannel customer engagement  refers to the ability to communicate and interact with customers on various platforms. A channel might be a traditional channel like call center, SMS, USSD or telco owned digital channels such as mobile application or web portal. It could be even a third party owned social media and OTT channels. Here the focus is on enabling a ‘channel’ or ‘touch point’ for engaging with customers. Since the customer engagement across these channels works in disconnected fashion, customer’s preferences or responses at specific channels are not getting accounted for the subsequent engagements through other channels. Omnichannel customer engagement  refers to the one stop unified marketing irrespective of the channel - be it the telco owned website or 3rd party owned social media channels. This approach will help marketers to provide a consistent and seamless experience to the customers, like extending same offer or service to customer on different touch points. Moreover marketer’s responses to customers on multiple channels are considered while deciding next action on other channels, thus helping marketers to orchestrate a continued experience across journey. Channel vs Journey-centric engagement The focus of multichannel approach is to reach maximum number of customers and engage with them offering maximum number of offers/services. In most of the scenarios, multichannel customer engagement is about reaching out to customers’ enmasse, thus creating a brand presence across the channels. On the other hand, the omnichannel approach is more holistic in nature & engages with customers to ensure they are having a consistent experience through their continued journey with the telco. The approach strives to maintain an engagement which is consistent thus providing a uniform customer experience across the channels.  As several studies reveal, telcos with omnichannel journey-centric customer engagement approach in place achieve up to 91% higher customer retention rate on average, compared to telcos with no such practices. Advanced Customer Value Management (CVM) platforms are designed with the omnichannel engagement approach. Customer’s interactions and recorded preferences across the channels are captured and analyzed. This insight is leveraged to provide personalized and contextual offers to the customers as the telcos engage with them, irrespective of the channel. Silo vs Centralized Multichannel engagement often means leveraging as many channels as possible to engage with customers, but those channels are managed separately in silos. However, with omnichannel approach, all channels are managed and orchestrated centrally.  Marketers can determine engagement strategies and actions centrally to ensure consistent experience. CVM platforms help to reduce manual offer management at individual channel level. Being integrated with the offer management, CVM platforms  can sync all the offers across channels effortlessly in creating consistency across the channels. To achieve this, it is of utmost important to connect the CVM platform to all the channels which has customer offering so that the consistency is maintained. Omnichannel needs a coherent approach Executing an omnichannel engagement strategy needs a coherent approach. It has to start from the top with management’s focus on delivering customer experience rather than just maximizing reach. Key elements of a coherent approach required are: Omnichannel management team: Experience shows that all impacted department’s needs to be represented in the team and backing by top management is critical. An omnichannel transformation should be jointly driven by marketing, customer service and IT teams. Customer journey mapping:Identifying the interaction points of different customers allows telcos to construct different possible customer journeys so as to define desired experience and supporting actions at different touch points. Prioritizing capability gaps: Priority should be given to fix gaps or issues in processes or systems to achieve omnichannel engagement. For example, unified customer data and customer behavior analytics are prerequisites for orchestrating an omnichannel customer journey. So it is important for telcos to have a centralized mechanism to aggregate, manage and analyze customer data for deriving insights. An experienced technology and business partner: Most companies need external support in both devising omnichannel strategy as well as in executing it. Partnering with a solution provider having a combination of technical and domain knowledge will help in achieving omnichannel engagement goals quickly and with minimal effort. As we discussed, omnichannel approach is very effective in enhancing the customer value, as it deals with engaging customers on any touch point with relevance; resulting in higher upsell/ cross sell and customer retention. But executing it would need telcos to put in place different blocks both operation-wise and technology-wise.

Blog Telecom · Apr 30, 2020

Impact of COVID 19 Pandemic on Telecom Network Usage

The outbreak of COVID 19 and the subsequent lockdown across the globe has forced millions of people confined to their houses. Consumers and firms are depending on the network provided by CSPs more than ever for social connectivity and business continuity. This is quite visible from trends like unprecedented rise in eCommerce activities for medicines and other essentials or increased sign up for entertainment and gaming apps or higher usage of apps for voice calls and video calls to distant countries. Let us find out in detail, what are the patterns observed across the globe in terms of telecom services usage. Be it basic telecommunication or high-end collaborative platforms, or entertainment platforms - telecom operators across the globe have enabled mankind to manage this unprecedented citation to a great extent. COVID 19 impact on key KPIs across selected CSPs In general, almost all the CSPs across the globe have seen high usage for the data services, thanks to the OTT platforms. Voice usage also has increased for some of the CSPs. But our analysis proves that the surge in data usage hasn’t directly translated to higher revenue. While CSPs with a higher increase in voice services have secured a decent increase in their revenue. Region-wise network usage trends The impact of Covid 19 on network usage in different regions are summarized below: Africa Telcos across Africa have seen data usage surge by 20-40% with partial or complete lockdowns coming into effect. While voice usage has seen a marginal decline, ranging from 1-8%, the increase in data usage has not brought about a proportionate increase in revenues due to Lower contribution of data revenue Increased discounting announced by telcos as a relief measure to consumers The few telcos who took proactive measures before the lockdown implementation have been able to hold; and even increase revenues, at least for the first 1-2 weeks. Multiplay telcos were the only ones to see a double-digit revenue drop, due to the waiving of mobile money transaction fees on lower-value transactions. Overall, the impact of lockdown in the African region seems muted, especially if seen in comparison to some countries in the APAC region. The primary reason for this being the widespread penetration of mobile money in the African markets, which reduces consumers’ dependency on brick and mortar retail drastically. South-East Asia In South-East Asia, the pandemic has so far impacted operators with a decrease in data usage and voice usage resulted in revenue loss by 24% to 32%. SE Asia regions were one of the first to experience the pandemic impact; governments have responded with nation-wide lockdown and have insisted work from home for both private firms and government institutions. This has created a shift in data and voice usage, subscriber mobile network usage has shifted to wifi, even the voice calls have shifted to OTT platforms. This shift is reflected in about 50% drop in data usage and 30% drop in voice usage in some of the markets in this region. South Asia Governments across the region have encouraged citizens to adopt digital mediums for daily transactions. This has resulted in increased data usage by 18% to 32% across countries. For some operators, the majority of the data consumption was due to content OTT platforms. Although revenue has dropped by 15% to 28% due to the increasing rate of pack utilization, which has reduced revenue per MB and a steep decline in voice usage by 10%. CSPs in the region are heavily dependent on retailer networks for most of their monthly top-ups. To offer flexibility to customers, many CSPs have seeded data benefits, voice benefits & extra validity to avoid forced churn. Comparing revenue impact & approach for CSPs  in the region is the providers who have not seeded the voice have experienced a steep decline in revenue by than the operators who did. Latin America Like other regions, the lockdown has impacted CSPs across the Latin American region, where data usage has increased by 7% and revenue has declined by 14% for the region. In the region, increasing demand for data services has challenged many operators to maintain the digital experience and connectivity speed. What lies ahead This is time for CSPs to engage with their customers through digital channels proactively and offer the services that meet their immediate and future requirements. In order to reduce revenue vulnerability, and ensure continued customer engagement and loyalty, CSPs must start focussing on; Customer engagement over multiple channels: Engaging with customers through multiple touch-points, both digital (mobile apps, web, social media, chatbots, and interactive speakers) and non-digital (Interactive SMS, USSD and IVR) Increase wallet share: CSPs operating with multi-play services or having partnerships in other lines of business should utilize the power of analytics to identify potential customers for other adjective services such as highspeed connectivity, mobile money, or video streaming platforms. AI-powered engagement: If not late, this may be the right time to use AI-driven analytics to engage with the customers. Instead of bombarding the customers with irrelevant and broad product offerings, AI-driven analytics can help to recommend the next best offers to the customers based on their past and immediate preferences and actions. As humankind is fighting a pandemic that has made physical distancing mandatory, CSPs have helped to keep us together atleast digitally. But as the demand for data connectivity increases across the subscriber base, CSPs are also in need of high investment in terms of infrastructure. However the shift in consumer’s preference to avail commercial, educational and entertainment services via digital channels is expected to benefit CSPs in the medium to long run.

Blog Enterprise · Apr 7, 2020

Marketers! Brace for the pandemic impact

Corona outbreak is one of the biggest and dreadful black swan events that has ever happened in the history of mankind. It is spreading at a rapid pace too, impacting almost all the countries and industries globally. The extent of economic impact and recovery however could vary for each industry. Hospitality business is almost shutdown completely, whereas Telecom service providers are hit marginally.  There has been some disruption to the Telco’s supply chain and operations with the shutdown of retail stores and call centers. But analysts believe that Telecom industry will stand to gain in short term from the increasing use of data and in the long run from an apparent shift in consumption behavior. Market turbulence from shifting consumer behavior Marketers need to prepare themselves for a new world order, where consumers are being impacted by the growing concerns of pandemic outbreak and economic recession. With the enforcement of outbreak containment measures like lockdown and social distancing, consumers irrespective of age group or financial status are going to increasingly choose, buy and avail products and services digitally. It is no longer just another choice of channel to engage or a channel preferred only by the millennial segment, but has emerged as a lifeline for Gen-C to stay connected and survive in the world as we are seeing during this pandemic crisis. Consumers will get choosy and buying decisions are driven more by ‘the need of the hour’ and ‘value for money’ than by comfort and luxury. Analysts predict that sales of big ticket items like cars, home, travel plans is expected to come down by more than 50%. Crowd fear is pushing consumers to favor delivery and consumption of services – shopping or dining or entertainment more at home. This will tempt even the local stores to establish home delivery and digital infrastructure to support taking up orders online and for receiving payments online. And then consumer preferences are also expected to change on a short notice as this is quite an unprecedented time, even one fake news could drastically change the consumer demand. Brace for the impact Marketers need to think about ways and means to keep the customer engagement on during this difficult time. They cannot keep communicating usual marketing messages as if nothing has changed around; that can potentially hurt customers and make them disengaged. At the same time marketers cannot remain silent and wait for Pandemic crisis to get over to engage with customers, which can potentially lead to customers leaving them. It needs a balanced approach. Here are a few things marketers can do immediately: Stay connected with consumers: Reach out to consumers proactively. Regular updates can show you care and are there for them. Also hear customers out – keep channels of engagement such as emails or digital channels open, to understand what your customers need, their changing behavior and wants, and how you can help them during this challenging time. Be sensitive to the emotional context: Carefully craft marketing communication not only for the business context but also for the emotional context. Also when you are trying to approach consumers for marketing, please take their emotions and sentiments also into consideration. Treatment also need to vary accordingly as per the emotional state of mind. Consumers will feel more connected when we stay relevant and orchestrate experiences respecting their emotions. Predict and act fast: As consumers tend to change their behavior and needs often even on a day, marketers need to keep their prediction engine always on to forecast these changes and act accordingly. You need to adjust your offers and services quickly as per the predicted behavior change.   This needs adoption of technologies like AI/ML. Prepare to emerge stronger Engagement with consumers’ today demand contextually sensitive communication and actions based on the need of the hour. However marketers also need to prepare for post-pandemic era, understand the market realities evolving like shifts in consumer buying behavior and growing affinity for digital channels and get ready to adapt to the dynamics quickly and smoothly.

Blog Telecom · Feb 28, 2020

Best Practices in Digital Sales for Telcos-Part 1

Product Portfolio Sizing Telecom marketers today have more sophisticated and user friendly Marketing Automation tools at their disposal. It has become easier for them now to plan, design and execute multi-channel marketing campaigns – outbound and inbound. And with the increased application of AI/ML techniques, there is a lot of hype around how marketers can now take personalization to the next level. Marketing systems can now automatically choose best product/offer from available options for each customer fitting their historical behavior and contextual needs. However, increasing the level of personalization will bring in additional complexity in micro-segmentation, product/offer management as well as impact measurement and reporting. More the products, higher will be the permutations and combinations an AI/ML model has to consider while choosing the right one for each customer. This may eventually lead to higher computation overheads and thereby, higher operational costs. Hence, it is imperative for the marketers to first arrive at an optimum product portfolio size. Macro & Micro Factors Influencing Product Portfolio Marketers need to consider multiple factors while deciding on the product portfolio, some of which are elaborated below: Industry: Maturity level of the industry including the number of players, their relative market power, regulatory guidelines, etc. will guide the level of customization and size of portfolio. Market position: Higher level of customization should ideally be adopted by incumbents or market leaders, since the size will allow for economies of scale and better ROIs. A new entrant or a challenger should prefer a broad base approach for ease of communication as well as to curtail costs and support higher marketing spends. Elasticity of consumers: Elasticity of the customer’s incremental spend is a very important factor. Running highly personalized campaigns on a relatively inelastic customer base will not yield any incremental revenues for the marketer. Ease of communication: Personalized campaigns need to very meticulous in communicating with the consumer, failing which, the entire program may fail to get traction Data/Smartphone penetration: Smartphone users may be easy to communicate with, considering the ease of reading as well as language/script compatibility on these devices. However, with 4G being the dominant operator across the globe, and low-cost device ecosystem growing, this may cease to be a factor. Distribution network: For markets with low literacy levels (or complicated product constructs), a good distribution network is critical for the success of personalized campaigns, since channel partners are a critical link between the marketer and the consumer. Approaches to Product Portfolio Sizing Based on the above factors discussed, marketers can follow any of the below approaches to come up with an optimum product portfolio size: Conservative approach (low or no level of personalization): This approach is usually best for new entrants and challengers, wherein a clear value proposition makes it easy to recall for value seeking customers. A popular example of this approach is discounting for new acquisitions only. However, if not calibrated well, it may backfire for products/services with low entry barriers; leading to rotational churn, thereby increasing acquisition costs per customer. Balanced approach: As the name suggests, this approach utilizes the incremental gains from segmented products, while trying to maintain the open market product penetrations. It would be fruitful if segmented benefits are offered to a very limited customer base with specific needs. An example is offering sachet packs only to customers who do not purchase longer subscriptions. Simple scale up: Recommended for scenarios where the profit margins are slim and personalization is primarily based on customer’s usage/spend level. Buy more save more campaigns are a typical example of this approach. Other methods of using simple scale up can be progressive rewards or loyalty based rewards. High personalization: High personalization is recommended for cases where the brand is well established and commands good engagement with the customers. The primary objective of this approach is to increase profit margins by driving incremental spends at a more granular level. While simple scale-up will look at the customer’s behavior, marketers can also add context to the offering, depending on stages of the customer’s journey. Since this approach can be very granular and complex, it is imperative to have omni-channel marketing automation capabilities to do this without compromising on customer experience. Segment of One: This approach is recommended only for cases where the products are promoted on a one-to-one basis, and the marketing campaigns focus on other aspects of the brand. Personalized VAS services can be one such example, wherein the benefits can be decided basis customer’s profile. In order for marketers to move closer to Segment of One, it becomes necessary to augment marketing automation with machine learning capabilities which can make it possible to utilize large data volume beyond human cognition. Conclusion: Choosing the right product portfolio size is important to extract maximum revenue from a CVM program. While every market is unique, above approaches can be used to make the decision more structured and exhaustive for better results.

Blog Telecom · Oct 24, 2019

Aiding the Digital Transformation of Telco

A study across 140 OpCos covering 2.5 Billion subscribers and $625 Billion of revenues, reported by Roland Berger shows that 9-10% of Telco revenues come from adjacencies and new services. By 2025 this is estimated to be 22% globally, ranging from 11% in Europe to 33% in North America. The major ones among these adjacencies and new services are IoT and Mobile Financial Services, which are considered ‘Very Near’ to Telcos’ core services. Among other adjacencies, Security and Analytics are sufficiently ‘Near’ for Telco and e-Commerce, Digital Advertising and Media & Entertainment are further away from core services and Retail Banking the furthest.  A Telco can imbibe the adjacencies and new services to varying degrees depending on the scope exercised by the Telco with respect to each service. A Telco can exercise the following six distinct, but overlapping, scope items with respect to any service. And for exercising any scope, especially for adjacent and new services, Telco needs to make investments in their network infrastructure, information technology, and business processes. Access: This is the least of the scope exercised by the Telco and an example is of OTT services, where the Telco merely extends the access as a rather dumb pipe. The investments needed for this were faster and cheaper data networks (3G, 4G and 5G). Discovery: The second degree of scope wherein the Telco facilitates the discovery of the service by the consumer. On-deck services through co-branding and joint marketing are examples of this. Commerce: The third degree of scope where the Telco assumes the retailing of the service. Telco has been doing this for a long time for devices and traditional value-added services in addition to core services. Fulfillment: The fourth degree of scope where the Telco also fulfills the service. Traditionally this has been done for devices by many Telcos and more recently for Media & Entertainment Services through not only faster backbones but CDNs, Edge hosting, Streaming technology, Rights management, etc. MTN Shortz is an example where MTN exercises all four degrees of scope. Ownership Rights: This is the highest degree of scope wherein the Telco owns the service through M&A (Time Warner by AT&T), acquiring new licenses (NBFC license by airtel), acquiring content rights (Reliance Jio). Depending on the strategy, market maturity, investment appetite, regulatory regime, etc. different Telco exercises different degrees of scope with respect to different services.  A CSP that provides core Telco services is at the bottom left of the picture and the top right is a full-fledged DSP (digital service provider). Digital transformation takes the Telco on a journey from CSP towards DSP through a path that is appropriate for that Telco. Which ever be the path taken by a Telco for each of the adjacent and new services, it invariably goes through Discovery and Commerce as the first two steps.  The business focus of the Telco while exercising Discovery and Commerce scope with any service is to up sell and cross sell the adjacent or new service to its customers. This trend of generating revenues from adjacent and new services – indeed is the primary reason for a Telco to set on a journey from a CSP to a DSP. And the degree of success achieved in penetrating these services to their subscriber base underlines the business case for the Telco to exercise additional scope of Fulfilment and even Rights Ownership for that service.  Flytxt through its Intelligent Customer Engagement product suite is focused on enabling a Telco to leverage AI, advanced analytics and marketing automation to profitably and quickly up sell and cross sell core services and these new and adjacent services to the Telco’s subscriber base. Flytxt’s products and solutions are designed to seamlessly work with any standard and custom solutions used by Telco for managing the adjacent or new services.

Blog Enterprise · Oct 4, 2019

Managing Customer Relationships in 2025

Building longer, deeper, and profitable relationships with your customers is central to the subscription economy that is set to encompass major part of our lives. Customer relationship management that was once a business intelligence or product-driven activity, rightfully changed to a customer-centric activity, about a decade ago. Customer centricity helps enterprises to develop a deep understanding of their customers, predict their wants and needs and act in a manner to meet those wants and needs. By this, enterprises gain a strategic advantage through superior customer experience, increased stickiness, higher sales, and higher profits. Developments in data-driven marketing technology over the past decade have increased the efficiency and effectiveness of enterprises. Flytxt provides one of the leading solutions in this area that is easy-to-use and integrated with out-of-the-box unique marketing AI for automated campaign design and optimization. Over the next 5 years, rapid developments in technology, some of which are already available in the laboratories, will allow enterprises and consumers to enjoy a vastly transformed and true customer-centricity. Leadership in Customer Relationship Management in 2025 will be determined by the following 6 major characteristics: Autonomous marketing that brings speed and accuracy beyond human capability, eliminates manual labour and human bias while encouraging creativity. Federated customer value management where enterprises collaborate and pool their resources to drive higher customer value across their customer base. Explainable marketing AI when the actions taken by AI to automate as well as to increase marketing effectiveness will be explained by the AI itself. Ubiquitous contextuality where the context across devices, access mechanisms, service lines, and enterprises will determine the marketing context. Strict data privacy ownership where each consumer will own their data and control how, when, where and who will use their data. Hybrid cloud architecture that provides marketing flexibility and enterprise-level collaboration while preserving data privacy and security. Autonomous Marketing: The entire marketing process from data analysis through segmentation, decisioning, execution, measurement and optimization will be done by the software without practically any manual processes. Human creativity will be focused on setting strategies, goals and boundaries for the machine to operate within. With autonomous marketing enterprises will never miss an opportunity to engage with their customers, that too in an optimal manner. The speed and precision needed to identify and act upon each customer wants and needs in a timely manner is simply beyond human capability. Enterprises that adopt autonomous marketing systems will hugely benefit not only from the split-second precise decisions but also by eliminating all human bias while being at the same time imbibing human creativity. Federated Customer Value Management:All customers are consumers of many enterprises. Often, we purchase the same or similar goods and services from multiple enterprises. Each consumer’s behavior, needs and wants are decided by a combination of their interactions with all the enterprises and not necessarily by the interactions with one enterprise. The customer spend on one enterprise is significantly influenced by their spend on others. Ideally, every enterprise should consider their customers’ interactions with all other enterprises as well as their own to take the best decisions to increase customer value. Enterprises who adopt technologies that help them to share sensitive, private data and collaboratively orchestrate customer value management programs will take a leap in customer value management over others. Explainable Marketing AI: Every enterprise has a need to understand the reasoning behind its business decisions and the impact thereof to craft and execute successful strategies. And this holds true for marketing as well as any other department. While marketing AI will continue to create significantly better outcomes, its adoption, and effective usage will be held back if the basis for its decisions and actions are not explainable or understood by the enterprise. Explainable marketing AI will not only make the best marketing decisions but also throw light into what is the basis and reasoning behind those decisions. Enterprises must adopt explainable marketing AI so that they can understand the reasoning behind the machine’s decisions and work with the machine to adapt the reasoning – much like how an enterprise works with a human expert synergizing their thought processes. Ubiquitous Contextuality: More and more businesses will operate across the physical and digital world and so will consumers move seamlessly across the physical and digital worlds to experience their service provider. And an ever-increasing number of connected devices and access networks connect the consumer to their service provider enterprises, not one at a time but simultaneously across virtually all of them. The context of a consumer will not be determined based on a single device or channel but will be distributed across many of them. Think of all the devices in the smart home, connected car, office, Wi-Fi hotspots, and beacons on the roads. In order to develop a deep understanding of the consumer, the context has to be stitched across a very broad set of devices and environments. With a broader context, enterprise can analyze and understand more about customers, thus creating a deeper relationship with them. Strict Data Privacy & Ownership: Access and rights to process customer data is central to building and managing valuable customer relationships. Customer data ownership and privacy are paramount in this. Using customer data that has been obtained through less transparent means or fuzzy agreements is already under question, with the biggest names in the industry tripping over it. Just anonymization of customer identity or data localization alone will not be enough to assure the customers that their data is safe and private. Each customer must have irrefutable ownership of all data about them and must be able to decide who can use their data, when, how, for what purpose,etc. And all private data, except in the hands of the individual, will be stored and processed with strict encryption. Hybrid Cloud Architecture: Data is a key asset; it’s privacy and security are of utmost importance. At the same time, customer engagements have to be orchestrated collaboratively within enterprises and between enterprises in order to build long-lasting, profitable customer relationships. Hybrid cloud architectures that accommodate data in the private cloud of the enterprise, or ultimately even with the individual customer, and orchestrate secured sharing/use of data in a common public cloud will be the leading architecture for any manner of customer relationship management. Enterprise who adopts a hybrid cloud architecture will be able to adequately balance the needs of data privacy and security and at the same time enjoy the benefits of collaboration and federation, placing them well ahead of others.

Blog Enterprise · Sep 26, 2019

Refresh MarTech for digital customer engagement

Around a decade ago, customer engagement was limited to a handful of ways. Today, with the growth of social media and numerous other digital channels, enterprises have to look at customer engagement in a new way. Engagement is always on. Enterprises used to converse with customers mainly via sales representatives or customer service personnel. In those days engagement was discrete and mostly initiated by customers. In the initial years of the mobile revolution, SMS was the major medium marketers used to communicate with their customers. Emails and voice calls were used in certain situations however email was prominent in a B2B scenario. Nevertheless, the communication was often broken at many points and hardly helped the enterprises to develop a unified customer engagement strategy, leading to delayed, irrelevant communication and inconsistent customer experiences across channels. The scenario started changing with the growing adoption of digital channels, such as social media channels, chatbots, mobile apps, and other digital channels. This opened up a huge opportunity for enterprises to stay with consumers more, look into their buying behavior and sentiments, analyze them and respond in a timely manner with relevance. Chatbots and voice devices like Google Nest are the latest ones added to the growing list of new digital channels. AI-enabled chatbots are currently used more as self-help tools for improving customer service. Enterprises wish to use chatbots for improving customer experience and generating more sales as well. Advantages of customer engagement through multiple digital channels Modern customers expect consistent, seamless experiences when interacting with enterprises, regardless of the channels they use. They want a unified experience that makes it quick and easy to get consistent information. As enterprises use multi-digital channels to derive a customer-centric view and to engage with customers, it helps in meeting their customers’ needs. Customer-centric 360-degree view: Customer data along with the digital interactions across multiple digital channels helps the enterprises to build a holistic comprehensive view of the customers - their usage/purchase behavior, preferences and predicted needs. This, in turn, helps to profile and segment the customers for effective engagements even at an individual level. Advancements in AI and machine learning helps enterprises to analyze data at scale and derive such useful insights to take more informed decisions and actions. Omnichannel digital experience: With multichannel digital engagement, enterprises are able to deliver consistent, personalized customer experiences throughout each journey and across all channels and touchpoints. This is possible, as the customer interactions at multiple channels are getting consolidated to derive a customer-centric view. Update your MarTech for digital customer engagement The key factor for success is to provide excellent and seamless customer engagement across each and every channel. Highly personalized and relevant customer engagements are possible with a deeper understanding of customer’s behavior and contextual needs as well as real-time multi-channel engagement capability. Enterprises rely mostly on Martech solution providers to come up with sophisticated AI, analytics and automation capabilities to support customer engagement on these new digital channels. For example, vendors should help enterprises leverage AI and machine learning to identify the next best offer or preferred engagement channel for each customer. Enterprises need vendors who can unify customer engagement and allow them to strategize and execute different marketing actions across channels from one interface. A few MarTech solution providers have upgraded their solutions by integrating ‘digital channel management’ capability. Flytxt’s flagship intelligent MarTech product NEON-dX has an add-on module Digital Plus designed to help marketers engage with customers over digital touchpoints like social media channels, web applications, mobile apps, etc. Moreover, customer data from these channels can be integrated and analyzed for deeper customer profiling and engagement.

Blog Enterprise · Aug 10, 2019

Marketing in the Usage Economy

The usage economy brings new challenges demanding new solutions for data-driven marketing. Customer data analytics – descriptive, predictive and prescriptive analysis of customer behaviour – helps enterprises to develop a deep understanding of their customers, predict their wants and needs and act in a manner to meet customers’ expectations. By this, enterprises strive to generate longer, deeper and more valuable customer relationships.  The type of business determines customer behaviour. As compared to asset sales business, enterprises offering usage models have richer customer data needing more sophisticated treatment. In a traditional trading business, assets are purchased from enterprises and once purchased the ownership and almost all associated risks are transferred to the consumer. After this discrete ‘purchase & transfer-ownership’ transaction, the enterprise goes on to the next transaction. Business, essentially, is then a collection of such discrete transactions and the goal of marketing is to increase the number of such purchase transactions. That is, the customer behaviour to be influenced is (discrete) purchase transactions and the most important historical data for this is the past (discrete) purchase transactions. In a usage economy  the consumer only “uses” the services and does not purchase the asset. The ownership of the asset and associated risks in delivering the services rest with the enterprise. The consumer pays for using the service, typically based on quantity, quality, time of use etc. Business is then determined by how much, when and at what quality the consumer uses the services – that is, the usage pattern. The goal of marketing is to increase what the customer pays by changing the usage pattern to one with higher value. The historical usage patterns reflecting the type of service, quantity, quality, context of usage, the sequence of usage, etc. as well as consumer characteristics decide the marketing actions to encourage higher value usage patterns. A telecom CMO characterises the difference as “If I sell a telecom subscription to a consumer, I have to serve him for life, unlike if I sell a shirt to him”. Telecom is, indeed, a prime example of subscription business; other examples are Banking, Utilities, Transportation, Ride Sharing, Mobile Payments etc. All retail industry, including e-commerce, is the traditional sales model. There is no real usage pattern when a consumer purchases a laptop, a clothesline and a collapsible ladder from a (r)etaier, but only discrete transactions. It is, however, true that today most businesses exhibit mixed characteristics with loyalty programs by retail, one off sales by Telco etc.  Also, there is a growing trend of traditional sales moving to subscription (e.g., DollarShaveClub now part of Unilever) or enterprises introducing some level of subscription elements (e.g., Amazon Prime, Loyalty programs etc.) in their business. The goal of marketing in a usage-based business is to encourage a higher value usage patterns whereas in the asset sale-purchase business it is to encourage more discrete purchase transactions. Future behaviour is determined by analysing past behaviour and the former must analyse past usage patterns and the latter past discrete purchase transactions. Enterprises already in as well as those planning to enter the usage economy must recognize this difference and plan their marketing strategy accordingly.

Blog Enterprise · Aug 8, 2019

Creating digital ecosystems

Henri Setiawan Wyanto,Head-SMB Digitalization-PT Telkom Indonesia was the guest speaker at Flytxt’s annual event this year. He talked about the digital transformation journey of PT Telkom and how they are preparing to fulfill the needs and aspirations of fast growing digital population of Indonesia. Beyond connectivity Rather than just a provider of connectivity, Telcos are trying to digitize and digitalize to evolve as a full scale digital service provider (DSP). However the evolution required to get to that next stage is significant and the value chains created are much more complex. As the subscriber base have multiplied many times, Telcos have invested heavily on the network and IT infrastructure. One of the biggest bottlenecks in a Telco’s digital transformation journey is its existing systems and architecture frameworks. Inorder to be a successful DSP, a communication service provider (CSP) should be digitally enabled, highly connected and should be capable enough to have horizontal applications to leverage the existing platforms. Transforming to a DSP  Digital transformation is both culture driven and technology driven. DSP can offer more beyond mere connectivity and provide ‘value added services’ to its consumers by increasing the product and subscription portfolio. In order to develop new services and deliver better digital experience Telcos should be flexible enough to create a ‘new Digital Ecosystem’, where they collaborate with other partners of the ecosystem. Along with collaborative partner network, they also need to focus on network experience optimization, deeper data analytics to understand customers better and personalized customer engagement with contextual offers. Major elements in transforming to DSP’s  Among the umpteen number of elements to make the transformation a success, the following four factors requires special mention. People: Build digital capabilities,innovations and leadership providing digital creative facilities and collaborations with academic research institute,business and community. In Telkom indonesia they provide opportunities to people to bring out new ideas at internal idea generation hub named AMOEBA and external idea generation platform known as Indigo.These mechanisms provide an opportunity for collaborative creative thinking. Process: Process digitization and governance enhancing the ability to process and analyze extremely large data sets to uncover patterns,sequences and relations for achieving operational efficiency and improving customer engagement. Platform: Leverage AI and machine learning to design,control and manage digital interactions as well as support a collaborative delivery framework with partners to boost digital services. Performance: Design thinking for excellent customer and employee experience. In Order to have a deeply personalized customer engagement, it is required to have an in-depth understanding of customers and their needs and preferences. Personalization of customer interactions requires complex analysis of large volumes of data. Mass personalization increases the number of potential actions that need to be considered for each customer journey. Analytics is the engine of growth of DSP: Inorder to be a successful DSP, CSP’s should know how to collect data intrinsically,aggregate it and know how to refine it into insights that can be achieved upon in real time. CSP’s transformed into DSP’s must be able to leverage big data analytics to continuously expand its knowledge and responsiveness to the customers Thinking beyond horizon As the digital aspirations and needs of customers will evolve from time to time, CSPs need to continuously innovate hinging on 4P’s - People, Processes, Platforms and Performance. PT Telkom believes that CSPs should make their journey more digital and innovative by exploring different possibilities, while creating a new ecosystem collaborating with partners like Flytxt who can bring in technologies to support their vision forward.

Blog Enterprise · May 28, 2019

Personalization gets ‘hyper’ with Machine Learning

In the not-so-distant past, sending an email by just introducing the recipient with the first name (“Dear John”) was considered as a “personalized email”. We have come a long way from there. The technology advancements have empowered brands to leverage customer data, derive valuable insights and deliver highly personalized communication at the right time via right channel. Today, consumers are swamped with endless marketing messages and offers, making it more and more difficult for brands to create experiences that will make them stand out of the competition. Hyper personalization is the key to this problem. According to Forrester, 77% of consumers have chosen, recommended, or paid more for a brand that provides a super personalized service or experience. Hyper-personalization takes personalized marketing one step further by leveraging machine learning. Decisions and recommendations are made in real-time and for each individual. A research with Forbes Insights revealed that most brands are already investing heavily in data initiatives. Machine learning helps brands in making the most of the data by diving deeper into consumer behavior, creating dynamic content, and enabling personalization at scale. Personalization in the age of Machine Learning Even though personalization is becoming more of a norm, consumers are often left frustrated by the not-so-personalized marketing communications from many brands. Like, for example, when a bank encourages its customer to apply for a credit card they already have. Or a retailer sends emails about a flash sale in a category which is not relevant for the consumer (e.g. lawn care for people living in a high-rise apartment). This makes consumers take their business to places where they feel recognized, appreciated and valued as an individual. So brands are now leveraging AI and machine learning to create better hyper-personalized experiences for consumers. Machine learning has changed the game of personalization for marketers. By leveraging machine learning (ML) technology, marketers can process vast amounts of data in real-time, to make the best decision about what to offer to each individual. Marketers can now make a shift from conventional rule-based personalization to using machine-learned algorithms and predictive analytics to deliver more relevant experience to each and every customer. So, how does ML-based personalization work? Building customer personas The first step for delivering hyper-personalized experience is to collect and analyze right set of data. By using ML-powered predictive analytics technology, brands can leverage consumer data from various sources to build buyer personas. ML system makes use of historical customer data for all the customers along with aggregate usage behavior of the customer for each specific product to derive an expected usage pattern corresponding to each product. Using 360-degree customer profiling and machine-learned ‘Next Best Offer’ capabilities of customer engagement products, brands can determine the right offer to be extended over different touch points fitting each customer’s demographic profile, lifetime events and his predicted needs and intent. Learning customer preferences Machine learning algorithms play a crucial role in determining customer preferences. Enterprises can learn user preferences in two ways. Firstly by observing their usage patterns and secondly by observing preferences expressed by the customer at a touch point like POS. For e.g. If an offer is accepted or rejected at a touchpoint, the customer profile can be dynamically updated in real-time and used for future decisioning. Examples of hyper-personalization from BFSI and commerce industries According to a Salesforce study, 51% of consumers expect that companies will anticipate their needs and make relevant suggestions before they even make contact. One of the best examples is of Amazon, which creates a unique, hyper-personalized experience for customers. Amazon uses data points like full name, search query, purchase history, average time spent on search, average spend amount, etc. They gather this data using predictive analytics and create a 360-degree customer profile for a deeper understanding of the customers and their shopping habits. This helps them in increasing customer satisfaction with hyper-personalized marketing techniques. For example: A customer searches its database for a pair of headphones, and when they click on the product, the interface automatically recognizes the search and a “Frequently bought together” section will appear on the page. (Source) Within the banking, insurance or telecommunications sectors, brands are now offering tailor-made solutions to customers based on their contextual needs.  An interesting example is that of a European bank that altered its retention strategy because of an interesting insight it noticed. Their initial strategy — which focused on targeting inactive customers — failed to translate into anything significant. The bank then turned to machine learning algorithms to predict currently active customers that were likely to reduce business with the bank based on an analysis of their current transaction patterns. What came out of it was a targeted campaign that focused on high-risk but currently active customers, reducing future churn by 15 percent. (Source) Conclusion Gartner predicts that organizations that excel in personalization will outsell companies that don’t by 20%. B2C enterprises need to consistently deliver products and offers tailored to individual customer’s needs for ensuring personalized experience over touch points. However, they often end up delivering broadly segmented and contextually irrelevant offers which leads to customer dissatisfaction. Offline analysis and heuristic decision making limits their capability to analyze all possible data points and recommend personalized offers at scale and in real-time. Enterprises need a more sophisticated decisioning engine with machine learning capabilities like Offer-X that can enable personalization at scale, recommending next best offers for each customer based on historical behavior, contextual needs and business objectives of enterprises.

Blog Telecom · Mar 28, 2019

Crucial but ignored mid-cycle customer intelligence for Telcos to influence broader engagement

Telecom service providers around the globe are now aware of the potential of data analytics for driving profitable customer engagement as shown by their increased spending in analytics tools and technologies. However, their efforts are still in silos, with marketing, sales and service teams adopting analytics to improve their own sphere of engagement. For example, marketers only use the intelligence drawn from mid-cycle (during the usage phase) customer events to improve segmented marketing programs targeted at improving upsell, cross-sell and retention. There was a very little focus around how this intelligence can probably help other teams who deal with other stages of customer engagement lifecycle like customer acquisition (sales) teams or customer service teams.

Blog Digital Telecom · Mar 15, 2019

How AI and analytics can help enterprises to increase adoption and usage of mobile wallets?

Mobile wallets offer strong benefits for consumers and enterprises alike. Consumers can make instant payments from anywhere and at any time, automatically redeem offers and make safer transactions. On the other hand, enterprises can get an edge over the competitors by providing enhanced customer experiences by leveraging mobile wallets as perfect tools for customer engagement and customer loyalty.

Blog Enterprise · Feb 18, 2019

Lifelong machine learning AutoML challenge: AI that creates AI

AutoML is an emerging AI discipline which attempts to automate the end-to-end process of applying machine learning to real-world problems. In several applications, data arrives continuously (online advertising, telcos, banking, climate modeling etc.), causing offline predictive models built and maintained manually to quickly become obsolete. This presents continuous learning, or Lifelong Machine Learning challenge for an AutoML system, and was the central theme of the NeurIPS 2018 AutoML challenge.

Blog Enterprise · Nov 28, 2018

Cross-domain aspect extraction for sentiment analysis

In the backdrop of Industry 4.0 revolution, the digital channels have become a go-to place for customers for expressing opinions, sharing experiences and giving feedbacks. It becomes imperative for businesses to understand these customer opinions, adapt their offering to the market sentiments and enhance better experiences.

Blog Enterprise · Nov 14, 2018

Embracing new channels of digital engagement: Role of AI

In this era of omni-channel customer engagement, enterprises have to offer a seamless, consistent and personalized experience to your customers at every channel and touch point through which they are interacting. Keeping this in mind, it is very interesting to note that SMS still manages to be a part of almost every marketer’s marketing channel mix. The simplicity and reach that it offers makes it a preferred channel. According to a 2014 study by Pew Internet, 97% of smartphone owners send text messages, making it the most widely used smartphone feature.

Blog Enterprise · Oct 19, 2018

Customer journey analysis: How to mind map your customers

What does a typical customer purchase journey look like? A customer sees your product, buys it then may repeat the purchase if they are satisfied with the outcome. In reality, though, the journey is far more complex. It includes multiple phases, such as switching between multiple devices and hopping from one touch point to another before making the final purchase decision. As per Google’s research, the average consumer uses as many as three to five channels or devices in the course of completing a purchase.

Blog Enterprise · Oct 1, 2018

AutoML : Bringing in real artificial intelligence capabilities

Traditional data science practices focus on solving a point problem after taking into consideration a specific dataset and domain at a given point of time. However, this may not be an effective strategy in terms of scalability and efficiency, as the same model may not provide optimal results when applied to a different dataset or domain.

Blog Enterprise · Sep 12, 2018

New rules of customer engagement: The game of personalization

Digital engagement is the talk of the town. Remarketing, online-to-offline and offline-to-online engagement, switching, and omni-channel engagement paradigms are here to stay. With the increasing adoption of digital channels by customers and enterprises, they have now become an integral part of customer engagement models.

Blog Enterprise · Sep 3, 2018

A matter of opinion – sentiment analysis for businesses

Social media has emerged as a popular sounding board for customers to express their experiences with a brand. Keeping a track of all customer’s emotions will help brands to better understand their feedbacks and opinions. Businesses can leverage NLP-powered sentiment analysis to gain actionable insights from the unstructured data available from social media channels and third party websites. And these insights in turn can drive effective business decisions and strategies. With the rise of the era of social media, customers are always connected and are empowered like never before. They commend. They condemn. They share feedbacks and opinions through social media channels in form of tweets, reviews, chats and comments. Enterprises realize value of capturing and acting on this ‘voice of the customer’. Sentiment analysis is a text analysis process that uses Natural language processing (NLP) to identify and analyze a given text in the document, sentence, or entity/phrase level collected from various channels, according to the underlying tone of the expression. In simpler terms, using sentiment analysis, we can decide whether a document, a sentence or an entity/phrase is having a tone that is positive, negative or neutral. The entities/phrases are called aspect terms and identifying the sentiment for each aspect term is called aspect based sentiment analysis (ABSA). ABSA gives us customer opinions/sentiments on different aspect of a product or a service. Nowadays, researchers are giving more attention for each aspect terms’ sentiment rather than focusing on overall polarity (e.g. positive or negative). This type of analysis gives a relatively more nuanced overview of sentiments. Importance and applications of Aspect Based Sentiment Analysis E-commerce is a good platform where people can express their views/opinions about services and products they access. In this context, aspect-based sentiment analysis specifies aspect terms (e.g. price, service, network, etc.) and their sentiment (positive/negative/neutral) which can help customers to decide what to consume and what to avoid. From the business point of view, it gives a better understanding of products or services and business management can improve the quality of products or services based on deeper insights obtained from reviewers’ comments. The goal here is to identify aspect terms from given reviews and classify them into sentiment categories (positive/negative/neutral). For example, in case of e-commerce domain, the product laptop could have various aspects associated with it, such as price, design, battery life, processor, etc. For instance, if we consider the review “Easy to start up and does not overheat as much as other laptops.” Here ‘start up’ and ‘overheat’ are the two aspect terms and the sentiments of both aspect terms are ‘positive’. In case of telecom domain, ‘app’ and ‘bill amount’ are two aspect terms of the review “Hopeless app. The current bill amount is never updated.” Here the sentiments of both aspect terms are negative. The following visualizations depict an application of this ASBA model on a set of comments provided by random users who attended the Mobile World Congress (MWC) event at Barcelona in 2018. Each bubble represents an aspect and its size represents how many comments are present for this aspect. MWC is green as people have talked about ‘MWC’ mostly in a positive way. Traffic bubble is red as people have already talked about ‘traffic’ mostly in a negative way. ‘Booths’ bubble is shown as blue as it is a new aspect which people have not talked about earlier. Aspect wise sentiment detection after user has entered the comment. Flytxt’s research work on Aspect Based Sentiment Analysis (ABSA) Flytxt has developed a supervised Machine Learning approach for Aspect Based Sentiment Analysis (ABSA). Conditional Random Fields (CRFs) are being used to deploy the supervised model for extracting aspect terms and identifying sentiments from customer reviews and comments. A large number of features are extracted from the reviews and most of the features are domain independent. CRFs assign a well-defined probability distribution over possible labeling, trained by maximum likelihood estimation. For ABSA, in first step, aspect terms are extracted using CRFs with the large set of features (e.g. word itself, context words, part of- speech tags, word frequency, etc.). In second step, each aspect terms’ sentiment/opinion is identified using CRFs. The supervised model is applied on three domains – i) Laptop, ii) Restaurant and iii) Amazon product reviews (e.g. coffee machine, cutlery, microwave, toaster, etc.) The goal of this project is to build a generic model which can be applied to any domain to discover relevant aspect terms and sentiments. We are also building a hybrid model using unsupervised and supervised approaches towards each of the discovered aspect. You can access the white paper here. Conclusion With the ever-expanding data sets in today’s world, tools like sentiment analysis open many gateways for analyzing this data to derive meaningful insights and gain a greater business value. However, there are many challenges in the path of implementing effective sentiment analysis. The emotions expressed by the customers may not be always having a direct tone and can be very complex in nature, like irony or sarcasm. This complicates the process of identifying a clear sentiment. Though, advancement in technology will overcome these challenges. The bottom line is that sentiment analysis is all about converting data into meaningful and actionable information in hands of companies. No matter how complex it is, its benefits are massive.

Blog Telecom · Jul 30, 2018

Leading-edge operators are adopting a digital-first strategy

Operators need to adopt a ‘digital - first’ strategy as consumers are increasingly using digital touch points for customer engagement. This is no longer restricted to a specific demographic but is widespread, as reflected by the increase in smartphone handset penetration worldwide. However, most operators lack clarity on the functional capabilities and characteristics that they must enable on their digital channels. Telecom operators worldwide are focusing on transforming their businesses in order to become Digital Service Providers (DSPs). Each operator has unique requirements and hence must take a personalized route to digital transformation. One of the core aspects of this transformation is the way operators engage with their customers across various customer touch points. Operators’ strategies for this transformation must include: Well-defined digital experience objectives with long - term execution plans Understanding of the competitive landscape so that digital functionality can be prioritized Ability to assess and measure their progress as they take digital initiatives Analysys Mason’s Digital eXperience Index (DXi) research is designed to support operators with the above aspects of their transformation initiatives. It focuses on helping operators to understand the competition in local and global markets, and assesses operators’ current maturity and progress. Furthermore, as all the operators take up digital initiatives, the DXi research identifies the functional capabilities that operators can focus on to differentiate themselves within their markets. This article outlines the DXi study and provides learnings and insights from our research on operators in the Asia–Pacific (APAC) region. Operators that provide greater engagement functionality through their digital channels score highly in Analysys Mason’s DXi assessment. They need to adopt a ‘digital - first’ strategy as consumers are increasingly using digital touch points for customer engagement. This is no longer restricted to a specific demographic but is widespread, as reflected by the increase in smartphone handset penetration worldwide. However, most operators lack clarity on the functional capabilities and characteristics that they must enable on their digital channels. Based on our understanding of consumer preferences, as well as a detailed understanding of functional characteristics and technology maturity, we created the DXi assessment tool to score various features based on their importance in affecting consumers’ digital experiences. These features were classified under marketing, sales or customer service, thereby capturing the entire customer lifecycle, and the emphasis was placed on prominent digital channels, led by operators’ primary smartphone apps (Fig: 2-1-1). The DXi research highlights the push for digital experience by operators in APAC, but few are adopting a ‘digital - first’ approach. In the first phase of our DXi research, we focused on the APAC region and covered 7 countries and 21 operators. Our analysis highlightsthat operators are making progress in enabling greater engagement capabilities on digital channels. We placed all of the operators studied in the ‘early’ or ‘transformational’ stages of their journey, based on their DXi scores (Fig : 2-1-2); none have entered the ‘digitalized’ zone as yet. Our research highlights that the smartphone apps of nearly all operators in the APAC region offer: The ability to view usage information The ability to purchase basic top-ups or bundles The delivery of ecommerce offers from third-party businesses (such as retail outlets or restaurants) The above features demonstrate that operators are enabling digital engagement. However, very few operators have adopted a ‘digital - first’ strategy, that is, one that moves customers to digital channels and focuses on improving in-app engagement functionality. For leading-edge operators, a ‘digital - first’ approach includes enabling advanced functionality such as message-based support within the smartphone app (as used by Digi and Optus), the ability to purchase new services (implemented by Spark and XL Axiata) and the offer of personalized deals based on customers’ usage (Optus has enabled this). Very few operators have enabled these capabilities. In addition to these, operators also need to improve the performance of their apps in order to achieve ‘digital - first’ status. End-user reviews on smartphone app stores are a reflection of app performance, and in our study, only DTAC averaged 4 or more (out of 5) on both Apple’s and Google’s app stores. Most operators scored highly in our DXi assessment for their websites, as these have historically been the primary self-service channels, and as such, operators have enabled them with advanced engagement capabilities. However, customer service features, such as live chat agents, were used by only a few operators despite their ability to reduce customer effort and improve customer experience. Most operators scored highly in our DXi assessment for their websites, as these have historically been the primary self-service channels, and as such, operators have enabled them with advanced engagement capabilities. However, customer service features, such as live chat agents, were used by only a few operators despite their ability to reduce customer effort and improve customer experience. The successful use of virtual assistants and chatbots is another capability that can truly differentiate an operators’ digital experience. Our DXi research indicates that nearly two thirds of operators in the APAC region are experimenting with virtual assistants, with most focusing on customer service. Telkomsel in Indonesia was the only operator in our study that had addressed all the three capabilities of interest (customer service, sales and marketing) with their virtual assistant ‘Veronika’. Our digital experience research is ongoing In addition to undertaking studies on digital experience maturity, we are also correlating our DXi findings with insights from Analysys Mason’s Connected Consumer Survey, to highlight how a rich digital experience can support operators’ business objectives. For example, our preliminary findings suggest that consumers’ intention to churn decreases with an increase in operators’ digital experience maturity. We plan to continue publishing reports as new insights are generated. We are currently working on assessing operators’ digital experience maturity in other regions of the world. All of our findings from this research will be published in the Digital Experience program.

Blog Enterprise · Jul 26, 2018

Infinite data sets and the evolution of data science

Big data essentially concerns itself with large collections of data about events and transactions recorded from the past. Allied terms like “fast data” extend this further and fashion faster updates to this history. But the underlying analytics processes on big data analyze the past; to predict the future. Data sets in this discourse are large, but always finite. Fundamentally, however, the physical universe is different. Data sets that correspond to digital capture of information from events and transactions by and among humans and machines aren’t actually finite - these events, transactions and their data capture will also exist in the future. For example, customer behaviours have events in the past as well as in the future. This philosophy of allowing data sets to extend to the (infinite) future requires the data scientist to think and prepare for a future beyond merely “big” data. Thus, data scientists that predict future customer behaviours will have to contend with infinite data sets. Dealing with infinite data is different Theoretically, some function must exist that will map the historical data set (the history) to target values (the prediction). The canonical machine learning problem is to find a computationally efficient approximation of this function. Data scientists then build predictive models using this approximation to foretell future behaviour. Approximations are derived from compactly representable properties of data sets – most commonly, the statistical distributions that fit the set. However, properties of finite data sets vary at a relatively lower rate than those of infinite data sets. Changes in the statistics of the data must be dealt with on an ongoing basis for infinite data sets. Extra-sensory unrecorded events like political upheaval and natural disaster, and changes in macro-economic, cultural and other trends are inevitable however; and these lead to non-homogeneity and non-stationarity of the distribution. Furthermore, data set representations will change over time for infinite data sets, including global notions of outliers and patterns, and local notions of improved or degraded ability of sensors and variability in availability of streams. Data scientists today build their models assuming homogeneity, stationarity and regularity of representation in the data set. Then they retrain the models when their (perhaps manual) observation of the target variables belies these assumptions. Target values are used for decisions that affect personal and professional lives. Human training of models, however, implies that there is an allowance for inefficient decisions until the training is complete and the model is retrained. Thus models needing frequent manual training don’t suit the cadence and assurance of decisions required for infinite data sets, and data science must evolve to address this problem. Methods to deal with infinite data Infinite data sets require data scientists to advance models and test them at a speed comparable to the arrival of new data; and to balance this speed with the accuracy of the decision the models entail. Also, multiple models may work on multiple data representations (like different granularities of aggregations); hence the ability to have many models being evaluated in parallel is necessary. An evolving conceptual tool for such parallel evaluation is ensemble modelling – choosing a combination of outputs from multiple models. We anticipate more focus in the data science community on building efficient, automated ensemble models to deal with infinite data sets. The data scientist must also prepare for adapting to many variances in data representation and data set properties over the lifetime of the model. Thus the need for data cleansing and data imputation must be sensed and addressed with time and quality guarantees; as should the need for revising approximations in response to property changes. For hand-crafted (non-AI) models, supervision (semi- or full) for training is inevitable; but preparation for the aforementioned variances makes its automation critical. AutoML engineered machine learning pipelines will progress to solve these challenges. Data scientists must keep track of developments in this nascent field and adopt state-of-the-art algorithms for automated feature extraction, construction and transformation, automated handling of skewed and missing data, and automated post-processing and calibration of target values. While handling the expected heterogeneity and non-stationarity in the distributions in the data set and the changes in the data representation; the data scientist should also be able to make new conclusions basis new correlations. In our parlance this means that evolving approximation functions map new information in the target variables. This variation in the target variable mapping is called concept drift. The data scientist would need mechanisms (automated, as above) to detect and correct for concept drift. While this field is evolving (even a precise definition of concept drift eludes the machine learning community today), the data scientist must closely follow developments – like methods that trigger detection of drift, synthetic drift data generators for model testing, and new classifications of drift handling and correction methods. Conclusions In this article we increase the awareness of the knowledge worker for the motivations for the evolution of data sciences. We have discussed the handling of infinite data sets and the different challenges that infinite data sets pose, like variations over time in statistical properties of homogeneity and stationarity. We advise the data scientist to prepare to apply techniques from key areas that will direct the evolution of data sciences for infinite data sets such as ensemble models, AutoML and concept drift.

Blog Telecom · Jun 8, 2018

How is Telecom industry using AI for powering experience economy?

Consumers in digital world are not going to be happy with just the product or service you are offering, rather they seek and expect additional utility in the form of experience. As per IDC report, almost two third of the enterprises are investing in AI systems to deliver this personalised customer experience. And with increasing market volatility and ever-changing customer expectations shaping today’s digital economy, telecom organisations need to pace-up their AI adoption to drive home the advantage through intelligent digital customer engagement.

Blog Telecom · Jun 4, 2018

How is eSIM impacting telecom ecosystem?

Introduced in 1991, the SIM card that we use today revolutionized the telecom industry and drove us into the world of portable communication. In February 2016, at Mobile World Congress, GSMA announced eSIM (embedded SIM) as a standard and opened pathways of new opportunities for consumers, telecom operators and device manufacturers.

Blog Enterprise · May 10, 2018

Forward looking statements on customer engagement enabled by Artificial Intelligence

B2C firms across industries have often been criticized for inconsistent customer experience. According to The Economic Times, 63.5% of the Telcos are currently focusing on making new technology investments, mainly on modern AI and analytics applications to enhance customer experience. The remaining 31.5% is still banking on upgrading legacy systems to achieve it.

Blog Telecom · Apr 26, 2018

Telco data and analytics: Contextualising mobile ads

If you are reading this blog, it is highly likely that you are reading this on your mobile. It is a common sight to see people being hooked on to their phones. Irrespective of one’s needs – whether getting daily dose of news or making purchases, mobile is no longer a mere device to make and receive calls.

Blog BFSI · Mar 29, 2018

Redefine ‘segmentation’ in the digital banking age

Financial Institutions have made people realise that finances are hardly a hiccup when it comes to fulfilling aspirations and achieving life goals. A 25-year-old professional can now own a house which he could have only imagined to buy with 20 years of savings. Aspiring entrepreneurs can now deliver big with a little of his own capital. With affordable interest rates and high liquidity, banks are here to meet financial aspirations of individuals and corporates. The issue now for the Financial Institutions (FIs), especially in the ones targeting retail and MSME sector, is to discover new segments that can potentially provide higher returns and do so in compliance with the core values of the institution. The massive stockpiles of customer data like the demographic details, income and spend patterns are already being leveraged. We already know a number of banking activities that are done over mobile with a substantial number of millennials using the mobile channel as depicted below. source: Payments Cards & Mobile However, there is another set of data comprising their mobile phone banking usage, preferences to their circle of friends and influence based on social media – in other words, their digital persona which largely remains untapped. Dynamic customer base with dynamic needs Non-banking financial companies (NBFCs) play an important role in the mass retail semi-urban and rural sectors. Traditionally these sectors have been of less interest to the banks. Until a year back, NBFCs were content in having business in specialised segments like Vehicle Finance, Gold Loan, etc. However, now the market dynamics has changed. The new age Small Finance Banks, equipped with the experience of being microfinance institutions know the semi urban and rural markets extremely well. Although NBFCs have shown growth on a year on year basis, such growth rate has not reflected the potential growth rate of the overall segment. Considering these factors, NBFCs will now have to look to diversify their portfolio, preferably offer products that can be sold to the existing customer base. We are already seeing this transformation in progress. NBFCs specialised in Gold Loan, are now looking to tap the Vehicle and Home Loan segments, similarly, NBFCs specialising in Vehicle loans are now targeting to grow in Home Loan segment. This is an important transformation that the NBFCs in India are going through and in the long run this diversification will help in increasing Year on Year disbursements, assets under management (AUM), Income, Return on Assets and above all decrease concentration risk. Traditional CRM is static Traditionally Financial Institutions have used demographic information stored in the CRM tools to use segment the customers. Experts say that, in an ideal scenario, with a GDP growth rate of 7-8 % on a year on year basis, the standard of living of people doubles in every 12-14 years. However, how many times does the CRM get updated with this data and what is the cost of this updating. Are there other ways to understand the ever changing preference of customers and meet the growing aspirations of the masses? Segmentation basis from KYC to BYOP Financial Institutions now realise that relying solely on the regular KYC data such as income, age and geography is not enough. In the last decade, financial Institutions have invested high on digital channels which have been readily lapped up by the customers as shown below. source: The Financial Brand Digital footprints can be obtained from every customer interaction that happens with any of the digital channels. Analysing these footprints using the new age analytics, artificial intelligence (AI) and machine learning techniques, reveals behaviour and consumption patterns of the customers. These insights can be used to build a digital persona of the customers - both on the behaviour (online shopping, international travel, mobile wallet usage) and consumption (dining, entertainment, personal vehicles, public transport usage) patterns. This continuous profiling can then be used to predict the spending pattern of a customer and from the spending pattern understand the current needs and predict the future financial needs of the customer. This will give rise to a winning banking model of contextual banking. It allows banks to offer a seamless experiencing by pushing financial offers to the customers at the place and time of need which may coincide with major lifestyle events like children going in for higher education, job change or relocation. Apart from regular promotions, contextual offers can also be used for cross selling or up selling thereby strengthening consumer relationships. In such cases, specific instances like a customer’s account balance not being able to cover a purchase or the customer exceeding the bank credit limit could act as triggers to enable actions like extending a customer’s credit limit or offer an overdraft. In the digital age, customers expect banks to understand their preferred communication channels, willingness to share personal data in various scenarios and ability to engage. A new model – referred to as "bring your own persona" (BYOP), built on the foundations of increasing digital footprint and advanced analytics and AI will emerge as a basis for segmentation in the new digital realm. Follow the telecom and retail innovation Players in the telecom and retail sector have innovated substantially in the digital marketing space. They have already started leveraging the new age machine learning techniques to identify nano segments and extend tailor made offers and services at the right time, right place and through right channel. Financial Institutions have an opportunity to learn from the techniques applied in the telecom and retail industries. Given the change in competitive landscape and customer profiles, financial institutions do not have many options but to adopt, adapt and grow. The key drivers of change in the way financial institutions nurture their customer relationships include the adoption of enabling technology and the insight available from the customer’s digital persona. Organisations that embrace these drivers, use them to better understand their customers’ habits and preferences and combine it with the user experience will have a true strategic advantage.

Blog Telecom · Feb 27, 2018

Are marketers ready to adapt with the shifting mobile consumer base?

India’s mobile consumer base has reached the one billion mark (second only to China), according to a recent study by the Telecom Regulatory Authority of India. India currently has a smartphone user base of over 300 million and the number is likely to grow in the following years. In 2013, there were approximately 170 million internet users in India. This number has grown to 580 million – a whopping 30 percent, year on year. Even the rural India internet user base is expecting staggering growth and is estimated to touch 280 million by 2018. Source: Telecom Regulatory Authority of India (TRAI) The likely key drivers to the internet usage growth include high smartphone penetration, expansion to smaller towns/rural India, increase in affordability due to lower cost, data-enabled handsets and an overall increase in awareness around the potential of internet and the services thereof. The growth in data usage and access to affordable data packs on mobile will drive millions of Indians to do business, get social and participate in governance through their hand-held devices. This growth will also usher in a change in the user ecosystem. Another tectonic shift As the mobile internet user universe continues to expand, there will be a considerable shift in the user demographics like age, gender and also geography. Female audiences belonging to the rural communities will have greater exposure to the mobile internet experience. So far, the growth of the internet user has largely been driven by the Indian youth who have been at the forefront of adopting the online and mobile-centric lifestyle. Even as these young evangelists grow older, they will continue to be active users, boosting the ranks of internet users aged over 25 years. As per the Internet and Mobile Association of India (IAMAI) report, by the end of 2018, users under the age of 25 years will account for 54 per cent of the total number of netizens in urban India, up from 40 per cent in 2013. With older Internet users having more disposable income, they will be more likely to transact online creating business opportunities for e-commerce players and other service providers. Source: Internet and Mobile Association of India (IAMAI) Reaching to ‘connected women’ India’s internet users over the previous years is clearly and quite overwhelmingly skewed in favour of males with females accounting for a meagre 25 per cent of the total user base in 2013.However, this vital aspect of the demographics playbook will also change dramatically, with women expected to constitute almost 33 per cent of the overall online population by 2018. Considering that women control 44 per cent of the total household spend in India, the increasing parity in gender ratios will have a major bearing on the Internet economy - in terms of marketing campaigns and other services directed at women. Companies that cater to products in the categories of groceries, health, well-being & hygiene among others will want to engage with this audience. New segments logging in The Internet class of 2018 will be more from the rural sectors, older, more mobile with lesser gender disparity than their predecessors. This class will open up significant growth opportunities for manufacturers and service providers alike, which can leverage the wider, targeted and more cost-optimal online channels effectively to cater to an increasingly Internet savvy customer base who is a part of this tectonic shift. While there are endless possibilities and the future looks bright, a lot of things need to be taken care of. The most crucial link is the wide availability of high-speed internet and low-cost devices in the far-reaching rural areas of the country coupled with providing internet education and training to the masses. Adapt to tap new opportunities Internet usage in the rural areas of India and among women is on the rise. While e-commerce industry is still in its infancy in rural markets, it is increasing at a rapid pace. The digital medium has a significant influence on the purchasing decisions of the users. India’s marketers need to take all these factors into account and plan now for a bigger, dynamic and an even more complex digital marketplace. While the choices for users in Indian metros are reaching a near saturation point, it is time to reach out to rural users which have almost always faced a dearth of premium services and an overall lack of availability of wide choices at their disposal. These customers who are undergoing an identity makeover have the money power and are definitely entitled to premium services such as online entertainment, state of the art ad tech and fin tech services , and even the ‘new retailing’ which is a convergence of online and offline retail, all of it at par with what their predecessors have almost always got.  

Blog Telecom · Feb 19, 2018

The four pillars of next generation digital customer value management

It is an established fact that Communication Service Providers (CSPs) have not been successful in protecting their traditional voice and messaging revenue streams from the Over the Top (OTT) players. Evaluations have shown that over-the-top (OTT) applications generate 20% to 50% less revenue for CSPs. While the exponential rise in data consumption has provided some relief, but this has not been enough to overcome the consistent decline in mobile voice average revenue per user (ARPU). Competitive pricing pressure and OTT disruption have been accompanied by rapidly increasing infrastructure costs as demands on bandwidth and speed have continued to grow exponentially. As per a World Economic Forum Report, there has been 13%-36% decline in ARPU in all regions globally since 2012. Decoding the industry challenges The telecommunications industry is at the forefront of digital transformation, both as an industry witnessing large-scale change in its market environment and as a key driver of worldwide digitisation. Investment by the telecommunications industry in technology and interoperability has underpinned an immense shift in information and capital through the global economy, while providing the building blocks for the emergence of entirely new business models across industries. Competitive marketplace and increasing usage of alternatives such as Wi-Fi and OTTs is seen as a threat to the telco traditional revenue. For these reasons, traditional offerings in postpaid such as voice and data now comprise unlimited packs due to which, CSPs are experiencing revenue stagnancy. On the other hand, an increasing number of consumers as well as businesses is engaging with digital subscriptions for entertainment, news, books and even healthcare. OTTs have cannibalised the traditional telecom revenue and it is clear that telecom offerings are longer a driver for incremental revenue. The shifting CSPs battleground Customers are beginning to judge the quality of the products or services they receive not only against the multiple players within that sector but also against the customer service they have experienced in other industries. Customers now expect levels of personalisation, on-demand access and quality standards matching those of the leaders in any industry. Making a note of this fact has become increasingly crucial for operators with expectations evolving faster than the industry’s ability to meet them. Strengthening CSP’s Digital subscription arm As digital disruptors and OTT players attack traditional communication revenues, telcos are pursuing opportunities to climb the digital ladder up to the services layer. With a large customer base, ownership of key infrastructure and strong technology capabilities, telecom operators are trying to take on the role of digital services providers, often emerging as disruptors to other industries. Bundling option for digital services along with telecom billing can provide visibility of subscription, digital usage and activities. Accessing online and digital behaviour of customers can allow CSPs to create digital customer centric products and services like video streaming, music subscription, mobile Wallets, micro-insurance and so on. Role of Customer Value Management (CVM) platform Creating Unified identity across the digital subscription Uncovering anonymity of customer is critical to serve them in digital world. Hence Identity management is an essential component of the digital marketing ecosystem. In simple terms, it is the ability to capture, source, integrate, cleanse and link data to recognise different consumers. CVM solutions need to empower the marketers to consolidate online and offline data sources to support unique identification of consumers and constantly uncover the anonymity of customers by profiling him more accurate and deeper. Persona Creation & Prediction of Customers’ Needs Once the anonymity is uncovered, it is time to create detailed persona for understanding your customers on a deeper level – leveraging emotional, motivational, situational, behavioral, and environmental context. Use of advanced analytics and artificial intelligence techniques helps in predicting their affinity, propensity so as to help markets understand the latent and momentary needs of customers. Automated, intelligent and real-time contextual customer engagement Telcos now wish to move beyond traditional descriptive and exploratory analytics (mainly used for post-mortem of business decisions, to advanced analytics and machine learning for automated decision making. These new big data analytics technology platforms are transforming personalisation by allowing telcos to manage customer expectations in the very moments of truth. Artificial Intelligence (AI) can greatly personalise and thereby, transform the customer engagement process. Tools like decision management, natural language processing (NLP), text analytics, and machine learning areas can be used to engage customers more deeply in the moment. They allow organisations to provide the type of experience the customer expects, balanced with the needs of the business – goals, margins, acquisition targets, top-line growth, etc. Entertainment OTT applications are real enablers for accessing a subscriber’s profiling, most recent usage behaviour & sentiments in real-time. Integrating these parameters with telecom data sources will enable customer CVM platforms to engage customers proficiently. Incremental Revenue Measurement and Optimisation Such subscription tactics have allowed operators to create unique service bundles which can also be charged in bill using multi charging models such as pay-per-view, pay-per day, pay-per genre and pay-per device. Distributing the revenues gathered through the CSP bill and pre-pay account to all players in the value has huge potential to generate incremental revenue for CSPs. Along with customer engagement, it is absolutely essential that measurement methodologies are also put in place to track incremental revenue generated along with digital journeys of customers. This will also help in giving a feedback to marketers as to which services are doing well and what strategies are working and not working leading to optimisation. Summing it all up Digital transformation has the potential to deliver evolving need for evolving telecom customers, while providing businesses with new opportunities towards value (revenue) creation for telecom organisations. Subscription services for entertainment, handset insurance, cab and live TV are partnering with telcos merging subscription billing models within telecom billing. Such unified integration of digital services will allow users to manage subscriptions conveniently and telcos to engage and influence digital behaviour of customers. Digital subscription has proven higher engagement for customers, creating visibility on digital usage of subscribers and has potential to yield greater revenue for telecom companies and for those reasons Telecom CVM has to incorporate digital content subscriptions strategies to thrive subscriber experience & grab uplifting revenue opportunities.

Blog Enterprise · Jan 16, 2018

Cognitive marketing: ‘Packaged intelligence’ in action

Cognitive marketing’ is coming of age with the emergence and increasing adoption of Artificial Intelligence driven marketing applications that can think and act on their own. Machines are increasingly going to take more marketing decisions. It may appear far-fetched, but it is definitely possible now with the advancements in Machine Learning and Artificial Intelligence domain. Some call this ‘cognitive marketing’, which will signal the emergence of many marketing applications which can think and act by itself. And some call it marketing automation 2.0 to refer to the mass adoption of artificial intelligence driven automation of digital customer engagement activities. The drivers Machines are becoming ‘smarter,’ thanks to the advancements in computing, sophistication in analytics technologies and access to large volumes of data to create broader and deeper intelligence. However machines are not only good at decision making, but also in acting on them seamlessly for digital customer engagement making use of smart devices, sensors and touch points. Chatbots are seen as an early form of such self-learning intelligence being put into digital marketing commercially. They come with built in intelligence for driving contextually relevant engagement with customers, again saving effort and resources of customer service team. Cognitive Intelligence driven Marketing With ‘smarter’ machines, marketers can look forward to focus on strategic decision making leaving operational decision making to machines. A cognitive marketing application can leverage Artificial Intelligence to independently make decisions learning from large volume of data sets. This will increase the speed of decisioning which will in turn improve operational efficiency. Subjective heuristic manual rules will be replaced by machine learning driven cognitive decisioning. Cognitive intelligence is achieved by ‘packaging’ various analytical models that continuously infer intelligence from underlying data and then embedding them to the decisioning workflows. For example a typical customer lifecycle marketing program can be automated with the application of various ‘packaged analytical models’ across the workflow as shown below. Since operational decisions are many and often repeatable in nature, machines can take the role of marketers in operational decision making and execution. Since Artificial Intelligence emphasises on feedback based optimisation, the operational decision making gets better over time just like how human make better decision with more experience and market know how. However machines have a significant edge over humans in the sense that decisions are always made based on facts and subjective biases doesn’t influence decision making, again adding to the efficiency. Man Vs Machines It is a widely accepted notion that humans can do a better job when it comes to taking decisions that need emotional and innovative thinking. However, the definition of ‘smartness’ has changed over the years and is now typically associated with academic performance or efficiency of doing tasks with minimal errors. This is where machines are all set to compete with humans and even outsmart them. Smart machines can ingest, process, store, and access information faster and efficiently than humans. Right kind of data can be ingested and prepared for analysis, this is what I call ‘smart’ and ‘good’ data which has more intrinsic value. Artificial Intelligence and machine learning can derive patterns much faster and produce a wider choice of options and predicted outcomes for making decisions better suited to a given problem and context. With the latest deep learning techniques using feedback loops, machines can even learn faster optimizing the decisions and actions continuously. Examples of Assistive Intelligence are automated marketing campaigns or automated back-office functions such billing, customer service and so on. The machine learning capability of the assisted intelligence allows for quality improvement of efficiency matrices. Assisted intelligence is all about empowering machines to execute actions. Objective here is more about improving operational efficiency, through clearly defined rule based repeatable tasks that can be automated using a software application or a physical robot, simulating the activities of human beings. Augmented intelligence is all about man-machine collaborated decision making. Today machines can make use of advanced analytics and visualisation capabilities to come up with deeper insights as well as precise recommendations for humans to take decisions in the moment of relevance. Suggesting a recommended list of offers for a customer service agent to extend to customers walking by to the store is a good example. Autonomous intelligence is about machines taking decisions and acting upon them without direct human involvement; it is the promise of future. There are some early adaptors, for eg: today NASDAQ has automated 75% of its trading using autonomous intelligence. Other use of this, would be in self-driving cars. With the advancements that is happening around AI, self-driven products and services with in-built intelligence, conversational and marketing capabilities is not a distant dream. The only challenge may be ability to adopt to changing technologies and incorporating it to build complex, but user centric applications for the enterprise to fully automate their digital customer engagement activities.

Blog Enterprise · Nov 30, 2017

Artificial Intelligence to advance machine vision

Human brain is capable of some amazing tasks like understanding the world in a single visual frame. It takes only a few tens of milliseconds for the brain to recognize the category of an object or environment. Further, humans are capable of learning and remembering a diverse set of places and patterns, and solving complex problems such as planning and navigation, involving vision, perception and cognition. The neural architecture of human beings have inspired researchers to simulate such abilities on machines to solve challenging problems using artificial intelligence. Consequently, deep learning has emerged as a powerful tool to solve problems involving machine vision and perception. Through artificial intelligence, machines have come closer to human ability in several cognitive tasks such as identifying and recognising objects and environment. Image classification is one of the hallmark tasks of computer vision. It allows defining a context for object recognition which will have diverse applications. The classical problem in computer vision, is that of determining whether or not image data contains some specific object, feature, or an activity of interest. Data Science R&D team at Flytxt has released an end-to-end scene recognition pipeline consisting of feature extraction, encoding, pooling and classification. The primary objective of this work is to clearly outline the practical implementation of a basic scene-recognition pipeline having a reasonable accuracy, using conventional computer vision techniques (without applying deep learning techniques), in python, using open-source libraries. Scene recognition approach with local and global descriptors The approach used by Flytxt R&D team utilizes global feature descriptors as well as local feature descriptors from images simultaneously, to form a hybrid feature descriptor corresponding to each image. It comprises of using DAISY features associated with key points within images as the local feature descriptor (similar to SIFT features) and histogram of oriented gradients (HOG) corresponding to an entire image as a global descriptor. As images vary in view point, scale, orientation, illumination and occlusion level of objects, extracting robust features (such as DAISY, SIFT, HOG etc.) to represent images is critical for building an effective image classification model.  As the number of key points vary across images, multiple DAISY descriptors would exist for each image. We use a bag-of-visual-words concept to encode each image as a histogram of dimensionality ‘K’ (where K is the vocabulary size or the number of possible “visual words”). Clustering is used to group DAISY features to form the "visual words" for encoding. Since training data could have several images, total number of DAISY descriptors could be very large (in millions). We use Mini-Batch K-Means algorithm to reduce the complexity of clustering, for fast encoding. The histogram corresponding to each image is augmented with its HOG descriptor using a pooling procedure, to generate the final feature vector corresponding to each image. The associated class label (e.g. living room, store etc.) would be already available since the training dataset is pre-labelled. A multi-class SVM (each class corresponds to a scene category such as living room, store etc.) is trained and cross validated to assess the model quality on the fifteen scene categories dataset. The average accuracy of the model was 76.4% in the case of a 40%–60% random split of images into training and testing datasets respectively. A detailed description of the approach is available in here. Also, a full implementation of the proposed model is available here.

Blog Enterprise · Nov 22, 2017

Are you choosing the right mobile advertising channel?

One of the hardest decisions for advertisers is choosing the right channel for reaching out to their audience segments. The mobile revolution has led to an explosion in the use of smaller screens for advertising. On mobile media itself, there are text, voice and video channels which can be exploited. So picking the right channel within the mobile platform on the basis of the content, target audience and objective of the campaign can be tricky. Big hits Be it a bar of Soap, Car, Insurance Policy or a Real Estate property, the beauty of mobile advertising is that anything and everything can be sold through various mobile channels. Apart from the advantage of smaller form factor of smartphones, marketers enjoy a diverse portfolio of channels that offer better CTRs and conversion rates. There are a number of reports which suggest that digital marketing spend is consistently increasing. It is also a known fact that digital marketers stand to gain by traditional ads dying. Traditional advertising medium like TV, radio and print do not offer a personalised touch point with the customer. Mobile advertising solves this problem to a great extent. Moreover it is more integrated too since consumers may now click on any ad and proceed to buying seamlessly. Mobile advertising works great for a wide range of ticket sizes – be it large and remote businesses or even local businesses. The mobile medium offers the ability to ‘geofence’ prospective new customers which allows businesses to only spend money on customers who are located within a specific geography. Such level of precise targeting may not be possible with most of the traditional advertising medium. And a few misses It is a no-brainer that advertising entertains, emotionally moves and can also just inform users. But there can also be a negative reaction to advertising like irritation and fatigue which can adversely impact the performance of a campaign. Hence, care should be taken in choosing the right channel for a product to be marketed for better campaign performance. Let us draw a comparison of advertising on Television vis-a-vis advertising on a Mobile Phone. While the consumer largely views the television at the time of leisure but the same is not the case with a mobile phone. The consumer uses his mobile handset for work as well as for leisure. Hence, the mobile channel chosen for a campaign plays an important role so that we do not invade someone’s personal space at work as this may have a negative reaction to the campaign. This needs to be kept in mind largely when a campaign is to be run in urban areas. Making the right choice Each mobile advertising channel has its unique reach and unique strengths. More importantly, a number of factors play a major role in determining what channel you must choose for mobile advertising like the product categories, audience profiles and behavior, location and the overall objective of the campaign. Display ads are great to build a brand while SMS marketing is very effective when you want to market exclusive offers. On the other hand, a combination of channels can be used if you want to alert your users about anything from a new feature update, to a sale, or a new product. Picking the right channel for your business vertical is a matter of understanding your consumer’s psyche and intent. The ‘idleness’ of the user can also determine success of a channel. A promotional SMS and MMS delivered to and read by the user when he is at leisure will command a high visibility. On the other hand, promotional OBD mandates the user to pick up the OBD call, in this case the ‘irritation’ factor needs to be kept in mind. Let us assume that your campaign targets a group which includes working professionals like Salaried Employees, SMEs or Proprietors in urban areas. If the advertiser is marketing a Real Estate property and if advertising time slots allotted by the Telecom Regulatory Authority of India are between 9am to 9pm, then a promotional call during their working hours can leave a negative impact. Getting the best for your business The suitable channel or channels may differ from vertical to vertical depending on the audience to be targeted. The target audience for automobile and real estate comprises mainly urban male citizens with smartphones. In such cases, RCM may be a suitable channel. Channels may also differ based on the appropriate content for certain verticals. Much like the highly colorful yet disposable nature of FMCG goods, the vertical also demands an engaging content for a shorter span. However, it also needs channels with a wider reach across multiple regions including the vernacular-speaking, feature phone-using tier-2 audience. And hence SMS and OBD channels work the best. Promotional content for entertainment sector needs to be appealing – visually and also to the ears. Hence, MMS, OBS and to a lesser extent SMS should suffice. Somewhat like the entertainment vertical, the travel sector is also a lot about the visual experiences and the MMS channel can do the needful. Conclusion To conclude, channels are meant to support each other. One message in one channel may not reach all the potential users. As an advertiser, it is important for you to distribute your messaging across multiple channels using a single platform at the same time or in a sequence or in a scattered form for maximum reach. In the end, the success of a campaign depends on picking the right channel to reach out to your audience.

Blog Enterprise · Nov 20, 2017

The rise of intelligent voice agents – Your virtual assistant

Enterprises today have defined digital customer experience as one of the primary drivers of business. Customers choose to communicate with enterprises based on perceived ease, quality and timeliness of service. And, they expect prompt response in real time which would incite a sort of personalised engagement. The plain vanilla Chatbots A number of digital savvy enterprises have launched Chatbots to engage with customers on touch points like website and mobile app. They handle tasks which require little to no human intervention. However, Chatbots, quite often, do not understand the user well and have a short attention span. They are designed to respond only to specific commands and adhere to only rigid, pre-defined conversation flow. It is for the user to figure out the right phrases that need to be typed to get appropriate response. Moreover, Chatbots lack the personality of a human being, creating a feeling of detachment for the customer. Another important drawback is the security loopholes in a Chatbots-based ecosystem. Users can be tricked into sharing their confidential information. There have been numerous instances of phishing attacks masterminded by hackers using Chatbots as the bait. To sum it all up, talking to a Chatbot is usually by no means time-saving and also defeats the purpose of convenience and creating a satisfying personal customer experience. Voice recognition is getting perfect Despite the limitations, it is an accepted fact that bots help in automating a part of the customer engagement, thereby reducing operating costs. However, there is plenty of scope for further improvement. An estimated 325.8 million people used voice control in the beginning of the year according to Global Web Index (i.e., 10% of the online population. By 2019, the voice recognition market is estimated to be a $601 million industry.) The leading players in this space are Apple Siri, Amazon Alexa, Google Assistant, Microsoft Cortana and Facebook M. Amazon Alexa enabled devices have the lion's share in the market followed by Google Assistant. Amazon sold 4.4 million Echo units in its first full year of sales and over 500,000 Google Home units were shipped in 2016. Microsoft Cortana has 133 million monthly users. 50% of all the searches will be voice searches by 2020. Growing popularity of voice search and interactions is mainly due to the advancements and perfection in Natural Language Processing that these players have brought in their platforms. Apple's Siri and Google's voice recognition have achieved almost 95% accuracy rate for the English language. Turning Voice Agents to Intelligent Agents Majority of the communication is not in the words we speak, but in the subtext, tone, and voice inflections of the speaker. Intelligent agents should be able to interpret voice inflections and map them onto emotions such as happiness, surprise, anger, and disgust. Artificial intelligence engine and natural language processing can breathe life into them- almost literally! Intelligent Voice Agents are capable of further taking over routine activities which otherwise require human intervention. Intelligent voice agents allow users to initiate a natural voice conversation. Backed by machine learning and artificial intelligence capabilities, the agents can think and learn about the user's surroundings, habits and usage patterns. Moreover, they need to have intelligence on customer's historic usage, device, location and so many other variables that influence behaviour to make the responses dynamic and relevant to specific customers. For example, if you are making a plan to travel to London, you could ask your virtual assistant to recommend an international roaming pack. Your virtual assistant would analyse your previous usage pattern and based on your duration of stay it will recommend an apt roaming pack. It can even understand that your price affinity so as to suggest the right pack out of the options. Another example would be your banking experience using intelligent voice agents. You can interact with your bank through the virtual assistant on applying for a credit card. The virtual assistant powered by AI at the back end can analyse your income and previous transactions and recommend a credit card with the apt spending limit. Digital customer engagement can be fortified with intelligent voice interfaces capable of personalising voice conversations on any digital touch point supporting the likes of Alexa and Assistant. (Flytxt Intelligent Voice Interface) An evolution waiting to happen! Enterprises can incorporate intelligent voice agents in several areas of business operations. Intelligent Voice Agents will become long-term virtual assistants to customers as they are designed to evolve with the customer. They will help in personalising interactions & elevate customer experience to the next level.

Blog Telecom · Nov 15, 2017

The journey from CSPs to DSPs: Digital eXperience index

Justin van der Lande (Principal Analyst) leads the Analytics and Digital Experience research programmes, which are part of Analysys Mason's Telecoms Software and Networks research stream. He specialises in business intelligence and analytics tools, the functionality of which cuts across all of the research programmes in this area. In this article, the author explains about things to be considered in the transformation from Communication Service Providers (CSPs) to the Digital Service Providers (DSPs). The Digital eXperience Index (DXi) for analytics will help communications service providers (CSPs) assess their customers' current digital experience and plan how they can improve this by leveraging the power of big data to create deeply personalised engagements. Analysys Mason has conducted multiple surveys and interviews with CSPs worldwide as part of the ongoing research into the progress of their transformations into digital service providers (DSPs). Each CSP has different expectations out of this transformation, but all agree on the pressing need to improve their customers' digital experience. Improving customers' digital experience is a massive undertaking that will have an impact on multiple departments and systems. Big data will play a key role in the successful transformation of CSPs to DSPs. However, many CSPs still do not have the requisite framework or infrastructure to gain insights from customer data. Each department often has its own independent analytics function, thus preventing the CSP from gaining an overall picture. CSPs must approach the application of analytical techniques from a holistic organisational perspective instead of individual departments designing isolated approach in order to successfully transform into DSPs. Our research for the Digital eXperience Index has identified three key characteristics that will enhance customers' digital experience from an analytics perspective (Figure 1). Analytical systems will have the greatest impact when customers are able to have highly-personalised engagements. Figure 1: Top characteristics that will enhance customers' digital experience from an analytics perspective CSPs have adopted differing approaches to digital transformation, which has given rise to conflicting views of the ideal process CSPs are investing considerable time and resources in becoming DSPs and most share a broad consensus on the need to digitise their support infrastructure as part of this process. However, there is no common approach to transforming their underlying network and operational infrastructure. Each CSP will undergo its own unique DSP transformation which is often dependent on factors such as business priorities, existing support infrastructure and region of operation. The transformation of a CSP into a DSP from the perspective of its customers will mean that they receive a consistent digital experience, comparable to their engagement with other online companies. CSPs are adopting different approaches to transforming their underlying support systems and there are often conflicting views on the different aspects of the digital transformation journey. A CSP's ability to extract insights from large data sets and feed these back to improve services is essential to improve customer engagement at a systems level. However, the presence of disparate legacy systems within most CSPs' support infrastructure severely limits their ability to acquire insights from big data. The Analysys Mason Digital eXperience Index provides an effective means for CSPs to assess their progress and compare against competitors within the wider market, based on a standard framework. Robust analytics infrastructure is essential if CSPs are to provide an engaging and fully-digitised experience Analysis Masons's Digital eXperience Index research has highlighted that CSPs have different priorities for digitisation (Figure 2). CSPs need to implement a robust supporting framework that will enable them to make informed decisions based on analysis of underlying customer and usage data if they are to benefit most from digitisation from an analytics perspective. Churn reduction: CSPs have made extensive use of churn models for over a decade. However, the sophistication of these models has increased dramatically as the number and types of services offered by CSPs and the popularity of OTT services have increased. Future churn models will be more complex and supported by extensive analysis of usage data in near-real time, which demands a stable analytics platform. Acquiring new customers: Customer acquisition, especially in developed markets with over 100% saturation, requires CSPs to be aware of clickstream data1 and to understand potential customers' requirement by tracking this before acquiring them. Selling more to current customers: CSPs need to develop contextual awareness to upsell to existing customers – they must know what services customers need and when they need them. Cutting costs: Many leading CSPs increasingly rely on analytical models to allocate support teams and network resource to improve utilisation. Figure 2: Important benefits that CSPs expect from digitisation, October 20152 CSPs must implement automated processes (with use cases driven by business needs, rather than IT functions) if they are to support mass personalisation to improve customer experience. Traditional operations that require business intelligence or analytics models developed by a central IT department will not scale to meet the requirements of mass personalisation. CSPs must deploy tools that enable business development teams to define broad requirements for different customer journeys, which can then be implemented by automated processes. This will require access to the data in a suitable format, as well as appropriate tools. The underlying data infrastructure must support real-time data gathering and analysis, and can create actions in near-real time. Data must be available in real time to provide up-to-date interactions with customers when they are accessing services or systems. The ability to act in near-real time will improve customer experience by enabling CSPs to influence customer journeys as they happen. Changing the routes that customers or prospective customers take will ensure the best possible outcomes. Personalisation of customer interactions requires complex analysis of large volumes of data – vendors must consider how to support this and if automation is necessary. Mass personalisation increases the number of potential actions that need to be considered for each customer journey. The necessity of building algorithms based on every customer decision places a high overhead on the teams tasked with creating these algorithms. Machine learning can be used to automate development of these algorithms, based on available historic data and CSPs' desired outcomes.

Blog Telecom · Oct 30, 2017

Extending Telco insights to online advertising

The advertising world has evolved over a period of time. With the growing popularity of smartphones and affordable data plans, online advertising, especially, display advertising has become a hot inventory in demand for digital marketers. However, advertisers always had one challenge to prove the worth of online/display advertising; which has been the lack of having enough insights to do proper targeting. This is where Telcos can play a bigger role; they arguably hold the largest repository of insights on digital consumer behaviour as today's consumers spend more time on these handheld devices. Internet access buoyed by mobile revolution The ubiquity of smartphones is undeniable with the mechanism for engagement stretching itself beyond the device. It comes as no surprise then that India is considered as the world's second largest market in Asia Pacific for smartphone users. A research says that by 2020, the number of Indians accessing the Internet over mobile devices is expected to touch a whopping 600 million. A home network, with the smartphone at the crux, can now integrate with TV, internet, and all other communications mechanisms. Online advertising on the rise Online advertising is no more a nascent phenomenon in the Indian advertising industry. It has lived up to expectations and is giving other advertising mediums a run for their money. Over a period of time, it has brought tremendous value for publishers (web sites including social media) and advertisers (brands, agencies) alike. In India, the digital advertising will comprise 12.7% of all the ad spend this year. There has been a tectonic shift from traditional forms of advertising with a larger focus on digital. While print and television still dominate the Indian advertising industry, digital remains the fastest growing platform with a year on year 40% growth. Lack of 'insights' to capture right browsing eyes Despite the increasing digital ad spend and the growth of the online advertising industry, it leaves a lot to be desired. Many companies in India are still using traditional mass advertising practices on online media. Most conventional data management platforms (DMPs) on the supply side use 3rd party audiences and rely heavily on their digital behaviour to build audience segments. This affects the quality and accuracy which in turn defeats the purpose which may result in less optimised ads, inefficient media spend, and more media wastage. Advertisers need a detailed profile of users with regard to their location, interest, gender, age group, demographics etc. to target them with more precision. Telco insights: The much needed adrenaline shot? It is a known fact that a user spends more time on mobile than any other device. The user relies on this device for numerous activities – cab booking, buying movie tickets, payment of utility bills, salary credit updates, payment of insurance premium payments, etc. The user data available is extremely valuable with regard to location, device, customer information, demographics, household income, purchasing information and personal preferences for health, astrology, sports, music, film and drama. The depth and quality of the data are the most coveted attributes that most of the sectors are looking for with focus on individual data classes like location and usage data. For instance, brands in the travel sector look for location data. Similarly, media and entertainment brands give weightage to data around content & application usage. When crunched and analysed by a DMP, the wealth of refined Telco data can be utilised for accurate ad placements in the online world. Telco Insights and Online Media - A marriage made in heaven As per Flytxt's mobile ad network in partnership with Telcos, mADmart's experience, mobile display ads and Facebook ads run through Telco targeting have resulted in higher CTRs and better conversion rates. These have also resulted in higher engagements and better ROI for the advertisers. Telcos can provide the authenticated user data and behaviour-based analytics as tools to differentiate and provide added-value to both advertisers and publishers. Telcos can even change the competitive landscape of digital advertising by establishing horizontal data and advertising technology partnerships across geographic boundaries. Thus, telcos are perfectly positioned to provide hugely valuable insights for brands and advertisers to reach specific online audiences at scale, thanks to the range and depth of data and insight available to them. It is high time that advertisers recognised the data sets that telcos offered and how they can benefit from them.

Blog Telecom · Oct 30, 2017

Reeling digital interactions to Customer Value in real-time

"Going forward, real-time marketing is going to be the holy grail of marketing." – Jonathan Mildenhall, Chief Marketing Office, Airbnb Feb '2013, Oreo, the cookie brand, turned out to be the biggest advertiser during super bowl event, an extravaganza considered to be the biggest real estate for the advertisers, without spending even a single penny. They flashed a tweet "You can still dunk in the dark." during an unexpected blackout during the game. It set a new norm in the act of real-time advertising in the industry till date. However, was it a mere coincidence to put up such a successful campaign within the fraction of a second? Perhaps not! The marketer planned their probable moves to capture the right probable event at the right time seizing the moment of truth to connect with customers. Today's customers are connected to enterprise more than ever on digital channels. They have shifted to purchasing and paying online for most of their lifestyle products including telecom services. They let the world know about their travel plans in advance to capture the attention of friends, opening up avenues for Telcos to upsell roaming packs. When they start searching for new handsets, perhaps it is the right time to offer them a new handset bundled plan. Telecom industry being the custodians of most of these digital interactions is swarmed with humongous data about such customer events and actions as high as terabytes captured on a daily basis. For Telco marketers, these actions are not just data, they are potential opportunities to offer customers with a suitable value proposition to delight them and maximise customer value. Essence of real-time marketing: Opportunity is now Real-time marketing academically refers to tracking customer actions and events continuously and then triggering the right marketing action like extending the best fit offer based on his persona and intent for that context. It is one of the most binding tool for Marketers towards executing Customer Value Management (CVM) programs with success. With real-time context aware marketing actions, marketers can tap any opportunity to upsell, cross-sell, retain and enhancing digital experience across customer lifecycle journey. The agile actions on part of marketer can potentially influence the customer decision leading to more positive outcomes when it comes to new subscriptions, top-ups and so on. However many times customer interactions are context driven and they present only a small window of opportunity for marketers to make use of it. Once the context changes, customer need and intent may also change with it. Hence marketer needs to preplan probable moves by a customer to capture right moment of truth. They need to identify possible customer actions and map marketing actions in advance to win those moments of truth. Today it is mostly mapped using heuristic rules, but tomorrow this will see machine learned mapping which can potentially do it more accurate and at larger scale, even on a one to one basis. The predicted 'sphere' of customer actions and triggers The secret lies in apt visualisation of customer actions and events and then making use of every possible window of engagement opportunity with a relevant proposition for the customer. Capturing the significant changes in customer behavior and actions, understanding the explicit and implicit needs and intent and then responding to them on time with a highly contextual proposal is bound to create a superior impact when compared with generic offers and one for all kind engagement. Along with the traditional customer actions like service activation, making payments etc., today there is a huge surge in customer's digital lifestyle events as well. The triggers such as browsing a site, using a mobile wallet, buying patterns, ecommerce triggers like cart abandonment, email triggers, bounce rates, interest based triggers, category based triggers, location based triggers are all up for the grabbing. Responses on time to influence customer decisions and outcomes Below is an attempt to capture a few scenarios which capitalise triggers generated for a customer: Extending to Non-Telco world The benefits of real-time marketing doesn't get confined to Telco marketers alone. Envision targeting a customer landing in Disneyland between 3-5 PM on a weekday basis their prior interest on social media. The author can use the location trigger to capture the moment and set a rolling campaign in the time frame basis his past behavior. Taking an example, how fruitful will it will be for Mc Donald's to accord such a customer with an offer with a history of purchase behavior at fast food chains to boost the off-peak sales? Certainly, the tract needs to be streamlined beforehand to make a mark on the field day. Summing it up! In profusion of overall competition and every marketer keeping their customer's panoramic coverage, real-time marketing is no longer an option. Marketer needs to be on the ring side when it comes to capitalising customer affair and the blueprint of such a plot needs to be laced well in advance. The prosumer is persistently triggering data congenial to the marketer. Take Note: "The consumer is not a moron; she is your wife." – David Ogilvy Cherish and create moments at the right time to foster your eternal bond.

Blog Telecom · Sep 11, 2017

Analytical model templates: Think one-to-many!

If we go deeper and analyse many business problems, we can observe them sharing common traits across the industries like the problem of customer churn or margin optimisation. It is beneficial for any analytics practice to pursue opportunities in solving such problems. The effort spent on developing, validating, and optimising analytical models for live deployment is huge, hence it makes sense if we can think one-to-many and have packaged model templates with applicability across the industries. The horizontal problem of too many choices In this article, we will discuss one such problem and see the design considerations that can be kept in mind while packaging the analytics capabilities to solve the problem. Enterprises especially in industries like Telecom, BFSI and Retail compete aggressively to acquire and retain customers. This leads to customers always having multiple options to choose from at any point of time like multiple malls or multiple websites to shop or multiple bank accounts or use of multiple SIMs. This has led to a tectonic shift from mere customer acquisition to customer retention and loyalty enhancement. Let us see how enterprises have been plagued with this common horizontal problem and how analytics practice can approach to building ‘packaged intelligence’ to help business users address this in different industries. Multi-SIM in Telecom Usage of multiple SIMs is quite common now a days especially in the emerging markets, where prepaid plans are the norm and subscribers are most price-sensitive. Lately, with the introduction of 4G services and Telcos competing to provide attractive data plans at the lowest prices, multiple SIM ownership has increased sharply. Multi-SIM owners typically have mobile devices which have dual-SIM standby feature so as to utilise the benefit of switching between the telecom carriers. Usage of multiple SIMs may have an adverse effect on the revenues and the churn rate of a Telco. Therefore in a highly competitive market scenario, it is crucial for a Telco to refrain its customers from switching to other telecom service providers. Multiple Accounts in Banking Banks and financial institutions these days look at providing a wholesome customer experience so as to sustain competitive advantage by increasing customer loyalty, targeting a greater return on promotional campaigns, reducing costs, and improving operational efficiencies. To improve the experience in line with customer expectations, banks must obtain a better understanding of their customers’ behaviors and motivations. Banking customers are increasingly demanding and insist on being identified with unique needs which are addressed personally. Predictive analytics can provide valuable and actionable insights over banks’ data of users holding multiple accounts so that they can encourage customers to use their account and maximise revenue. Multiple retail avenues to shop around Again choices are plenty for customers when it comes to retail sites and malls to shop around. Retailers are looking for ways to win over consumers, whether it’s in-store, online, at a kiosk or on the phone. With the right marketing tools and insights, retailers can make customers feel special. By analysing past click-through behavior, preferences, and history in real-time, retailers can provide a better customer experience by right price adjustments, recommendations and promotions. One methodology, multiple use cases The practice applies a distinctive evaluation technique to identify multiple buying alternatives in any industry, like having multiple current accounts in bank or buying from multiple retail malls or buying multiple SIMs. The customer might use the alternatives to get pricing or service quality benefits. Such customer behavior can be identified at an early stage so as to provide customized offers to keep them engaged and increase business with you. It will also help to identify loyal and not-so-loyal customers. A market research is conducted on a sample set of users to verify and categorise primary and secondary users. A list of questions is prepared to understand the customer inclination towards the primary and secondary choices. This information along with specific usage based KPIs are used to build the predictive model. Generally KPIs reflecting demographic, usage behavior and footfall patterns are used to analyse the customer behavior on choosing and using products from available alternatives. The RFM (regency, frequency and monetary value) model can be used to analyse the usage behavior. For example in telecom scenario, a survey is conducted on sample base to determine whether the user is using a particular service provider’s SIM as a primary or a secondary SIM. Then that information is attached with specific usage based KPIs like Off-net Minutes of Use (MOU), On-net MOU, average revenue per user (ARPU), handset type etc. to build a model to predict the multi-SIM behavior of larger base. The market research questionnaire and the KPIs are all considered to be a part of the primary data. This sample dataset is further segregated into test and train dataset. Train dataset is used to train the classification models. The trained model is then tested on the test dataset and accuracy of all the models is compared to find out the best model for prediction. One model template may fit all It is highly imperative for analytics practice to understand these business problems with common traits across different industries in depth so as to develop a methodology for identifying and building the best possible model that will help enterprises across different industries to address these problems. Once Analytics practice adopts this approach, they can scale up on delivery of analytics output in an efficient manner. However enterprises will also need appropriate technology platform to implement this collaborative analytics approach, where these model templates can be deployed, tested and optimised for their specific use case.

Blog Telecom · Aug 29, 2017

A glimpse into digital advertising pricing

We are living in a mobile-first world, where there are 7.2 billion people and 7.5 billion mobile devices. With mobile ad spend expected to touch Rs10, 000 crore by 2018, and account for over 70 percent of digital ad spend, mobile ads are becoming the biggest digital advertising market. Although both India and China have crossed the 1 billion mobile connections mark, India is the fastest growing mobile market, adding more subscribers annually than China. With the increasing penetration of data and low priced smartphones, there is an increased opportunity for the advertisers to leverage mobile channel in a better way. Advertisers can now reach out to their audiences at any point of time basis their knowledge about the user behaviour. There are no free lunches However, there is a cost involved to reach out to the audiences to showcase the products. Hence every advertiser plan their mobile budgets basis the marketing objectives they have to achieve. As marketers tailor their ad strategies to mobile consumers, they need to decide how to optimally use their marketing investment Not all advertisers in the market have humongous marketing budgets and targeted advertising comes to their rescue by reaching out to their precise target group, preventing spillage and hence effective utilisation of marketing budgets. We can safely say that Mobile is one of the most cost effective way to market your offering in alignment with the end goal of the business. Types of pricing models Today’s digital advertising is filled with many different pricing options. Just a few decades ago, there only used to be two options: CPC (cost per click) and CPM (cost per 1000 impressions). Now there are dozens. However, the advertisers chiefly use pricing options like CPM, CPC, and CPA (Cost per Action) which help the advertisers to make the maximum out of their marketing budgets. CPM (cost per 1000 impressions) The advertiser pays the publisher for every 1000 times the advertisement is displayed to a consumer. This is a model wherein, the publisher is paid every time the user views the ad. This kind of pricing model is popular when the main objective for the advertiser is creating awareness and success of the campaign can be judged basis the reach created through the campaign. CPM is particularly effective when you have high performing creative, as the cost of each action will go down as the total actions taken goes up. CPC (Cost per click) Cost per click is popular with publishers who use services such as Google AdSense, AdBrite, etc. This is the pricing model where the advertiser is paid basis the clicks user has done after viewing the ad. This model is effective for advertisers when they want their audiences to engage on their ads. It also helps them compare the performance of various ad networks. The ratio of clicks generated to impressions served is known as CTR and higher number reflects higher audience engagement. It is especially popular with advertisers because of the ability to track return on investment. CPA (Cost per Action) The CPA pricing model allows advertisers a bit more assurance for a quantifiable consumer engagement. This pricing model is used by advertisers only when the user has done any relevant action like app install, filling up of lead form, registration for the service etc. This model is effective for advertisers with restricted budgets, since it allows the payment to publisher when the advertiser actually has a relevant lead, making it a no risk option for the marketers. Each of these pricing models has its own pros and cons. The number of different pricing models will only continue to increase in the future with the increase in number of platforms and media competing for share of digital advertising budget. One price does not fit all With time, the mobile advertising industry has seen significant development and has evolved over years. Also, mobile advertising is more appealing to brands since ad blocking capabilities does not seem to be as advanced for smartphones as in the case of desktop advertising. A number of such factors have led to an overall increase in the mobile advertising costs for marketers. Apart from the SMS, mobile ads also comprise banner advertisements, video placements and other rich media experiences, which are thought to be more engaging and therefore more valuable to advertisers. There was a time when it was difficult to place effective ads on old mobile phones with small, low resolution screens. Hence, mobile ads were very cheap too. New mobile phones have larger screens and a high resolution leading to more advertiser-friendly formats. Hence, inventories like the rich media ad units receive higher CPMs as compared to SMS advertising. Also, higher prices may be more applicable to certain ad categories. Display advertising such as traditional banner ads command nearly twice the price on mobile compared to desktop while the price for a preroll video advertisements that show up before a clip score remains the same. Mobile advertising prices can also rise dramatically seasonally. For example Click through rate is high during periods like the holiday season when the working middle and upper middle class is likely to shop more, leading to an increase in the mobile ad prices. Getting the best of the lot Mobile ads are relatively new, but they are quickly emerging as the most sought after digital media for advertisers. As more consumers shift their sphere of activities to a tablet or mobile phone, advertisers will embrace this digital media more to promote their products and services. Hence it is important for advertisers to understand the pricing models associated with mobile media so as to make optimal use of their budget to achieve desired campaign objectives.

Blog Telecom · Aug 28, 2017

The coming of age of cognitive computing and data science

Industrial Conference of Data Mining (ICDM) and International Conference of Machine Learning and Data Mining (MLDM) are two established conferences in the field of Data Mining and Machine Learning. The conference proceedings are published by Springer, one of the most reputed international publishing houses. While ICDM is more inclined towards application of data mining and machine learning to solve real world business problems, MLDM attracts researchers from both Industries and academia for discussions on theoretical and practical data science. This year, both the conferences were co-located at Newark, New Jersey and Flytxt presented a research paper at each of these conferences. Industrial Conference of Data Mining (ICDM) A handful of the papers presented in ICDM were pertinent to industrial applications in domains like retail, transportation, semiconductor and healthcare. Natural Language Processing, Association Rule Mining, Multivariate Time Series Analysis, Large Scale Classification, Sub graph Pattern Mining were key themes of presentations at this year’s conference. Paper presentation at ICDM 2017 The paper titled “Towards a Large Scale Practical Churn Model in Prepaid Mobile Markets” presented at ICDM focuses on building a scalable prediction model to predict subscribers who are likely to churn in the near future. Here churn refers to the revenue churn (or inactivity based churn) where a subscriber stops consuming the network services (voice, data, SMS) for a period of 15 consecutive days. It emphasises practical aspects of model development such as data extraction, feature engineering, dimensionality reduction, model evaluation, distributed training and prediction and demonstrates an end to end modelling considering a real world dataset of 5 million active subscribers form a renowned Asian CSP. Apart from these, the paper also provides insights on how the model output (in the form of actionable lift chart) can help marketers in designing appropriate retention campaigns. At the end, it touches upon the productionalisation aspects of the model where model can run seamlessly as an end-to-end workflow in the production environment. The paper can be read here. AI: Deep Learning and Cognitive Computing to be the way forward? One of the key highlights of the conference was an open discussion where we discussed and brainstormed about the current and future scope of AI and how “Deep Learning” and “Cognitive Computing” are going to shape up our future. It was interesting to know from the practitioners about their general skepticism towards deep learning a couple of decades back, when data was scant even to train a simple feed forward neural network. However now deep learning has transformed the landscape of analytics with abundance of data and sophistication of techniques available to train data for solving even the most complex problems. One of the core issues of deep learning - “interpretability” was given much attention during the discussion. Other issues like need for large training set, longer training time, and actionability-vs-accuracy trade off were also highlighted. Apart of these, few open ended questions were also debated upon as follows: Will AI be ever have the motivation as human being have? Will AI ever ask relevant questions it does not know answer to? Will AI help in hypothesis generation rather than hypothesis validation based on a given context? MLDM 2017 Unlike ICDM, MLDM was a bit more theoretical in nature along with a few industry use cases utilising machine learning techniques. Some of the key themes of this conference were optimisation in large scale machine learning, deep learning in computer vision, anomaly detection and sequential pattern mining. Paper presentation at MLDM 2017 The paper titled ‘High Accuracy Predictive Modelling for Customer Churn Prediction in Telecom Industry’ was presented at MLDM. This paper was an extension of the first paper where efficacy of applying deep learning techniques on telecom dataset was analysed with respect to other standard linear and nonlinear machine learning techniques. The paper can be read here. Read more about this paper here. An Interesting guest lecture at MLDM On the Computer Vision front, there was an interesting talk by Professor Petia Radeva from the University of Barcelona, who discussed how nutritional habits (food intake behaviour) of users can be learned automatically via a deep learning framework, which could be helpful in efficient monitoring of medical conditions such as diabetes, obesity etc. A novel and fast approach based on Convolution Neural Network (CNN) was proposed for detecting and recognising food in conventional (pictures taken manually by mobile phones and other cameras) as well as egocentric images (pictures taken automatically by a wearable camera). Professor Petia discussed about a novel food-related objects localisation algorithm, which can classify an image into food type or non-food type category, and discover bounding boxes containing generic food in a single image. She also discussed about a food recognition algorithm which can learn to recognise the type of food present in an image. Interaction with practitioners and professors It was an enriching experience when we got a chance to meet people from industry bigwigs like Intel, General Motors, and VMWare and to know about problems they are trying to solve using data science. It was also great to meet professors from reputed universities like the National University of Singapore and Indiana University of Pennsylvania and know their research areas. Overall, it was a great learning experience.

Blog Telecom · Aug 16, 2017

Analytics Out of the Box: Towards optimized scale, speed and skill

An analytics practice uses data to deliver knowledge – as actionable insights, recommendations, smart data visualisations and partially or fully automated actions so as to deliver optimum business impact. The analytics practice is typically constituted by a specially nominated cross-functional (internal or out-sourced) team with the required core competencies – data sciences, decision sciences, data engineering and business analysis – within a business organization. This article offers the author’s views on how modern analytics practices can efficiently scale up their knowledge delivery with the use of appropriate technology. The supply-demand imbalance The gap in the demand and supply for relevant skills in analytics practice is a major challenge. There is a perpetual and ever growing need for data scientists. The academic institutions are rapidly coming up with new courses in the field to create adequate supply. The parody “how many engineers does it take to fit a light bulb?” is an apt metaphor for this context. The answer may be simple if we take it in the literate sense, but the same could lead to different answers if we think beyond it and interpret the problem in different ways. If we take the viewpoint that analytics practices are light bulbs and data scientists are engineers, the following statements are plausible: The time-and-material effort as well as the expected quality standards for fitting each (analytics practice) light bulb is significant enough to require more than a few (data scientists) engineers; Fitting a light bulb is a complex operation that requires multi-specialised and multi-skilled engineers; There are too many light bulbs to fit but the engineers are too busy, hence there is always a waiting time as the availability of engineer is a challenge. The functioning of an analytics practice is often misunderstood in the same way –the number of analytics outcomes that can be delivered by the practice is mostly perceived to be proportional to the number of data scientists it employs! The quest for efficiency The acquisition of right skills by personnel augmentation is necessary for analytics practice to function. However, analytics practice also need to focus on attaining the desired efficiency and scale in their processes so that they deliver more impactful knowledge and respond more dynamically and faster to the demands of their parent businesses. A common way to increase the efficiency of any delivery pipeline is to optimize production resources by maximizing product reusability and simplifying customizations of the delivered product. These efficiency goals are best specified for the analytics practice by orienting its output as ‘analytics product’ to connote packaging for ready consumption and ease of use. There is a deliberate attempt here not to call it an ‘analytical model’. It can be much more than that. Sometimes multiple models can be packaged along with the execution environment, necessary validations and a delivery/consumption mechanism put together in a box as an ‘analytics product’. This ‘analytics product’ philosophy resonates in our title Analytics Out-of-the-Box - a term chosen to describe packaging and delivery mechanisms. (The pun “out of the box” thinking is deliberate.) An analytics product must have the following characteristics: Ease of use: easily configurable user/application interfaces for analytics consumers (humans and applications); Repeatability: the ability to be instantiated and reused efficiently across multiple use cases. In addition to meeting these goals, analytics practices also need to increase the efficiency of analytics product lifecycles – from conception to delivery, support and optimisation. The making of ‘analytics product’ Churning out well-defined analytics products on an efficient lifecycle requires the production line to have the following characteristics: Packaging appropriate data connectors and data transformation logic Ability to develop and package multiple analytical models into one analytics product; A common execution environment for the iterative testing, deployment and product usage – this avoids repeated retro-fitting of products to different execution environments; An ability to continuously deliver multiple analytics products for one set of consumers; A feedback mechanism to feed in performance data to optimise the analytics product and Sufficiently abstracted controls for in-service configuration and performance management of analytics products in use. Analytics practices would do well to choose one software platform/product that allows these functions in their analytics product lifecycles. The traditional and modern Business Intelligence vendors also talk about ‘prepackaged analytics applications’, however this is too limited in its scope. They typically envisage providing a standard set of prepackaged dashboards and reports for helping solve standardized business problems. The ‘analytics product’ has to go beyond this, it needs to have flexibility ingrained to account for different data sources, data models, analytics techniques beyond statistical ones, types of users, output delivery mechanism, consumption resource and so on. The need is for ‘analytics products’ that can help the analytics practice to bring in speed and efficiency for solving even the most complex business problems like churn. Going forward the ‘analytics product’ is also expected to self-optimize with feedback loop also incorporated within its design. Analytics practices implementing these recommendations will see significant scale in efficiency and quality of delivery, reduction in friction with their consumers, and avoidance of people dependability.

Blog Telecom · Aug 9, 2017

Embedding intelligence in enterprise applications: Objective, efficient decision making

Enterprise software applications traditionally are designed for executing operations, however decisioning mostly need intelligent manual intervention. This approach places very high value in the experience of individuals, thereby bringing a greater deal of subjectivity into the system, which can be avoided. With the advanced utilisation of big data, machine learning and advanced business analytics close to 90% of the subjectivity involved can be reduced in operational decision making. Further, efficient systems and applications create well-oiled execution paradigm. However currently the reality is far from ideal. Large enterprises are creating Data sciences teams with the objective of bringing in greater deal of intelligence into their systems and workflows. However the disconnect between business and data science teams often leads to execution silos creating non permeable situations for data based insights to flow across the Enterprise. Enterprise applications typically capture the transactions but are not designed to capture the knowledge and experience which can take them to next level in decision making. Packaging right kind of intelligence within Enterprise applications can solve intelligent decisioning problem to a greater extent. Data science offers a high degree of sophistication in making organisational and tactical decisions utilising data. Currently CIOs are charting out strategies for data science and advanced analytics across the enterprise. But many of the solutions are piecemeal and do not offer the degree of automation that guarantees repeatable efficiencies and seamless execution across the enterprise. It is important that Data scientists create and design models which can be embedded in the workflow of business applications. They have to work with business practitioners to understand the larger context of the design and execution of the model. The biggest value proposition that packaged analytics brings in is the Objectivity & Efficiency in complex decision making and repeatable execution scenarios not to undermine other factors like reducing a wide range of inefficiencies around data preparation, model validation and more importantly human bias. Embedding Intelligence in Enterprise Workflows A few examples where ‘packaged’ analytics and intelligence creates efficient decision making in business workflows/applications: Wealth management advisory tools can have packaged analytics that profile and rank an investment product based on the context, life stage and risk appetite of investors. This can help a wealth management advisors garner insights about their customers but also reduce his own risk of under servicing with biased and non personalised advice based on his intuition. Small time stock trading accounts can be equipped with embedded analytics that can help in leveraging price & trade advantage to gain more returns. Digital customer engagement process can be enriched with analytical models that can help in discovering product affinity and churn propensity of customers. Chatbots and Virtual private Assistants can be enriched with Machine Learning and Advanced analytics bringing in a higher degree of relevance and cognitive element which can make conversation efficient and effective in improving conversions and customer satisfaction. Machine will not replace Human doctors in the near future, but EPR and health care applications equipped with advanced algorithms can offer suggestive approach to detect and diagnose life threatening ailments like cancer in early stages Retail Marketing could involve planning at a large scale which may involve customer value differentiation, understanding customer usage and adaptive behaviors, predicting inventory spikes and stock outs executed at Point of sale level. Such top down execution would greatly need packaged analytics to deal with contexts arising at store level to be rolled up to organizational level. Law enforcing agencies of governments can use crowd analytics added to video surveillance to manage and predict crowd movements in large events like congregations, fairs, etc. and managing and avoiding untoward incidents. The analytics under the hood of various popular apps like Amazon , Netflix driving recommendations and highly personalised experiences for their customers In all the above examples, there is an incumbent application used by the enterprise for performing specific business function. The addition of packaged analytics improves not only the functioning and performance of Enterprise Applications, but also makes the user highly efficient in attaining his goals. Challenges in ‘packaging’ intelligence Evolving a data supply chain: The strategy for success of analytics based execution involves nurtured investments not just on the infrastructure but also in creating a nimble supply chain of data. Right data need to be ingested, aggregated and enriched for a wide variety of downstream use cases in the context of enterprise and its current set of applications. It starts from the premise that organization might want to do it incrementally, than a big bang approach. Hence the overarching framework should be nimble and future ready. Systematically and progressively, at different junctures packaged analytics can be introduced and observed for improved efficiencies. Process for supervised intervention: It’s important that packaged analytics applications are designed as tools for aiding users, without re-engineering the existing process flows. Embedded or packaged analytics need to be implemented in a non-intrusive way to drive ease of use and enterprise adoption. The beginning phases of analytics adoption also need careful selection of training sets and treatment on outliers so that the algorithm learns the expected behavior to develop higher degree of accuracy. Trust, Transparency and buttoned control: It is important that in-house practitioners are taken into confidence about the relevance and use of packaged analytical models, which are embedded in the workflows of business applications. Specialist practitioners should be able to manage configurable aspects of the model to closely align with expectations in production environments. Impact should be validated with the quality of the output of the models and tweaked to drive outcomes as envisaged by business teams. Enterprise applications with embedded intelligence presents enormous opportunities for digital enterprises to gain tangible economic benefits through better operational decision making and efficient execution. However implementing such a practice successfully will need a slight reconfiguration of how business teams and data science teams collaborate and the use of technology tools with right out of the box analytics and execution capabilities.

Blog Telecom · Jul 28, 2017

Regression modelling for solving transportation problems

KDD CUP is a globally recognised data science competition where data scientists and researchers from reputed universities as well as industries across the globe participate to apply their knowledge and skills to solve challenging knowledge discovery and data mining problems. It is organised annually by ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining - a leading professional organisation of data miners). The challenge this year This year’s KDD Cup problem pertains to building predictive models capable of forecasting average travel time of vehicles as well as tollgate traffic volume on road networks. The problem posed on this competition is a difficult and significant one for traffic authorities as well as end users. Traffic patterns evolve with time, as several factors such as seasonality, weather conditions, business hours, holidays, road accidents, etc. influence it. Stochastic variations of traffic patterns make the problem more difficult to model with traditional approaches. Traffic authorities need to plan their traffic management strategies including traffic control, diversions, route maintenance, etc. in such a way that it causes minimal disruption and inconvenience to users. End users can also plan their travel effectively by knowing about congestions and expected delays in travel routes. Many mapping apps provide such capabilities today. Applying analytics algorithms on historical traffic data acquired via mobile apps as well as various sensors enable such capabilities. From the above challenges and/or motivations, KDD Cup 2017 organisers proposed following two tasks for predicting future traffic flow and estimate time of arrival. Task-1: This task involves predicting average travel time (ATT) of vehicles for a specific route for every 20-minute time window.Task-2: This task involves predicting average traffic volume (ATV) at each of the toll gates for every 20-minute time window. Flytxt data sciences R&D team took part in this challenge under the guidance of Prof. Santanu Chaudhury and Dr. Brejesh Lall from the Department of Electrical Engineering, IIT Delhi. A total of 3547 teams across the world participated in KDD Cup 2017. The organisers provided some historic datasets capturing historic vehicle travel time, vehicle volume, route information, road link structure, information on tollgates, weather etc., pertaining to a road network in China. Flytxt team secured 34th position for task 1 and 30th position for task 2, landing within the top 1% of the contestants. Microsoft China secured the first position for both the tasks. Flytxt approaches for average travel time and traffic volume predictions: The average travel time for a particular route depends on route length, route width, link length, number of links, type of the vehicles, historic traffic patterns observed on the route/link, etc. We modelled the problem as a regression problem and utilised a variety of regression techniques including K-nearest Neighbours (KNN), support vector regression (SVR), deep learning, boosting (XGBoost Regressor), etc. We preprocessed the historic datasets and generated different features based on route properties (e.g. route length, width, historic average travel time for each route, etc.), link properties (e.g. link length, link travel time, etc.), weather conditions, holidays, etc. We built six promising models based on route level and link level information to estimate average travel time. For model performance evaluation, Mean Absolute Percentage Error (MAPE) was used. The cross-validation MAPE scores of all six models are shown in Figure-1. We utilised model ensembles to combine individual models to improve the overall predictive performance. A median ensemble model produced the best MAPE scores on unseen data. Similar to task-1, average traffic volume prediction was modelled as a regression problem. We also preprocessed historic datasets and generated different features based on tollgate information, route information, weather, etc. We trained several models including random forest regression, boosting (XGBoost Regressor), and regression with dynamic bayesian networks. XGBoost Regressor produced the best cross-validation MAPE score (0.1355) among all candidates. XGBoost Regressor model achieved 0.1607 MAPE score on phase-2 data. A great learning experience The challenge provided a good learning experience - learning from evolving data (non-stationarity), accounting for stochastic factors such as weather, controlling overfitting and model complexity, etc. We also observed significance difference of leaderboard MAPE scores between two phases of unseen datasets. This could potentially be attributed to evolution of data distributions across the two phases making phase-1 modelling approaches not to work so well for phase-2 data. Overall, being a part of the KDD 2017 was an enriching experience and gave us a great opportunity to rub shoulders with renowned researchers and practitioners in data mining, knowledge discovery, data analytics, and big data from across the globe.

Blog Enterprise · Jul 27, 2017

Industry 4.0 revolution calls for enterprise analytics

We are at the verge of a technological revolution that will fundamentally change the way we live, work and interact with one another. The scope and complexity of this transformation is such that its impact is felt upon all stakeholders of the global polity, from the public and private enterprises to academia and civil society. The Fourth Industrial Revolution - Industry 4.0 The First Industrial evolution gave birth to the modern factory, with the mechanization of textile industry, which used water and steam power to mechanize production. The Second Revolution used moving assembly line that ushered in mass production using electricity. The Third industrial revolution, was digital - applied electronics and information technology to automate production. Fourth Industrial Revolution or Industry4.0 is conceptualised as an upgrade of the Third revolution. While the Third Industry Revolution focused on the automation of single machines and processes, Industry4.0 focuses on the end-to-end digitisation of all physical assets and is characterized by seamless integration of all entities – machines, systems, people, and so on. What will change - on consumer side and on enterprise side? The digitalisation, and digitisation as part of industry 4.0 create new ways of serving customer requirements and many enterprises see it as significant disruption of existing industry value chains. Tectonic shifts are already visible in consumer behavior and needs as they move towards the digital world. This will force enterprises to change the way they design, market, and deliver products and services. Industry 4.0 impacts enterprises in four main areas—namely customer expectations, product enhancement, collaborative innovation, and organizational forms. Customers are at the epicentre of the business with focus on improving how customers are served. Physical products and services, can now be enhanced with digital capabilities that increase their value. The digital economy is going to be driven by products that are created leveraging the information asset. New technologies will make such assets more durable and resilient, while data and analytics will transform how they are maintained and used. The need for enterprises to stay relevant and differentiate on customer experience will demand new forms of collaboration for business. How enterprises are gearing up for digital transformation? Most of the enterprises are digitising essential functions within their internal operations, as well as with their horizontal partners along the value chain. In addition, they are enhancing their product portfolio with digital functionalities and introducing innovative, data-driven services. Data fuels Industry4.0 revolution; hence the capability to do data analytics is the prerequisite for enterprises to thrive in digital economy. Enterprises are building processes and adopting technologies to use data analytics in their decision-making not only at a strategic level, but more importantly on an operational level. Many enterprises have established dedicated data analytics functions, either on a corporate level or on a business unit level to remain close to the operational business. Both the developed and the developing markets are striving to gain through Industry4.0, however approaches vary. In countries like Japan and Germany, enterprises focus more on digitising internal operations and partnering across the horizontal value chain. With high investment in technology and employee training, they view their digital transformation primarily in terms of gains in operational efficiency, cost reduction and quality assurance. Enterprises in the United States of America invest more heavily in developing disruptive business models, with focus on rapidly digitising their product and service portfolios. The labour intensive countries like China are more interested in automating and digitising manufacturing and production processes. Across these approaches, what lies common is the customer-centricity. Why analytics is a key enabler for enterprises in their transformation journey? As customers become centre of the digital business and ecosystem, there arise an increased need to understand customers more holistically in order to serve them better, and more importantly, in a given context. Customer experience in digital age is now a sum total of all those contextual experiences. This is why enterprises need to think beyond historical data based Business Intelligence. They need to have capabilities for advanced analytics to derive deeper insights and accurate foresights as well as real-time decisioning to act upon them quickly. Huge amount of data that is generated with end-to-end digitisation and integration, brings little value without the right use of analytics techniques to provide insights at the point of decisioning itself. It will in turn enable companies to create personalised and more contextually relevant products and services, which usually generate significantly higher margins than generic offerings. Enterprises use data to drive decisions so as to control and improve their overall business planning and operations. To succeed, enterprises will need to use data in predictive, forward-looking ways that make sense of market developments and customer behaviour to improve products and develop new products and services. Differentiating new age analytics vs traditional analytics Traditionally analytics was conceived as a tool that could produce and capture a larger quantity of historical data to discern patterns for improving internal business decisions. Sophisticated modelling capabilities, and functionalities like simulation and optimisation make advanced analytics more of a trouble shooter tool than a reporting tool. The techniques used by advanced analytics are more future oriented. For example, a predictive model can help to predict which customers are going to churn. The fundamental difference between traditional and advanced analytics, is in the process followed to design and solve a business problem. In traditional analytics, the analysis is typically built to be repeatable. The types of information analysed and the format in which the information is presented is predefined. The advanced analytics techniques uses a set of analysis and data mining to gain business insight. It is more proactive and adhoc as compared to traditional analytics. Data used in traditional analytics is more structured. Reports based on historical data is used by enterprises to understand operational performance. Advanced analytics on the other hand helps enterprises to capture unique behaviour of each customer enabling personalisation of marketing activities and improved marketing ROI. Also advanced analytics has the capability to use unstructured data such as those from social media providing enterprises with access to real-time information. Thus organisations can monitor market sentiments and effectiveness of marketing campaign and also improve their product design by analysing social comments. Enterprise analytics: Leads to business agility and transformational benefits Things happen at the blink of an eye in the digital world. Enterprises who are agile in responding to the dynamic market and customer needs are going to have a decisive edge over the others. Hence for digital enterprises, the ability to instantly digest data, derive insights and put them into decision making frameworks and workflows is what will make an enterprise analytics infrastructure stand apart from the current tools and technologies. Having right insights at the right time help enterprises to not only identify, but also make use of possible avenues for enhancing customer experience, operational efficiency and even business and product innovation. An analytics platform that can not only accelerate data to value but also do it most efficiently is one of the critical technology elements that will drive enterprises forward through this demanding industry4.0 revolution phase to gain transformational benefits.

Blog Telecom · Jun 29, 2017

Pooling in customer insights: the way forward for market research

Many enterprises today rely on market research companies to gain knowledge about consumers, competitors and the overall market. Organizations spend 1-2 % of their revenues on Market Research (MR) surveys and Point of Sale (POS) studies. However, despite the exorbitant spending, insights are often limited in nature to support strong business decisions. The author lays down the inherent challenges in the market research area specifically on the data front and how MR industry can reinvent its practices by pooling insights from various digital sources, one of the major one being the Telco data. Physical surveys and other scattered data sources, which are currently the primary sources of market research, have issues with regard to authenticity and reliability, quality of interpretation and turnaround times. Other problems include lack of interest from the subject respondents, biased response and incorrect information recorded due to the privacy concerns of the respondents. Surveys are done over a sample size of a few thousand people which often fails to capture the actual variations in the population leading to skewed, inaccurate and imprecise insights impacting overall quality of the research. The 'mobile' advantage With enterprises across the world embracing digitalisation, even customer engagement is undergoing subsequent change. Telcos, being the enablers and chief conduit of this digital world, amass huge amounts of consumer data related to customer behaviour and transaction patterns as they engage more in the digital channels. By virtue of this, Telcos are capable of providing highly valuable accurate, granular and timely consumer insights encompassing demographics, location, behavior and context. Pooling in of 'customer insights' The advancements in technologies for data storage and analytics makes it possible for now bringing in all these varieties of data coming to Telcos because of consumer engagement at different 'digital' enterprises to transform the way how market research is done especially around consumer behavior. Telco provided consumer insights provide a unique adavtange that they are based on empirical sources of data gathered from different industries by recording the actual activity and transactions done by the consumers in digital channels, ruling out scope for biases. These insights can be presented to market research companies in anonymised aggregated format without revealing any personally identifiable information (PII) information. These aggregated insights are captured over a base of millions of people and thus present a more accurate picture of the variations. Most importantly, once the data analytics infrastructure is in place at the Telcos, these insights can be updated more frequently say daily, weekly, monthly and yearly format facilitating time critical business decisions with better responsiveness to competition and market. In traditional forms of market research, while it has been possible to measure the reach of the medium and response, Telco data also has location advantage. With Telco data, it is now possible to know where consumers are at different points of time, in what directions they are moving and what activities on mobile are they engaged in at different locations and times. Though conventional MR practices continue to hold relevance, the digital insights captured by Telcoswill add immense value to the data that forms the core of all MR activities. In a nutshell, Telco enabled digital insights add value in the following ways: Faster access to a variety of data from customer interactions and transactions in digital form Aggregated and anonymised insights – so consumer privacy is maintained Accuracy of data is significantly high – free of different biases- based on empirical sources of data More Diverse and comprehensive insights coming from transactions from different enterprises Cost of acquiring data is much less and involves mostly single time investments to set up systems to capture and process insights Market Research 2.0: lot more to offer across industries Telco-obtained consumer insights have the potential to add value in many more industries than can be reached by traditional MR. The variety and the novelty of the use cases that can be achieved by pooling in digital insights far supersedes the insights that can be obtained through traditional surveys. Handsets/device manufacturing, retail, e-commerce, public infrastructure, banking, media measurement, App analytics are just some of the industries where a lot more value can be added by using Telco provided insights in combination with MR insights. Some of the examples of how market research can benefit from insights drawn from Telco data have been mentioned below: Device/Handset Research Research done by market research companies for handsets are often very broad level and based on shipment and sales data available from their POS systems. Telco data has the ability to redefine this research by providing highly granular device insights like market share movements and migration trends for new as well as existing handsets, persona of the users using different devices, their distribution across various parameters like demographics, usage, psychographics, location etc. The spending patterns from credit card companies or banks can further help in deriving a more accurate affordability matrix for consumer segments. App Analytics The rich and diverse Telco data can provide great value to app developers with access to App Usage behavior of consumers and even a comparative usage analysis of various Apps. These App Usage insights are highly valuable for App Developers looking to optimise their App strategy and understand their consumers. These insights could also be valuable for App publishers to leverage their inventory to its maximum potential by understanding the profile of such people. Infrastructure & Urban Planning Using location stitching on insights, Telcos can add a lot of value to this sector in numerous ways. The data can be put to use for better traffic management, new infrastructure development like roads, stores, shopping areas, restaurants and offices and developing new regions by analysing the profile of citizens. In the end, Telco and other enterprise data definitely gives an edge over traditional survey based data for advancing the market research practice. It all boils down to how efficiently we can pull in variety of enterprise data, transform it to deeper actionable insights and make these insights available for enterprises to aid in their strategic and tactical decision making.

Blog Telecom · Jun 29, 2017

Measure the digital campaign: the 5 rules to follow

Netflix and Amazon - what is that these amazing companies have in common? Both of them are quite successful ‘digital marketers’. They revel in acquiring and engaging with customers online. Both these companies treat ‘customer’ and ‘content’ as the pillars of their market growth. They have almost perfected the art of market segmentation. The "segments of one": micro-segments that target each customer uniquely, allow them to convert visitors to loyal and high value customers. Every B2C enterprise wants to extend personalised experience to its customers with tailor-made offers fitting their contextual needs and lifecycle stage. And to achieve this, they need to run thousands of daily, weekly and monthly campaigns targeting millions of customers bucketed to different segments on multiple inbound and outbound channels. With new delivery methods and channels as well as advancements in customer data analytics, campaign management has become a complex far-flung proposition for marketers. Digital campaigns run across a variety of digital channels- email, social media platforms, company websites, communities and blogs, and on mobile devices. And as the so called ‘system of engagement’ technology become more sophisticated, digital marketing will get more automated and intelligent. Finding order in chaos To be successful, every marketing campaign should address audience segmentation, content development, email automation, omni - channel communication and message personalisation. These multiple, complex elements make it extremely difficult to measure the efficacy of every campaign that is executed. The marketer always has to answer the million-dollar questions as to which metrics need to be used to measure the Return of investment (ROI) of digital campaign or what should be the time-frame for measurement or what should be the benchmark of success. While different industries would use different metrics to measure the success of their digital campaigns, which again would depend on the type of product they are selling, the type of customers they are targeting, visibility of the company in the region and acceptance of the products in the market. However as a bold attempt, we can define 5 Golden rules that every marketer could follow to measure impact while executing digital campaign Define expected ROI or ROO – Before running any campaign, it is very important to set the expectations clear. Return on Investment and Return on Objective are two different ideologies. Not everything can be measured in terms of money and not everything can be measured in a short time frame. Using Control Group - The idea of a control group is simple. Select a random (or nearly random) sample from your campaign's marketing list and exclude them from promotion. Then measure the control group's activity and compare it to the activity of the group targeted via a campaign. The difference between the control and campaign group gives you a pretty good notion of how effective – and profitable – the campaign is. The basic hypothesis that is put into practice here is that a certain fraction of the customers targeted are going to purchase from you anyway during the campaign period irrespective of the campaign. The control group lets you filter out that effect, as well as the effects of other channels which may be influencing behaviour, such as display advertising. Reasonable clicks / uptakes / purchase rate - All the campaigns are run with the objective of achieving measurable returns. It is important to ensure that conversion rate is a reasonable number. Benchmarking needs to be done with industry standards. For e.g. the average conversion rate for telecom products is around 15 %, and that for e-commerce is around 5 – 7%. Poor conversion effectively leads to negative revenues as there is always a cost for communicating to prospective clients. Positive net conversions - Net conversion = (Target group conversion % - Control group target conversion %). This number should always be positive in any case. Negative conversion implies that the campaign has not been quite effective and people who got standard offers are in fact responding better. This can also result in customer dissatisfaction as he/she may be getting wrong offers. This could also result in revenue loss due to the added cost of the campaign which was not successful in the first place. Uplift in net customer value – It is critical to compare value generated by the customers with their historic contribution say last month/year. Only if the customer contributes higher to margin and value, he will move up the value chain. Performance improvement expert H. James Harrington once said, “Measurement is the first step that leads to control and, eventually, to improvement.” Measuring the effectiveness of a campaign helps to provide deep insights into it is performance – in terms of reception and conversions. Consistent measurement efforts will help to alert the companies as to which ideas should be replicated and which should be discontinued. It also provides credibility to the companies by demonstrating that digital marketing campaign is both a powerful and worthwhile investment if all its elements are executed well and measured too.

Blog Telecom · May 29, 2017

Can Mobile keep up its promise of delivering zero media wastage!

It is now a fact proven beyond any doubt that mobile advertising is a rapidly growing opportunity for marketers and mobile adtech firms. Mobile screen has become the third largest advertising medium in India and there are enough reports and numbers out there which confirm this going up in the future. It is one medium that follows the consumers wherever they go. Also, it is a platform where an ever—increasing number of consumers are spending their time. There are about 1.1 billion mobile connections in India as of November 2016 and about 968 million active connections as per a Telecom Regulatory Authority of India (TRAI) report. The total unique mobile subscriber base stands at nearly 616 million as of June 2016. This is way ahead in terms of reach to consumers any advertising medium could offer to the marketers. Hence, the share of overall advertising spends being allocated to mobile media is increasing steadily.

Blog Telecom · May 26, 2017

Predictive customer churn modelling in Telecom industry with high accuracy

Customer retention is one of the most common and critical problems in the telecom industry with regard to Customer Relationship Management (CRM). Due to saturated markets and intensive competition, most companies have realised that existing customers are their most valuable asset. They understand that is more beneficial to keep their existing customers satisfied than to keep focusing on acquiring new customers both from effort and cost perspectives. Hence the need to identify customers that are most prone to switching with greater accuracy is of high priority for Telcos world-wide.

Blog Telecom · Apr 24, 2017

The theory of unlimited plans: The rampage for infinity is here

The Telecom industry is at a major cusp of change with telcos making unlimited offerings. In a bid to woo the customer, operators are aggressively pushing attractive data benefits coupled with, needless to say, unlimited voice and SMS! This is a major leap towards customer retention and bringing in temporary customer delight. However, let us address the elephant in the room, is this sustainable? For how long will the customer be hooked to these baits?

Blog Telecom · Mar 30, 2017

Audience knowhow is priceless with Telco Data!

The mobile device has become the most powerful channel for persuasion. Out of all the recent technological advancements, this is one device that has changed the way we live, communicate, buy, read, search, transact and work. Until the start of this decade, mobile was an instrument designed to talk to other people and any other activity that one could do on mobile was incidental. However, with the kind of advancements we have seen in mobile computing, we have perhaps reached a stage today wherein, the amount of time one uses the mobile to talk versus the amount of time that is spent on other activities is extremely skewed.

Blog Telecom · Mar 28, 2017

Using signals of user communication from social media to predict likeliness of code-borrowing and code-mixing

The lush green Indian Institute of Technology Madras (IIT) campus made a picturesque backdrop in the celebratory photos we took at the Conference on Data Sciences (CoDS) Data Challenge. This was the second year in a row that a team from Flytxt came on top at a challenging data science contest. Last year it was KDD cup, this year it is IKDD CoDS, a contest conducted by India chapter of the Association for Computing Machinery's (ACM) Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) along with their annual conference. It was indeed a proud moment with the team securing top position based on model score, more than the joy of winning it the amount of learning one gets with such contests is immense. This year's challenge was from Multilingual Natural Language Processing domain, a prominent field of research within machine learning and artificial intelligence. With the availability of the sheer volume of regional content (especially textual) on social media, entertainment websites and the internet at large, information retrieval community has increasingly recognised the prominence of multilingual NLP research. Code-mixing and code-borrowing are two important linguistic phenomena in Multilingual Natural Language Processing. When words and phrases of one language (say English), is used while communicating in another language (say Hindi), then code mixing is said to occur. This phenomenon is often seen in the communication among bilingual and multilingual speakers. For instance, English-Hindi bilingual speakers often use English words like money,cool etc. in their Hindi conversations, though they are not Hindi words. A similar linguistic phenomenon is called Code-Borrowing, wherein a word or a phrase from a foreign language is used as a part of the native vocabulary of the domain language. For instance, native Hindi speakers might use words such as ‘botal’ and ‘kaptaan’ which are actually borrowed from the English words ‘bottle’ and ‘captain’ respectively. Code-mixing could eventually lead to foreign words getting into the vocabulary of a native speaking community of a different language. For instance, a native Hindi speaker might say ‘match dekhna’ (to see the match) wherein the word ‘match’ is an example of code-borrowing. Identification of Code-borrowing from Code-mixing helps in many aspects of multilingual information retrieval and Natural language processing. For example, if we could distinguish multilingual queries having only borrowed foreign words or phrases, then for processing these queries we need to access only monolingual documents of domain language. This ultimately reduces the computational cost of such queries. The Challenge The ACM IKDD CoDS 2017 Data Challenge was to develop a model that predicts the likeliness of a word to be borrowed from English to Hindi, using various signals of user communication obtained from social media. What makes this problem challenging is that there is no clear signal of code-borrowing, and the borrowing phenomenon evolves over time. Due to this dynamic nature, tracking recent conversations among people is helpful to track the likeliness of words to be borrowed and social media data is the most valuable data source for this purpose. A social dataset consisting of approximately 0.24 million tweets from Twitter was used for estimating the likeliness of words to be borrowed. The words in each tweet were tagged as Hindi, English or Other. Based on the rules defined on word tags present within, every tweet was tagged as an English (EN) tweet, Hindi (HI) tweet or a Code Mixed Hindi (CMH) tweet using heuristics. The key idea is that a word is more likely to be borrowed from English to Hindi, if it is used mostly in Hindi tweets. The rules used for tagging tweets are shown in the table below. Distribution of tweets tagged to each category is shown below. Words related to trending topics are more likely to occur in the tweets. Moreover, trending topics for English and Hindi tweets might be different. The statistics derived from such a dataset is prone to noise, which will affect the performance of the model being developed. To counter this bias towards trending topics, a set of relevant tweets has to be identified. Since tweets are associated with Hashtags, a Hashtag based filter was applied to eliminate irrelevant tweets. The Hashtag filter first identifies relevant Hashtags as the ones which are associated with HI or CMH tweets. During filtering, tweets with these relevant Hashtags are selected, to form a set of relevant tweets. This set of relevant tweets represents an unbiased dataset, from which word statistics are computed for our model. Each tweet from relevant tweets was lowercased and tokenised to words. Tokenised words from a tweet were stemmed to its root form to extract accurate statistics. The statistics extracted for a word reflects the numbers tweets containing the word and users who have used the word in their tweets. These counts are aggregated for EN, HI and CMH tweets. The following statistics are extracted for each word whose likeliness of being code borrowed is to be computed. RT U hi(w) : The number of users who have used the word w in their Hindi tweets. RT U cmh(w) : The number of users who have used the word w in their CMH tweets. RT U en(w) : The number of users who have used the word w in their English tweets. RT T hi(w) : The number of Hindi tweets containing the word w. RT T cmh(w) : The number of CMH tweets containing the word w. RT T en(w) : The number of English tweets containing the word w. The collected statistics were used for training various machine learning models such as ordinal regression, linear regression, nonlinear regression and neural networks. However, due to limited availability of training data, the machine learning models were not able to produce the best results.Hence, a hand-crafted mathematical function using the gathered statistics was formulated, which produced the best result. The hand-crafted function is: Here, RHTUR (w) reflects user level preferences of usage of a word in a particular language whereas RHTTR (w) indicates a global usage preference. Since the number of conversations from users can vary significantly in social media datasets, we factored in both these preferences to estimate the likeliness of an English word being code borrowed to Hindi. A detailed technical treatment is available at our paper and poster respectively: Paper: Code-borrowedness of English words in Hindi language Poster: Code-borrowedness of English words in Hindi language

Blog BFSI · Mar 22, 2017

Analytics a game changer for ATM Banking?

Banks have been on their journey in digitalising their operations and bringing customers to the centre of business activities. They have realised that big data analytics is a key technology enabler to drive their aspirations in both these directions. Analytics can empower channel managers and omni-channel banking executives with the game-changing knowledge of where, when and how customers interact with their bank or financial institution to differentiate their offerings and personalise banking experience.

Blog Telecom · Mar 10, 2017

`Go vernacular’ in promotions: Not for all markets?

According to the latest data released by the Telecom Regulatory Authority of India (TRAI), there are over 968 million mobile phone subscriptions. Indians who list English as their primary, second or third language, as per the Census of 2011, numbers a mere 125 million. This would imply that there is a massive market for local language texting in India. The SMS has been dubbed as `the cheapest, quickest, easiest form of peer-to-peer mobile communication’. It is easy to assume that communication in ‘vernacular languages’ through SMS in India might be a big thing. However, this assumption is far from reality.

Blog Telecom · Feb 23, 2017

Deep learning based on recurrent neural networks for predicting customer churn

Deep learning got popular after it was proven commercially for use cases like speech recognition and image processing. It has its own advantages over traditional machine learning models as it can learn better representations by itself from large raw data set with very little dependency on humans. It has been featuring now in the top 10 technology trends predicted by industry observers like Gartner for last few years. Deep learning is increasingly getting adopted across many industries including telecom.

Blog Telecom · Feb 15, 2017

Beyond customer acquisition: Embrace proactive engagement across customer lifecycle

According to a recent study by Rosetta, a customer engagement focused agency – ‘Engaged customers buy 90% more frequently and spend 60% more per transaction’ Engaged customers are the ideal customers to have, more so in the telecom industry with fierce competitive dynamics emerging across traditional and digital players. To stay competitive and be profitable in the present business scenario, telcos’ need to rethink their customer engagement strategy that enable them to develop and retain customers ‘for life’ rather than merely go on an acquisition rampage. Proactive customer engagement instills a strong connection between a consumer and a brand that is strengthened over time, resulting in mutual value. Telcos across the world have established vast experienced teams tracing panoramic consumer affair and analyse customer behaviour on various parameters in order to plan their campaigns accordingly – feeding personalised offerings to the customers.

Blog Telecom · Jan 30, 2017

Mobile Advertising – The way forward for India

The government’s demonetisation decision, barring 86 per cent of currency in circulation, was undeniably disruptive. The Jan Dhan-Aadhaar-Mobile trinity and now demonetisation are the backbone of far-reaching reforms to improve the socioeconomic condition of the most marginalised sections in our society – the rural citizens.

Blog Telecom · Jan 24, 2017

Innovation will make or break Indian telcos

Telcos in India and around the world are undergoing a major evolution phase with the advent of Fourth generation Long Term Evolution (4G LTE), but is also facing the brunt of dwindling numbers. This is common with almost all the communication service providers (CSP) across the country. While the specific challenges faced by an operator in a region may vary, but they have some common short-term as well as long-term challenges which they have to tackle.

Blog BFSI · Dec 27, 2016

Sentiment analysis: ‘Social’ savvy banks can gauge flip-flops of customer service in quick time

Social media is expected to be a vital cog to the new-age banking with its potential as a media to engage with digital customers at scale as well as its ability to spread sentiment and messages faster. It offers financial institutions an ideal opportunity to share not only promotional offers and information but also address customer issues and concerns before it aggravates further to result in mass distress and churn. Indian consumers are getting increasingly digital savvy with around 90% increase in internet user base and more than 150 million active social media users with majority of them on Facebook. It explains why most of the banks have their own Facebook profiles and use Twitter not only for marketing and customer service, but also to offer new innovative services. The recent ICICI and Twitter tie up - ‘Tweet to Transact’ is an example of many initiatives from banks in going ‘digital’ and ‘social’. Flytxt carried out a sentiment analysis of 4 of the top notch banks in India to analyse how their digital presence and initiatives are shaping up customer opinion and market sentiment. We picked both nationalised as well as private institutions to have a better comparative picture. Data for the past 4 months was extracted from relevant Facebook and Twitter pages maintained by these banks for analysis. The text data (tweets, feeds, comments) was preprocessed to remove unwanted text such as special characters and stop words, followed by tokenisation where a sentence was split into words or tokens. This was followed by the process of lemmatisation where the tokens are converted into groups of different inflected forms of a word, called lemma. After this, a bag-of-words model was created which is essentially a list of all tokens. After this, the sentiment analysis was carried out based on opinion lexicons which are basically sentiment dictionaries containing lists of English positive and negative sentiment words (around 6800 words in total). An intersection of the bag-of-words model with the positive sentiment dictionary and negative sentiment dictionary created the lists of positive sentiment and negative sentiment words, respectively. Top 100 positive and negative sentiments were visualized as positive sentiment and negative sentiment word clouds, respectively. A sentiment score was also calculated based on the number of positive and negative sentiments which determined the overall sentiment polarity levels. Let us have a look at some of the results of sentiment analysis done for each bank: Bank 1: Rewards won followers; but there’s no replacement for good service One of the largest privately owned banks gained a lot of support on social media for its cash back reward schemes. Offers like winning a free Kindle and special ones for women customers earned appreciation on social media. Invoking positive sentiments by spreading awareness of such offers and reward schemes can be one fairly successful social media strategy for banks. However, an analysis of keywords that resulted in negative sentiment clearly reflected the fact that customers are not happy with their grievances redressal system. Instances of passing the buck among customer service executives and relationship managers as well as lack of communication have been major pain points raised by customers. Bank 2: Digital customers applaud and curse in same breath Analysis of social media comments from another large private bank also proved that offers and incentives go a long way in receiving positive vibes and cheers from customers. For example, there were lots of positive comments on offers like free online shopping vouchers on first transaction using internet banking or a free e-book reader with mobile banking services. It is a closed loop with the digital customers appreciating the digital initiatives of banks on digital channels. However, the bank also received quite a bit of negative feedback and sentiment on their services like online payment transfer, credit card services and mobile banking. Many customers cited poor customer service as a reason for their dissatisfaction. The analysis clearly showed that social media is a good platform to gauge positive and negative feedback on services, more so for digital ones. Bank 3: Even failure of one product line can lead to negative sentiment overflow For this Indian private sector bank, assured, efficient delivery of bank products and free reward points proved to be a winning factor to gain trust, acceptance and appreciation from the social media peers. However, loan customers expressed displeasure with loan accounts not being closed on time despite numerous follow-ups by the customers. Lack of communication and ownership by the managers added to the fury. Many criticised the service, calling it ‘worst’ and ‘pathetic’. Bank 4: Loved by the masses; lethargic approach is a dampener This Indian multinational, public sector bank has always been popular among the Indian middle-class community. It has always been associated with trust and security. This image is further exemplified with a promise for a more personal experience. Even the offers have been directed towards appeasing the masses like free bus tickets, which have also have been popular. Despite the popularity, the lethargic and bureaucratic approach associated with a typical public sector organisation has been a dampener even among the social media masses. Little or no vocal communication due to a flaky helpline number was also a deal breaker as was use of archaic vocabulary in the fine print. Conclusion Overall, Bank 2 and Bank 4 had a greater social presence. While the former earned 90968 number of tweets/feeds/comments, the latter too enjoyed nearly as many responses at 90662. Bank 1 and Bank 3, respectively, earned 49635 and 52040 responses on social media. Bank 2 had the highest positive polarity followed Bank 1 and Bank 4. Bank 2 was promoting various interesting offers from the bank through social media which made their sentiment score higher. In a nutshell, incentives and rewards invoked very high positive sentiment. Similarly, good services and quick and active responsiveness to customer problems earned a positive feedback for these banks. Contrariwise, the presence of negative sentiment was around a number issues and complaints pertaining to the customer service. While there is no replacement for good services, working on the negative sentiments could improve the customer experience. This will, in turn, improve the popularity and profit for these banks. Listening to social media enables a deeper understanding of customer’s feedback and emotions for banks and if they can act on time it can help them in not only retaining customers but also enhancing brand loyalty and relationship value.

Blog Telecom · Dec 12, 2016

Man-machine collaboration: Smarter, faster decisioning for maximum impact

Fierce competition in emerging markets has been forcing telcos to reduce their tariffs drastically, resulting in decreased average revenue per user (ARPU). The impact is further exacerbated by the onslaught of OTT players and green-field operators who offer very attractive value-added services to lure customers from incumbent operators. Amidst these unfavorable market headwinds, telcos are in a fix to ensure that subscribers get superior customer experience to maintain brand loyalty. The bright spot for telcos is the access to large amounts of customer data. With the ability to perform real-time and batch data acquisition, cleansing, transformation, analytics, warehousing and reporting techniques on this data, telcos can ensure they deliver on the heightened expectations of customers while meeting their revenue and profit objectives. In current scenario, telcos are in dire need to deploy cost-effective integrated analytics solutions and employ expert analytics personnel to generate actionable intelligence. Human Intelligence: Expert analytics personnel for ingenuity, innovation and insights The scope of actionable intelligence is expanding from historical insights to predictive and prescriptive foresights to enable strategic, operational and tactical decisions making. Marketers, in telecom industry, often have complex and huge amounts of customer data that they need to analyse, to arrive at the right set of insights to take informed decisions. Flytxt’s Revenue planning team works in close association with Flytxt marketing consultants as well as client team to deliver actionable intelligence through daily, weekly and monthly reports on customer behaviour and campaign performance. The revenue planning team enables various stakeholders (Product owners, Marketing Operations, Regional Executives and Corporate Executives) in the organisation to work from a single view of the customer base The team comprises of domain experts with experience of working with global telecom operators in various roles such as Product Management, Marketing, Operations and Analytics. The primary Focus areas of the team is to ensure standard insightful reports are delivered to relevant team members and decision makers. Timing of the reports is extremely critical. There are some reports that set the tone for the entire month, so they have to be shared in first week of the month. Some reports are daily reports that help campaign managers to monitor the health of the business and finally the weekly reports provides trend of movement of key KPIs. The revenue planning team delivers more than 30 unique reports monthly to a Tier 1 Telco in Asia. These reports are the foundation of strategic, operational and tactical decisions that deliver an overall 2-3% impact on revenues. This economic impact is an accumulation of growth in unique subscriber acquisitions across multiple regions. The subscriber acquisition and retention across regions is delivered through a slew of marketing programs which influences key marketing KPIs such as ARPU per subscriber, Data Consumption per subscriber etc. The marketing programs cover the whole spectrum of marketing KPIs to manage the customer lifecycle through cross-sell, upsell and retention campaigns. These insights also enables the product team to come up with new innovative offers in line with the market demand and optimise the revenue streams across product lines. Smarter decisioning: Proactive, Granular and impact-driven business reporting One size does not fit all, when it comes to performance measurement of campaigns. It is extremely critical to select key performance indicators for every campaign and program that gets executed. Upsell campaigns are generally looked from immediate revenue enhancement perspective whereas retention campaign measurement is done for next 2 -3 months to gauge the customer response. Every reports comes with associated recommendation, graphs etc. to help client take smarter decisions. The best practices from across the regions are shared to give clients visibility on what’s working in other regions/ similar markets, to make the most of innovative ideas from other regions. The team also holds regular reviews on weekly and monthly basis with client teams to assess performance of the programs and do iterative optimisations for maximum impact. Exploratory Analytics: One stop shop for actionable intelligence and decision making Flytxt’s big data analytics platform NEON offers – Exploratory Analytics module to obtain deeper insights on consumer behaviour and market segmentation. It is a high-performance analytics module embedded with the Customer value management solution, optimized for handling real-time, petabyte scale analysis of databases at the CDR level. The combination of several advanced analytics techniques—including subscriber profiling, usage analysis, demographics insights, and campaign analysis with automated feedback come together to optimise the marketing campaigns and facilitate the most appropriate form of customer engagement. Exploratory analytics module enables marketers to: Explore Segments: Enables markers to identify subscriber segments and understand their behavior by analyzing the relationships between various performance indicators. Analyse Campaigns: Enables the marketers to analyse the campaign performances that are either currently running or have been previously executed. The module offers the flexibility to generate reports both manually for ad-hoc scenarios and automated ones for pre-configured periods to analyse campaign performances and subscribers behaviour. Influence key business drivers across enterprise with timely and actionable intelligence To adapt to a rapidly changing business scenario, telcos today are refining business models. Key business drivers center on becoming more customer centric, creating the operational synergies to efficiently deliver superior customer experience. It is mandatory for telcos to analyse their customers’ needs and tailor all their business processes in the value chain to effectively meet their customers’ unique requirements and heightened expectations. Implicit in this argument is the assumption that telco have the ability to turn large volumes of data pertaining to their customers and services into actionable insight. Exploratory Analytics module, coupled with the analytical and domain expertise of revenue planning team can significantly help in almost all aspects of the value chain to effectively interact with customers and design suitable offerings.

Blog Enterprise · Nov 29, 2016

US Presidential Election 2016: When sentiments ‘trump’ed trends

The US election results are out and everyone is stunned by Donald Trump’s unexpected victory over Hillary Clinton. We here at Flytxt, carried out an exploratory analysis on the same. The main objective of this analysis was to find answers to a few mind boggling questions. Was there a good enough evidence that Trump would win the election? Did the trend analysis on poll data miss anything in predicting the election outcome? Were the Twitter sentiments better indicators of people supporting Trump despite some controversial remarks made by Trump?

Blog Telecom · Nov 18, 2016

Mobile advertising makeover: 3S framework for success

Advertisers can excel in the mobile advertising execution by taking a cue from Leonardo Da Vinci’s quote - "Simplicity is the ultimate Sophistication". Few general statements we tend to hear from the marketers: "we want to do something very innovative or something nobody has done". All these aspirational goals are a good starting point for leveraging the powers of Mobile advertising, however they just happen to be the means to an elusive end: how can our mobile ad campaign reach customers in a seamless engaging way and drive desired behavior like conversions or downloads or purchase? With cell phone subscriptions outnumbering the world population, Mobile advertising has emerged as a key medium for marketers and business owners. The goals from digital marketing however, still remain the same: attract new customers, retain loyal customers, and spread brand awareness. The fact of the matter is few marketers have managed to get the mix right on mobile medium. The key questions while preparing a mobile advertising campaign for marketers: Do we know our audience and understand their behavioural instincts? Have we used the potential of simplicity of mobile communication channels? Are we planning for mobile separately or is it a part of integrated marketing plan? Advertisers need to plan for the mobile advertising in the same way the traditional advertising gets strategized. The approach to bring mobile advertising under the umbrella of integrated marketing plan is the first step towards maximizing results. The best way to achieve this is by following the three ‘S’ mechanism while devising a mobile advertising plan. Keep it Simple, Scalable and Sustainable. The advertisers may claim - that's how they form any media strategy. But the real difference is in using the same metrics in the digital realm, keeping in mind the powers and constraints of mobile medium. The most common thing in digital advertising is to buy purchase keywords and look for responses. This model despite being simple is not a sustainable one. The entire holy trinity of Simplicity, Scalability and Sustainability needs to be addressed throughout the ad campaign keeping the communication, creative, content and channels synced with each other for maximum impact. Simplicity with creativity Mobile advertising is all about finding ways to reach your customers on the devices they use the most. So the foremost is to ensure the choice of content, language, format and channel is done in the most judicious manner. As people will view ads on a much smaller screen than a desktop monitor, it is essential to confirm the ads are still visible and carry the same impact when viewed on small screens. While video has been widely considered the go to medium in digital advertising, the simple telecom channels such as SMS guarantees an open rate of 90% and Rich content messaging virtually guarantees enhanced customer engagement through multimedia Ad format. mADmart, multi-operator powered mobile advertising marketplace, used the missed call prompt channel to enable the brand maximise the reach to the desired urban and rural population in 3 states and engage with the target audience in their regional language in a non-intrusive and delightful manner. Contextual creative messages pre-recorded in child’s voice for various situations when the number is busy, the number is not reachable and the number is switched off, did the job of attracting full attention of the callers and the short 15 second message ensured complete attention during the interaction. Scalability with precision Mobile is the most personal device customers have. This unique feature of mobile equips marketers with the ability to understand customers truly and communicate with them in a personalised manner. This approach of mass personalisation will ensure brands have the ability to consider demographic, behavioral and psychographic traits cumulatively to stimulate brand awareness and drive engagement at massive scale. The ability to run multi-region campaign with the precision of consumer insights is a unique way to chase exponential outcomes in branding and customer awareness campaigns. mADmart enabled Hiranandani, a Real estate developer in Mumbai, leverage simple SMS channel to reach out to more than 2 lakhs affluent customers across Mumbai region. The SMS message communicated the investment benefits associated with the early investment in the property and included a dedicated number for recipients to give a missed call in order to register their interest. Sustainability with continuous improvement Mobile advertising conventionally gets used in an ad-hoc manner and is not an integral plan of the overall advertising plan. The ability to utilize insights from one campaign’s performance to drive another mobile advertising campaign or feed the inputs to traditional media campaign is something that has not been the norm so far. The sustainability to conduct and expand mobile campaigns ensures the proven practices of the campaign gets replicated across different times and locations. It also helps brands to leverage the interactive capability of mobile to ensure the captive audience obtained through one campaign is led through the different stages of the purchase funnel, in subsequent campaigns. mADmart enabled HDFC LAP (Loan against property) to run a multi-city campaign across India. The campaign started from Thane and spread to Pune, Chandigarh, Surat, Delhi and Kolkata over a period of 3-4 months. The program offered pinpointed reach coupled with location based targeting through pin-codes around industrial zones to ensure relevant communication and reduce wastage. Based on the campaign performance from one region the campaigns’ targeting criteria in other regions were improved i.e. further refinement in targeting conditions from bank customers’ to current account holders, from 25-40 year old males to 30-40 year old males. These targeting condition improvement further improved the response rates and delivered efficient results. The three S come together to deliver the 4th S: Success Mobile being the single best scalable medium aided with a simple communication approach can ensure a sustainable advertising campaign performance. The idea behind this article is not to share a defined structure rather a set of guiding principles that can be used while devising a mobile advertising plan to ensure best results are derived and the campaign’s success gets measured on metrics that matter in the digital world.

Blog Telecom · Nov 15, 2016

Connecting the digital experience dots: It’s all about ‘ME’

Digitalisation of services has driven convergence across industries leading to a very fast rise of the connected, demanding and influential consumer leading to a role reversal. Historically, telcos had full control over the services and the products that customers accessed over the network. The increasing use of over the top applications has turned the tables in favour of the customers who control what service they want, when they want it and how often they use it. Even the most novice users today have a comfort level with their personal mobile devices and use them to execute tasks and make decisions. Mobile computing has led to a world connecting people, devices, organisations and appliances. The ubiquitous mobile phone, with the multiple digital products and services, is a part of a customer’s digital identity.

Blog Enterprise · Nov 4, 2016

Festive season sales in India: Consumer insights beyond the hype, hysteria and hoopla

The top Indian e-commerce brands recently concluded their 5-day online festive season sales bonanza in the 1st week of October. The event witnessed sales soaring to newer heights. Riding on consumers’ festive-season spirits coupled with better monsoon season and improved GDP growth projections, the e-commerce industry has been able to bring onboard numerous new consumers, reach many unserved locations and truly deliver on the explosive growth promises.

Blog Enterprise · Oct 13, 2016

Festival Marketing: When heartshare trumps mindshare

It is Navratri-Dussehra, Diwali, Christmas and New Year that turn in maximum sales for the year for most consumer-oriented companies. People go out of their way to splurge themselves and their loved ones. Be it sweets, clothing, jewellery, silver articles or general goods, most of the consumers are tuned in to the marketplace - mobile, online and in-store. The success mantra for marketers to drive festive sales - understand and sell to consumers emotions, stimulate the infectious festive spirit, choose early on products you wish to promote, stay ahead and advertise early as well as be prepared to go big in offers to have customers spend more with you.

Blog Enterprise · Sep 28, 2016

The ultimate data science challenge- Experience of being a finalist

KDD Cup is a global annual interdisciplinary competition in the fields of Knowledge Discovery and Data Mining. The annual event is organised by ACM SIGKDD (Special Interest Group on Knowledge Discovery and Data Mining - a leading professional organization of data miners) along with the KDD Conference. The KDD CUP is a highly regarded competition in the area of data science. A complex new challenge is offered every year and data scientists from top universities and industry research labs all around the world compete to win the prestigious KDD Cup.

Blog Telecom · Sep 28, 2016

Digital convergence opportunities for telcos: Monetising on the entertainment quotient with IPTV

Telcos across the globe are launching a broad range of digital services to counter the threat of OTT (over-the-top) players’ encroachment upon the traditional telco revenue streams. IPTV (Internet Protocol television) presents a special advantage for telcos to leverage their distinct assets and capabilities. Ubiquitous networks coupled with extensive insights on their millions of customers would enable telcos to deliver personalised and contextual entertainment services. This will not only create new revenue streams but also improve monetisation with the existing ones. Opportunity to take digital strides with IPTV Services The rising trend of digital content consumption on mobile and Smart TV, especially the rich media (in particular, video and TV), continues to be the most important area of focus for telcos globally. This offers telcos with a rare opportunity to own critical pieces of customer experience around digital entertainment by augmenting their existing voice and data services with digital services such as IPTV with relevant content portfolio. However, the challenge for telcos would be to match and exceed expectations already set by digital stalwarts like digital video aggregator platforms (Youtube, Vimeo, etc.) and OTT players (Netflix, Hotstar etc.) by delivering relevant and contextual IPTV content to consumers. The lucrative IPTV market trends for telcos IPTV has been gaining popularity over the traditional TV as a source of entertainment given the flexibility it provides to the viewers and also due to an increase in access to the broadband services in Asia-Pacific market. The global IPTV market is expected to touch $79.3 bn by 2020 with a CAGR of 18.10%. There are different players across geographies offering IPTV services. AT&T U-verse, Verizon FiOS TV, Deutsche Telekom’s T-Home, Belgacom TV, France Telecom’s MaLinge TV, Telecom Italia’s Alice Home TV, British Telecom’s BT Vision and American Movil’s Claro TV etc. It’s not just IPTV it’s MYTV Mobile, or for that matter a tablet, being a more personal device than TV or even PC, allows consumers to expect an ultra-personalised entertainment experience around their habits and lifestyle. The viewing behaviour of the consumers has evolved from watching the original episodes of their favourite series, events or live matches to a time-shifted viewing to a delayed viewing supported by a DVR (digital video recording) backup. In addition, the viewers also collaborate and engage on social media platforms to search for their favourite program, movie or star cast and share their views with other viewers on various elements of programs. While meeting consumer expectation is a tough ask but the flipside for telcos is that once rewarded with a superior experience, consumers not only spend more but also become evangelists and recommend their friends and families. Ride on consumer insights to succeed in IPTV With content being the king in the future of digital entertainment, telcos providing IPTV services, can not only become content providers by entering into exclusive partnerships with the producers, but they can also provide accurate measurement of different programs to the broadcasters to further strengthen their programming strategy. Moreover, there are many core marketing use-cases for telcos to create tangible top-line impact through accelerated IPTV adoption among subscribers because telcos have the ability to be a key player in IPTV services by leveraging the consumer insights that lies beneath the heaps of data that is generated every day. With advanced classification techniques and machine learning algorithms telcos can not only gain insights on product and pricing analytics like bundling and unbundling while designing bouquets of channels but they can also create behavioural segmentation such as movie lovers, VOD (video on demand) enthusiasts, sports lovers, evening watchers, GEC (general entertainment channel) regulars, events trackers, leisure lovers, niche ninjas and so on based on the distinct characteristics that the segment associates with. Comprehensive lifecycle marketing of IPTV services Just like traditional services, a Marketer, running an IPTV service, needs to take a lifecycle value maximisation approach to IPTV services. This mandates analytics-driven personalisation and real-time contextualisation along with the ability to engage with the customer at the right moment. Following are the major categories of the objectives that guide analytics driven roll-out of different offers. Cross-Sell: A marketer planning to cross-sell offer of sports HD pack to capitalise on the forthcoming EPL needs to identify the probable takers. Some of them could be the consumers watching sports genre at least 90 minutes a week and also showing affinity towards HD channels by having HD packs for GEC genre. This along with the AON (Age on Network) of subscribers and their NPS (Net Promoter Score) could help refine the target segment even further. Up-Sell: A marketer intending to upgrade the subscribers with basic packs needs to look into the subscribers with higher viewing time per week coupled with their behaviour which establishes their interest in viewing a diverse set of channels across genres. That will help one to promote extra channels under advanced packs across different genres to capture their interests and increase the offer uptake rate. Retention: A marketer can spot at-risk viewers by observing a drop in the overall viewership time or inactive long timers to send revival offers. An early warning system can detect drop from one category or one channel as a signal to eventual churn. Also, the original vs. repeat telecast viewers, or late payers will need to be identified to send relevant reminders to increase engagement as a part of retention. Machine learning algorithm will also help classify the at-risk viewers of niche programs to send retention offers. Personalisation: By carefully mining the viewing behaviour of the viewers, the marketer can send personalised offers, be it recommending the VOD action movie by spotting affinity towards action movies or recommending a GEC serial or another channel matching the viewership pattern. Also, it would be pretty useful for the new channel and new program launches to find relevant viewers that can be recommended to the broadcasters. Advertising: For the marketer, advertising and revenue management brings a plethora of use cases to deal with. Right from the program and channel performance analytics to assessing the potential of floating an AFP (Advertiser Funded Program) with the advertiser for the niche target segment (e.g. 35-45 years old, working female for an FMCG product). It also provides an opportunity to look in to the niche programs with lesser but loyal HVC (high value customer) viewership to set the premium. Forecasting of demand and optimisation of revenue become the key areas of analytics here. Packaged analytics: enabler for telcos to maximise value from IPTV Business use cases falling under each of the above objectives associate with different behavioural segments that demand the need for targeting conditions that can be fed in to the analytics engine, where different analytics techniques need to be applied – Prescriptive, Predictive, Descriptive, Exploratory and Heuristic analytics. Flytxt analytics engine in the NEON platform empowers the telcos to apply these techniques using packaged analytics. In the next articles, we will have a closer look at these techniques to see how relevant they become to solve the business problems belonging to aforementioned objectives that help telco monetise on the overall Entertainment Quotient of the viewers.

Blog · Sep 20, 2016

India at Rio Olympics 2016: The winners are not always the most loved ones; the social media analysis says so!

Post the mega event Rio Olympics 2016, Flytxt data science team did an analysis using Facebook and Twitter pages specific to India at the Rio Olympics 2016. The attempt was to find answers to some of the following questions. Who among the Indian athletes hit the popularity chart? Whose popularity took a dip? What was the sentiment among the Indian junta? Which sporting events gained maximum attention? Analysis was carried out with data collected before the event (5th August) and data after the games (21st August).This was followed by filtering and tokenization. The process includes sentences being converted to words and removal of irrelevant stop words. The number of occurrences was the criteria to find the popular item (such as sportsperson or event) in the games. The sentiment analysis comprised use of a dictionary of over 4000 words that included positive and negative terms. In this article, top 100 items have been visualized as word clouds. Dipa Karmakar outscored even the medal winner in terms of popularity Before the event, P.V Sindhu was ranked low in terms of expectations behind Saina. Sakshi was not even mentioned as a medal aspirant. Shooters as expected were the most expected ones to win medal. However post event the popularity chart totally swung, popularity of medal winners as expected soared, but the one who surprised everyone and became an overnight star is young gymnast Dipa Karmakar. Most mentioned female athletes: Badminton Player PV Sindhu Wrestler Sakshi Malik Gymnast Dipa Karmakar Badminton Player Saina Nehwal Archer Deepika Kumari Tennis Player Sania Mirza Wrestler Vinesh Phogat Most mentioned male athletes: Shooter Jitu Rai Archer Atanu Das Boxer Vikas Krishan Yadav Wrestler Narsingh Pancham Yadav Boxer Manoj Kumar Shooter Gagan Narang Badminton Player Srikanth Kidambi Most mentioned Indian athletes post Olympics Female athletes earned an overall more positive sentiment compared to male athletes Most positive sentiment in tweets prevailed for PV Sindhu (27.5 %), Dipa Karmakar (17.5 %) and Sakshi Malik (12.5 %) followed by Saina Nehwal (6.3 %), Atanu Das (6 %) and Jitu Rai (5.5 %). Words that typically invoked positive sentiment included proud, congratulation, champion, support and great By looking at the results of the analysis, the Indians associated following adjectives to athletes. The spectacular PV Sindhu The brilliant Sakshi Malik The talented Dipa Karmakar The amazing Saina Nehwal The fabulous Atanu Das Though a medal was won in wrestling, it is yet to catch fire among Indians Hockey kept its popularity intact with Indian team giving a much improved performance, even beating the ultimate gold medal winners, Argentina. Though badminton surged ahead of Tennis and shooting, wrestling is yet to move ahead in popularity chart.

Blog Telecom · Aug 24, 2016

Population estimation gets more dynamic, granular with telco data

Insights on spatial variations in distribution and composition of population have been the key inputs for governments and enterprises in urban infrastructure planning, retail store-location optimization, city pockets development and city outgrowths analysis, transport system planning, disaster relief and more. Traditionally, there has been a high dependency on census data and satellite imagery for understanding population distribution in a region. Population data is more static with incumbent methods Despite the broad relevance and importance of census data, the ability to derive granular insights in a timely and accurate manner remains a challenge because the census data does not capture the population dynamics as functions of space and time. From a temporal perspective, the census activity is carried out periodically with 1 to 10 year cycles. From a spatial perspective the census data at best represents a nighttime residential population. The traditional methods of population measurement like physical surveys and research works are done through foot-on-street approach. However, these surveys suffer from scaling issues since it is practically impossible to cover every nook and corner of a region and the costs of doing so are enormous. It deters research companies to undertake such surveys on an extensive scale, without compromising on their ROIs. Thus, some of the enterprises conduct surveys on a smaller sample base to get the time-based population estimates and then extrapolate the population dynamics for the whole region. Hence the conventional methods of mapping population with location still suffers from a time lag and offers limited insights on the movement patterns. Consequently, spatially detailed changes across period of days, weeks, months, or even year to year, are difficult to measure These limitations necessitate the development of alternative or complementary approaches to population estimation. Opportunity for dynamic population estimation with telco data An instant view of population in specific locations is useful in scenarios that require a time-sensitive understanding of population density and movement like traffic control. The ubiquitous nature of mobile phone and its increased penetration creates an unprecedented opportunity for gaining such real-time temporal insights on population. Telco data can give better real-time measure of population in a location. Moreover, telco data is also useful in having more accurate view of movement patterns of population mapped to tower switching. If telco data can be combined with other third party and public data, we can potentially have a more dynamic and accurate view of population distribution. Not just estimate it, but self-serve it too Flytxt brings its latest packaged analytics model Population Density Measurement, powered with smart visualisations, which can efficiently measure the population density of a city on a granular level. The model leads to both quantification and qualification of population up to highly granular levels of 1 square Km. The model uses telecom subscribers’ latch information to come up with an estimate of subscribers’ count for each 1 km2 grid area within the city. In order to perform this analysis, the signal coverage area of each Base Transceiver Station (BTS) within a city was approximated using Voronoi polygons, which takes into account the spatial arrangements of these BTSs’. The city was then divided into a set of 1 km2 grids and each grid spans across multiple Voronoi polygons. Subscriber density per grid was then estimated using the subscribers’ latch count corresponding to each of the Voronoi polygons, and a suitably defined transfer function. Information obtained using a single mobile operator’s data can be further extrapolated appropriately to obtain the grid wise distribution of population density and the associated profiling for the entire city. Population Density Measurement model captures the temporal change in the distribution of population density on a daily, weekly, monthly basis providing interesting insights related to subscribers’ movement patterns over time. The following population density maps are generated using this model by taking into account subscribers’ latch data from a renowned Asian Operator for a time period of 1 week. Going beyond population density measurement Telco data adds another unique dimension – along with its capability to track population density and movement, it can also give intelligence on the demographic, psychographic and socio-economic profile of the population in a specific region at a specific time. Telcos’ large variety and volume of subscriber data can provide a large amount of diverse insights about the behavior and persona of every subscriber. These insights, when aggregated, gives an abstracted non-PII view of the population across various attributes like device demographics (age, gender), location, psychographic interests and consumption behavior. Combining spatial and temporal distribution of population along with these behavioral insights can unlock more opportunities to derive value for government and enterprises. It could help in more accurate footfall analysis for recommending ideal store locations to retailers or help brands to promote local events and do test campaigns with niche population base. This article was originally published in Analytics India Magazine

Blog Digital · Aug 12, 2016

Pokémon Go: A blockbuster in the making for retailers

The Nintendo’s Pokémon Go, a free augmented reality (AR) based mobile game is on fire. Its popularity has already added billions to Nintendo’s market capitalization. The installations of the game even surpassed Tinder, the popular dating app. There have also been records of the users spending a lot more time on the game than all popular social media apps including Twitter and Facebook. In US around 25 million users played this game at its peak on a day. Attracting gaming and technology enthusiasts alike Pokémon GO’s instant popularity has been a result of nostalgia for the classic 20-year-old cartoon franchise. Since its inception in 1996, the Pokémon brand has enjoyed strong recognition through video games, a card game, an anime series and other media. In addition, because of its unique game mechanics, Pokémon Go is also attracting users who have never played a Pokémon game but those who are keen to try out new technologies. Augmented Reality (AR) technology is not an unexplored territory. Automobile players like Ford and Audi has allowed users to preview cars with AR apps. Retailers like House of Fraser use AR technology to let consumers scan shoppable windows. Pokémon Go, however, has finally made it an everyday thing and even provides new opportunities for local businesses to capture new customers and bring existing customers back through their doors. Lure the ‘hunters’ Leave aside the resistance of some retailers to those who visit shops to hunt the virtual monsters. Many shops are attracting the ‘hunters’ by advertising themselves as ‘Poke Stops’, where they can grab new Pokémon balls and up their levels. Local businesses can more aggressively court players by activating a “lure module” feature that attracts virtual Pokémon characters to the store, thereby tempting in nearby players. It is only a matter of time that even the bigger retailers would follow suit. The lure for the hunters combined by Pokémon GO-specific messaging and promotions and mobile wallet offers that players download as they pass by a business while searching for Pokémon could be a new lease of life for bigger brick and mortar stores. Traditional retailers have already been in a tough position with ecommerce giants eating away at their customer base. These traditional stores have long been deploying the likes of Foursquare, Groupon and LivingSocial to drive traffic by tying promotions to check-ins, using geo-location to provide relevant coupons. Turn ‘hunt’ to ‘conversions’ However, Pokémon Go did what the likes of Foursquare have been trying to do for years. There hasn’t been another geo-location social platform that can lure so many people all at once! This stems from the sheer popularity of Pokémon franchise because of which the location-based game has captured pop culture’s attention. More importantly, it fuses the real world with the virtual world – a la Foursquare’s check-ins. It drives people into new locations and provides an opportunity for retailers to do marketing which is more ambient and goes beyond just the digital. For example, they can sponsor a game location or simply put a sign outside the store window advertising it as Pokémon friendly. They can host a hunt in their brick-and-mortar space and even put phone charging points (The game uses GPS which drains a lot of juice!). All of this will only build goodwill with the players, but keep them in the store for a longer period. In other words, keep customers in and happy to increase possibilities of sales conversion. With all the things that are being done and can be done, retailers should remember not to shoot in the dark. While it is a good idea to lure the hunters, a smarter one would be to reach out to the right hunters in the right location too. The idea is to harness the power of reach and relevance to a creative geo-location platform. This can be done with partners who have access to Telco’s reach and who have the right intelligence to build demographic and behavioral data of the likely footfall in the location. The retailers can then look to even combine their Pokémon offers with their own product offers befitting customer’s persona and needs. Pokémon GO may only be a tipping point of the icebergWhile Pokémon GO is the current favorite, a few sceptics might dismiss it as a passing fad. A more sustainable idea would be to create a Pokémon-style themed ecosystem where retailers can explore multiple channels – images, videos, voice which would open door to a plethora of opportunities to engage with customers. Customers on the other hand can have fun and value in doing so. Monetisation options are most likely to be offered within the platform for retailers and advertising partners to make use of in the near future. The game based on capturing virtual animals has already captured the attention of millions of users all over the world. It is time for retailers to realise that they gotcha catch em all!

Blog Telecom · Aug 5, 2016

Digital is not a destination, but a journey in Telecom!

The Digital Service Provider or DSP is a concept that has been bandied around the Telecoms world for a long time.  Some call it an evolution.  Others call it a revolution.  Still, others just scoff at just another Telco acronym. For us, the transformation from a Communications Service Provider (CSP) to a DSP is very much an evolutionary fork in the road. It is not a path that all CSPs will choose to take, nor should it necessarily be so. Some CSPs may weather the storm for many years to come with their current approach to networks and subscribers. However, those that ignore the fundamental changes in the service expected from them, will eventually be swept away. What has changed and why has it happened? The change has actually occurred in places where most CSPs were not particularly focused. It was initiated and is being led by the customers themselves. The very definition of customer experience, customer service and customer expectations have been irrevocably altered by the likes of Google, Amazon, Netflix, Alibaba, Uber and AirBnB, to name just a few. Online experiences and the level of engagement with service providers and their customers have been transformed. Service Providers that still just count subscribers are oblivious to, or simply in denial of this seismic shift. The customer is in the driver's seat, well-armed with almost infinite knowledge, tools and choice. Customers are not just subscribers, customers need to be served based on their personal needs with products and services customised on an individual level. Digital is a mindset and transformation is a continuous journey DSPs must understand that customers are now empowered and digitally savvy, often more so than operators themselves. DSPs need to adopt digital as a mindset, a cultural shift that will see every employee suffused with the understanding of how "Digital" affects their own specific role – from the CEO to the CSR, from Marketing to Engineering. Management must be committed, but rank and file must be sworn into the brotherhood. In the end, Digital is not a destination, but a journey. The digital journey is one that comprise of stages, agile cycles of improvement and execution and realisation. If DSPs need to learn anything from the online internet giants, it’s that agility is crucial at Internet speed. This is something that most operators have struggled with, but they can no longer afford to ignore it. In the Philippines Globe and Smart understand the needs and wants of their customers even before they do. Their approach of offering Digital Lifestyle packages with free data for Navigation or Travelling or Fitness and then offering them as opt-in services to choose on a mobile app is an example of digital telco coming into play. Customer engagement is critical Omni-channel digital engagement is the crux of the transformation from CSP to DSP. Thanks to their own experiences, customers have changed their expectations. They demand more information at the tip of their fingers and the ability to choose the services independently without having to call customer service or stop by a service center. They want digital online interfaces and preferable on their mobile phone. Customers expect service and engagement to be on their terms; in short, personalised! A great example of success in this area comes from the UK MVNO Giffgaff. Giffgaff is a low-cost, no-frills, Millennial-friendly Telefonica-owned MVNO that provides support exclusively through its customer community. Customers literally answer each other's questions in online forums and get rewarded for assisting fellow customers with "payback points", which are exchanged twice a year for cash via PayPal or account credit. Giffgaff has no customer service phone lines. T-Mobile USA has taken a very colorful and aggressive approach to engaging with customers through its "Uncarrier campaign". The campaign has delivered sweeping initiatives such as tearing up long-term contracts, simplifying mobile costs, embracing social media and zero-rating music and video to name a few. T-Mobile's understanding of customer engagement and its brilliant Uncarrier execution have turned a lingering, acquisition target into the third largest mobile operator in the US and its fastest growing operator. Mobile and web applications enhance engagement with customers and provide quick access to information and services. DSPs utilise apps to further empower savvy digital customers by providing them with a personalised, self-service tool. These tools enable access to transparent data on services, usage and bills, and the apps give customers the power to browse, purchase and often self-provision new services and products. Analytics is the engine room Analytics and data abound throughout telecom operators' backend and indeed customer-facing systems. The successful DSP will know intrinsically how to collect that data, aggregate it and how to refine it into insights that can be actioned upon in real-time. DSPs leverage big data analytics to continuously expand their knowledge of and responsiveness to customers. DSPs are constantly looking to improve their service, products and engagement based on interactions with customers and on tracking their behavior. Telefonica has embraced the digital telco revolution from the direction of Big Data analytics. They use the data they collect on their network to better service their customers, they are helping others give better services to their customers based on their data from their network. Data is being used for Personalisation, analysing data, selling data and finding insights from data. Digital transformation goes way beyond engagement Some people make the mistake of thinking digital transformation is just an exercise in improving customer experience. Nothing could be further from the truth. Of course, digital transformation is focused on placing the customer at the center of the DSP business model, but it does not stop there. By undertaking such a transformation, a DSP crosses a chasm that lead to a wealth of new opportunities for new services and revenues. The impact of digital is something that disrupts every facet of the telco, from sales and marketing to Networks, IT and beyond. To successfully bridge the chasm, there can be no weak links in the chain. AT&T's approach to IoT (Internet of Things) is a poster-child for telecoms digital transformation. AT&T recognised very early on that the IoT opportunity could be a game changer. While many operators view IoT as a revenue opportunity, mostly to sell more data and connections, AT&T went way beyond this approach to create an entire IoT ecosystem. The end result is they provide much more than simple connectivity and consequently sit much higher up in the value chain. AT&T has developed IoT-as-a service. This has enabled the company to partner directly with developers. Their platform offers developers critical servicing, including customisable schemas, real-time data schemes, triggers and notifications, and a very supportive community. By taking the digital approach to IoT, AT&T has not only opened up new business opportunities and revenues, it has created and engaged with a very loyal group of customers that did not exist before. The enterprise Opportunity Another potentially lucrative opportunity for DSPs is the enterprise. Some DSPs have managed to successfully use their digital approach to create new service offerings to enterprise. A great example is Deutsche Telekom in Germany that is leading their transformation by becoming the ultimate connection to the CLOUD. Cloud applications, cloud services, cloud storage, cloud security. DT have identified the transformation in IT technology and have built their strategy to connect to the cloud and offer cloud services and apps. They have expanded their role from CSP to include Cloud IT. Another DSP leader is Telefónica's Smart Steps service. Smart Steps provides insights based on the behavior of crowds to help companies make informed business decisions. The service was launched in October 2012 as the first product of Telefónica Dynamic Insights. Telefónica packaged its analytics and data science capabilities into a service offering to address the needs of certain enterprise verticals, such as transport. The value is in assisting enterprise to gain an actionable understanding of their customers' journey. The opportunity to translate a DSPs assets and capabilities into new enterprise offerings is literally staggering. However, the opportunity stresses the need to adopt the digital mindset. DSPs need to be very focused on a multi-sided business model so that they will see their internal digital systems partially opened up and sold as part of a service to enterprise customers. Data insights, infrastructure management, identity management and payment services are just some examples of DSPs’ capabilities and process that can be packaged as enterprise services. Many DSPs have also successfully developed enterprise service offerings within vertical markets such as health, transport or finance. Telstra, the Australian incumbent, has plowed resources into the lucrative e-health opportunity. Telstra offers a range of services to connect and serve patients, healthcare workers, hospitals, pharmacies, government agencies and health funds to build a safer, more convenient way of managing health. The upshot is that it has created new revenue opportunities and a bigger, more loyal customer base. Think beyond the horizon Just as customers have learned from their experiences in other fields, so must DSPs. Service providers should not limit their digital focus to customer engagement. While customer experience and engagement are extremely important they are by no means the end of the digital transformation nor do they solely represent the sum-total of the lucrative opportunities that await beyond the horizon. But be warned, Digital Transformation is not a greeting card slogan or a 140-character tweet. For DSPs to derive real value from the digital evolution, they must wholeheartedly embrace digital, both within their systems and most importantly in their psyche too! This article was originally published in Flytxt print magazine Insightz

Blog Digital Telecom · Jul 27, 2016

Adaptive customer engagement with mobile Apps: Think contextual, act personal

Digital transformation imperatives for telcos amidst intensified customer expectations coupled with shrinking revenue streams are accelerating the move from traditional agent-assisted channels to less-expensive, more intuitive machine-assisted channels. Adaptive, personalised, and contextual customer engagement approach is must for telcos to fend-off digital-native competitors and drive long-term profitable relationships and customer loyalty. These adaptive customer engagements must be seamless and fluid from customers’ perspective yet intentional and valuable from business perspective. Simply put: App-based customer engagement is slated to become the next battlefield to decide winners or losers in the digital economy. R.I.P. Customisation, Long Live Personalisation  “If a user doesn’t return to your App after one week, there is a 60% chance they never will. On the flip side, loyal customers make 25% more in-App purchases than occasional users.” – Localytics When it comes to attracting customer’s attention on proprietary Mobile Apps, Telcos are up against numerous other Apps which have been at the helm of delivering extreme personalisation and superior experience. Today, most technology companies are focused on giving users the flexibility to opt for content and App themes tailored to their interests and needs. That’s customisation and not enough for consumers who are living in the luxury of everything going digital. It takes a while and requires some effort on customers’ part to get it right on a customised app. The law of diminishing returns kicks in – causing confusion and dissonance in customers who are required to ask for what they need in order to get relevant experience. Personalisation on the other hand is when an App adapts itself based on the historic knowledge about the customers, without them having to put much effort at all- it just happens in few clicks. “If I had asked people what they wanted, they would have said faster horses.”      – Henry Ford In this digital world where so much data gets captured across customer journeys (discovery, purchase, usage, upgrade) and user interactions (swipes, taps, pinches), consumers don’t just expect digital enterprises to deliver on what they want. In fact the need of the hour is to go beyond and deliver a personalised experience proactively. Personalisation: The proverbial fulcrum of customer engagement Today telcos understands that in order to convert app downloads to loyal users, they must invest in building and nurturing a deeper relationship with prospective and existing users. This mobile-centric approach has taken many forms, at the heart of which is the idea of personalisation. It is when consumers feel that their experience has been refined for their benefit, they feel like engaging more. Content personalisation: Spearhead the data-driven experiences Consumers expect nothing less than the most relevant content on every touch point, more so on mobile app. This can only be achieved by a powerful big data analytics engine equipped with purpose-built analytics models that mines data from various internal and external sources. The content that is shown to customers can be based on their historical preferences, contextual needs as well as their social circle behavior. This will ensure that the most useful content is made available to consumers without him going through a time consuming discovery process. By delivering on these personalised, data-driven experiences, customers tend to repay the enterprises not just in revenue, but also in advocacy. App personalisation: Make the first impression a lasting one Ease of use is critical for the adoption of self-service mobile Apps by subscribers. A device-optimised interface is a must to ensure that customers have hassle-free access to self-service regardless of their access device. App personalisation needs to be embedded as a part of App design in such a way that the App is able to automatically adapt the self-service features to different devices depending on their capabilities and the desired level of richness and interactivity. Another recent and under-utilised development in the App world is the advent in both iOS and Android of ‘deep linking’. Rather than taking a user to the App home screen - or even worse, to the App store to download it - they can be taken directly to a specific location within an App thus delivering contextual experience. Telcos need to have a big data analytic platform in the back-end with automation and machine-learning capabilities to ensure personalised holistic experience on mobile app. Omni-channel Personalisation: Connect the cross-channel dots In the digital era, the trend of the “Internet of me” is likely to extend to the “omnichannel experience of me” as customers will expect relevant, personalised and interactive engagements whenever and wherever they want them. Self-care has already evolved into self service, which includes customers researching and buying new products and services themselves. And while they are doing so they tend to ‘channel hop’ during the discovery, decision-making and buying processes. In order to maintain engagement, Apps need to provide more functionality and better personalisation based on behavioural signals from other channels, such as Web, in-store and in-App data. Analytics-driven systems with automation and machine-learning capabilities offer telcos the ability to track and respond to subscribers in an Omni-channel environment, while empowering them to take personalised next-best-action. Self-care Apps have come a long way, but the holy-grail is further ahead In a digital landscape where no single customer’s journey is identical, it is essential to develop, orchestrate and optimise an ever-agile, flexible and nimble customer engagement strategy. A self-care App is a powerful mode to deliver personalised mobile experience.  A highly relevant experience together with serving the content and functionality needs of the consumer has the potential to satisfy the underlying emotional needs to trigger continuous engagement, brand loyalty and profitable relationship.

Blog Telecom · Jun 28, 2016

Net Neutrality: Save or stifle the internet

The net neutrality debate seems to continue for the foreseeable future, in the form of zero rating, free basics, tiered access, sponsored content, differential pricing and so on, with much at stake socially, commercially and politically. Prima-facie the arguments favouring net neutrality appear to be intuitive, convincing and definitive enough. However the practical implications on emerging business-models and consumer-access are far from being universally clear. Internet evolution at crossroads: The trinity collides The future of internet can best be described by borrowing the concept of “The impossible trinity” from international economics. Three fundamental forces: Consumers quest for freedom, enterprise drive for profits and regulators need for control are at play in shaping the future of internet. Three primary scenarios result from the interplay of these powerful forces. Internet evolution at crossroads Darwinian Evolution: Enterprise drive for profit is the dominant force while consumers’ freedom acts as a key market force to make the natural selection resulting into the survival of best business models. Uber’s aggressive yet customer-centric business model has led to the Darwinian evolutionary course for transportation industry. Egalitarian Destiny: Consumers freedom overwhelms the other two forces and independent and individual choices take priority over regulation and drives enterprise business models. The accelerated surge of social media epitomises the benefits of consumers being in charge. Forbearance Prevails: When regulators significantly alter the course of evolution of an industry through significant oversight resulting into policies which hinders some of the business models being undertaken by enterprises. Telecom industry has historically been subjected to strict regulations and the trend seems to continue with the net neutrality laws being enforced only on the network providers and not on any other players in the digital ecosystem. Net Neutrality rules: Upholding digital ethics in digital economy The three big rules of upholding net neutrality are no blocking, no throttling, and no favouritism based on content, websites, applications or platforms. These rules are based on the premise that tampering with user’s uninhibited access to the internet will pose a threat to healthy competition and prevent new businesses from getting a fair chance of survival in the market. The other argument touts that a non-neutral internet will restrain people’s freedom on what they want to view or have access to, within the country’s legal boundaries. The future state: Breathing life into scenarios While these arguments are perfectly valid, we tend to overlook the impact these net neutrality rules may have on the future state of the internet. These scenarios have existed and will continue to exist even if net neutrality, in its true sense, gets enforced universally. The act of sponsoring specific content, providing free but selected content, allowing certain services or content a faster access is, in effect, an attempt at promoting certain brands while undermining others which is increasingly moving to the digital space. A closer look at few other industries reveals that such practices are rampant all across. The app-based cab-aggregator services and the rapidly mushrooming e-commerce players offer heavy discounts to acquire customers. There are toll free numbers that gives consumers a free but limited access to use selected services. Free samples are given out by players to induce trials and buying a particular product results into cashbacks. These are nothing but sponsorships in guise. Partner-driven business models: Sustaining and leveraging digital leadership The device and software industry presents another case in point. Microsoft extends the reach of Skype, and sky drive, by bundling it with its OS; Apple does the same for iCloud when it bundles it with Apple devices; all android based mobile OS come with pre-installed Google Map app and more. The practice of bundling and promoting another offering with a popular product has, in fact, been perfected by Microsoft, Apple and Google. Bundling is not just limited to a company’s own products either; Google partners with device manufacturers for using android as OS, which has resulted in android dominance in the mobile device market; Google and Apple control which apps will be available through their OS by mandating payment through app stores; Google constantly gets into law suits with companies who suffer lower ranking due to change in search algorithms and the list just goes on. Samsung Galaxy tabs open door to the exciting world of free eBooks with partnership with Amazon and offers Kindle as the default application for reading eBooks on all Samsung Galaxy Tab models. Case for sponsored access: Connect business value and consumer value It becomes very clear from these examples that players with deep pockets and better reach will always have an upper-hand, with or without net neutrality. Having said that the one with the right innovation and customer-centricity will win the race. Services like zero rating, free-basics or sponsored content are just innovative marketing techniques. The 'service' in this context refers to the units of data or bits that a person consumes for internet access. The value per bit for different services accessed through internet is inherently different for consumers, service providers and network providers. The flexibility to be able to differentially price data services in partnership with service providers will also enable telcos to launch affordable customer-centric data packs as suited to the requirements of different categories of users and hence foster growth in the usage of internet services. It’s possible that certain segment of consumers may not afford using mobile internet in general but would love to use utility apps like cab booking. If a cab-aggregator player is willing to sponsor mobile data along with their app, it only stands to benefit these consumers. This is analogous to the practice of delivering a product to customers’ doorstep instead of asking them to go to the market at their own expense for purchase. Forcing a one-size-fits-all solution on the internet stifles innovation by blocking enterprises to experiment and turn new ideas or business models into successful products. A balanced approach can be taken to quickly enable access to first-time internet users without violating the differential pricing laws by offering customers tariff differentiation that are entirely independent of content. This can be achieved by providing sponsored access to data that enables a user to access the entire internet in return to using an App or watching an advertisement. Widening digital divide: Elope with the internet When it comes to end consumers, the idea of having free access to everything on the web does have considerable surface appeal but, rhetorically unappealing as it may be, this debate is not entirely black and white. We forget that the voices defending this cause are people who already have the privilege of access to internet. In other words, they can afford to pay the basic cost of availing an internet connection. In emerging countries, where a vast majority of the population lives in rural hinterland with minimal or no access to education infrastructure, mobile internet penetration has the potential to bring positive change in people’s lives and bridge the digital divide. In addition there's a growing disparity between the bandwidth needs of casual users for basic Internet use (Web browsing, Social media, IM apps) and the bandwidth needs of advanced users for advanced usage (such as video content, large file downloads, file sharing and application streaming). Unlike in western countries where telcos are able to charge exorbitant prices for voice and data telcos in emerging markets are not able to invest enough in maintaining Quality of Service. The advent of OTT services are shrinking the margins for these telcos even further in emerging markets. In this situation, strict Net Neutrality will force telcos to raise the cost of data, which will restrict digital access only to a segment of consumers who can afford it. This will ultimately result in further widening of digital divide. A tiered-lane access may, in practice, be an innovative solution to this problem. One lane could be allotted for existing and potential customers who would like to get neutral access to content on the web by paying for it. The other lane could be sponsored or zero rated, selective access lane that allows non- consumers to become consumers. This will enable a world where those who can’t afford internet have access to information like Wikipedia, education sites, medical services, agricultural information, and weather forecasts and potentially all relevant internet services. Maintaining a level playing field: 90% of iceberg is underwater As far as free and fair competition is concerned, the existing system is actually skewed against telcos who have to comply with a series of regulations and pay taxes while many OTT players undercut their business, evade all such obligations and reap huge profits. Under the current regulatory framework OTT players are completely unregulated, unmonitored and inadequately taxed. Telcos, on the other hand, pay spectrum charges, licensing fee, interception charges and have many other obligations related to cost sharing and maintenance of proper records, security, legal intercepts, and emergency services and so on. The OTT players and internet companies are completely exempt from such scrutiny owing to their digital nature and sometimes due to the lack of physical presence in the country. A case in point is Netflix, the proponent of Net Neutrality, admitted to sending lower quality video to mobile subscribers on AT&T and Verizon’s networks, albeit in the name of maintaining data cap restrictions of the network providers. In most cases OTT players are not even subject to consumer protection laws. Often, the internet companies start off by offering free services and when consumers are hooked-on they start charging the customers without double confirmation which telcos are subjected to. In past Italy has been investigating such cases against Google, Apple, Amazon and a game company named Gameloft. A universal net-neutrality framework that over-regulates some players in the market while others are left completely untouched goes against the whole argument of maintaining a level-playing field for all players which the net neutrality strives for. Also the option for all players, irrespective of size and reach, to sponsor the bandwidth at the same rate will herald the level playing field in true sense. A hyper local start-up, just trying to get adoption among targeted local-residents, can take advantage of this to promote services bundled with sponsored data access to induce trials. Let Darwinian evolution take course: Hands off the net Many of the key questions in the context of net-neutrality lack right or wrong answers but rather call for difficult trade-offs. As long as the antitrust laws are in place and consumers have the flexibility to choose among methods and providers for Internet access, consumers can pick winners and losers in the digital economy. The table below summarises some of the contrarian views and alternatives available to keep the net neutral without launching the net-neutrality laws in current form. Moreover there is a need to ensure that mobile internet or services available on internet does not remain a privilege of certain classes and widens the digital divide by alienating the under-served. The proposal by Nanadan Nilekani to offer free internet in India through Direct Benefit Transfer scheme has great potential in achieving this goal. Though, if this initiative fails to take off immediately, private players should be allowed to participate in expanding the digital reach even if it is guided to some extent by their own interests. Users who learn of the benefits of the internet would then advance to the paid version of the "full" internet. The government, however, can ensure that all players get equal opportunity to participate by routing them through Telcos, who are already adequately regulated. Also if consumers really want the Net to remain Neutral, I believe it will always be available like today. The approach to straightaway block the innovative business models that allow sponsored internet access in the name of saving internet with an all-encompassing Net neutrality law may inadvertently stifle the course of digital evolution. Unleashing more investment and competition, not writing more regulation, is the best way to keep the Internet open, innovative and free.

Blog Telecom · Jun 27, 2016

Packaged analytics: Accelerate time to value

As the age of digital economy dawns upon us, the business growth of enterprises will depend upon their ability to rapidly define, transform or realign their business models as per the evolving market dynamics. Modern day enterprises, in their quest to obtain a customer-centric perspective of their businesses, are incessantly amassing vast quantities of heterogeneous data from an increasing array of data sources from both within and outside the organization. However, as the Gartner Research submits, the ability to turn this massive data into business value remains a huge challenge. Only the agile will survive and thrive in digital economy Conventionally, business leaders have been accountable to seek new revenue streams and protect existing ones using data and analytics, while the IT leaders shoulder the weight of the technical development, deployment and delivery of analytical models. There is lag spanning several weeks between opportunity identification by the business team to the delivery of use-case specific analytics model by the IT team resulting into either a missed opportunity or a sub-optimal result. Time to value through conventional and packaged analytics approachThis is where packaged analytics as a concept becomes relevant. Enterprises need to take a value-centric approach to analytics. Analytical models have higher inherent value, if they can be readily deployed in a matter of days and can allow business users to carry out further business specific refinements quickly, before being rolled out for commercial use. Packaged analytics is all about ensuring availability of such ‘best fit’ and ‘high performance’ analytical models for enterprises in different business contexts.Innovate early and innovate often With packaged analytics, business leaders can aspire to be among the elite 10% who can quantify and unlock the economic value from data by launching innovative use-cases at the right time. It will also allow enterprises to deploy advanced analytics on a small scale for a single department or single application and then expand seamlessly to support other departments and applications using the same model and platform. Deploy quickly, experiment iteratively, and scale rapidly – is how enterprises need to think about putting in place analytics capabilities and that is what packaged analytics promises to deliver. Packaged analytics need to be seen as a modern business-user-centric capability. Its success has to be measured in terms of ease and speed at which enterprises can deploy and execute the models as well as iteratively refine them based on the model output.With pre-built integration with various data-sources, issue-specific KPIs, machine learning framework, analytics workbench for experimentation, and best-practice templates, packaged analytics can help in accelerated adoption of analytics across the organisation. Packaged analytics models need to integrate all the components required to deliver a ready to use solution:Connectors for data ingestion from multiple data sources (Real-time, Near-real-time, Batch)Data preparation tools (ETL, Anonymisation, Aggregation)Pre-defined KPIs for specific business problemsMachine learning framework and analytical algorithmsAnalytics workbench for fine-tuning, performance enhancementSelf-service visualisation dashboards Accelerate time from data to decision to dollarsNow let us see what promises packaged analytics hold for enterprises and business users. It will help them to do faster, deeper, objective-driven analytics more efficiently with a self-serve interface, significantly reducing the ‘data to decision to dollar’ time.Telecom industry is among the frontrunners in generating a humongous volume of numerous varieties of data. With Packaged analytics telcos can embrace this new reality to drive operational efficiencies, embark upon new revenue streams, and improve the customer experience through data driven insights. A snippet of economic value delivered to various telcos through packaged analytics models: Examples: packaged analytical modelsIn addition to the packaged analytics models that drive the internal monetisation for telcos with subscriber insights such as churn score, retention score, channel affinity, next best offer propensity etc., there are advanced analytics models to open up external monetisation revenue streams as well. For mobile advertising consumer insights such as E-commerce users, Mobile-wallet users, Rural-consumers, International travellers etc. can enable brands to engage with the most relevant audience through an optimal channel. With Packaged analytics Telcos can also deliver useful insights to partners in the digital economy such as device migration patterns, brand loyalty score, population density etc. to drive additional revenue.Packaged analytics approach: From inception to actionFlytxt offers 100+ proprietary packaged analytics models of which 80+ models are running live with 7 telcos across 5 countries, consistently delivering 2-7% of economic value over last few years.Having established the value of packaged analytics, now let’s take a closer look on how to develop a packaged analytics framework that can deliver ‘right fit’ and ‘high performance’ analytical models to enterprises. Flytxt has a cross functional team including data scientists, developers and testers to build, customise, package, publish and maintain a set of packaged analytics models in the model library. The objective of this team is to build models to provide a repeatable and consistent solution to a wide variety of business problems using industry best practices and techniques.This enables faster rollout and empowers Dev-ops to instantiate and monitor models across various clients. The platform is then designed to allow the clients to subscribe and experiment with the models from model library and offers run-time environments for executing both data processing scripts and analytics models. Here again flexibility of platform to support different technologies and scripts is the key. Flytxt packaged analytics delivery frameworkThe fast mover advantage with packaged analyticsWith Packaged analytics, enterprises are equipped to embed intelligence into business processes, operate at transactional speeds, deliver insights to customer-facing employees, and provide ever-deeper insights for decision making.Packaged analytics offers an opportunity for data and analytics leaders to simplify the analytics landscape and reduce IT complexity with a self-serve capability for business users to accelerate time to insight and improve business value. As the concept and delivery framework matures, we will begin to see accelerated adoption of packaged analytics across the enterprises.

Blog Telecom · May 31, 2016

Unfriendly call-drops in the ocean of friendly conversations

In recent times, call-drop phenomena has emerged as a menacing blot on India’s much-appreciated telecom revolution. The annoyance among the mobile consumers in the world’s second largest mobile user market is understandable when being cut off in the middle of a conversation frequently. Despite the seemingly ubiquitous telecom towers in the cities, consumers still have to rush from one room to another to find the best spot at home or office to continue the conversations. Complaints relating to telecom sector, including call-drops, is the second biggest concern of consumers, according to data provided by the National Consumer Helpline (NCH). And the Google trend further substantiates call-drops as the top-of-the-mind consumer concern. Google trend on Keyword search “Call-drop”The regulatory body in India TRAI, defines a call-drop as the telcos’ inability to maintain a call, either incoming or outgoing, once it has been correctly established. Even though TRAI’s recent attempt to hold the telcos accountable for the call-drop nuisance through bringing in compensation plans for the consumers has been struck down by the court, it is important for telcos to handle call-drops properly. 100% mobile penetration and multi-SIM phenomena are leaving limited avenues for telcos to go after new customer acquisition. Thus customer retention is emerging as the most important strategic pillar for telcos long-term profitability. Diving deep reveals the drivers of call-drops There is a continuous debate among stakeholders (Telcos, Regulators, government etc.) on the extent of responsibility on call-drops that is attributable to telcos. The devil lies in the details and there are a multitude of factors that cause a call-drop. From the aforementioned reasons for call-drops only a fraction is directly attributable to the telcos. This is what they can look to manage. The handover issue can be corrected by giving proper definition among neighboring BTS (Base Transceiver Station). The shadow coverage can be controlled by controlling the power at which the BTS radiate or by changing the alignment of antenna. While the error in transmission media, hardware obsolescence and frequency interference issues can be reduced with proactive monitoring, pre-emptive maintenance of hardware and forward-looking frequency planning respectively. All call-drops are annoying, some are more annoying than others Frequent call-drops are not just annoying for the consumers, it is equally problematic for the telcos. Some direct cost associated with a call-drop includes: Multi-SIM phenomena: In a market like India, where 70% of the subscribers in certain segments are multi-SIM users, it is likely for them to move to another SIM for the subsequent call after call-drop. OTT options: In case of frequent call-drops subscribers may end up sending an SMS or WatsApp message instead of trying to call again. In addition, call-drops also have some indirect costs as well, which are far worse: The biggest of this is subscriber annoyance and eventual churn, thus shrinking the customer base and lifecycle value. Even if the subscriber does not churn, their perception about relative network quality takes a hit if call from one network drops and calls from another network goes through. Influential subscribers are likely to influence others to churn as well. Taking the bull by the horns with packaged analytics Traditionally, call-drops are always associated with network quality. Action are taken by cell site / area / zone in isolation and not by looking at the subscribers who are affected by frequent call-drops. This approach seems to be looking at only one side of the coin as it misses out on the most important parameter - the subscriber facing the inconvenience. With packaged analytics models telcos can come up with a comprehensive impact score that can be used as a barometer for the overall impact of a call-drop from a subscriber’s perspective.  We define the call-drop impact as:Call-drop Impact Score = f ( Annoyance Score, Customer lifetime value Score, Influence Score) Packaged analytics models to calculate comprehensive impact score of call-drops Bimodal approach to curb the impact of call-drops A comprehensive impact score like the one explained above for call-drop can enable telcos to take a bimodal approach to tackle the menace of call-drops. This results into a more proactive customer experience management. Network side Actions: Use capacity concerns from high-value and influential customers as input for rollout of new cell sites and network upgrades Identify home-location and work-location of high-value subscribers and ensure the cell-tower quality of the route is up to the mark. Subscriber side actions: Tie subscriber usage pattern insights with actual network quality to accurately predict churn and run retention campaigns. Run promotional campaigns factoring-in the comprehensive impact score of the subscribers to proactively reduce annoyance  of valuable subscribers ( High CLV, High Influence) This customer-centric view of the network quality issues faced by the subscribers enables telcos to better align their operational actions with their business objectives.

Blog Telecom · May 2, 2016

Telecom data analytics is a game changer for telcos in the digital era

Q&A with Mario Nolla, Senior VP – Analytics and Consulting Practice, Flytxt, in VanillaPlus Magazine.          In telecom industry, there seems to be a renewed focus around analytics. As someone who has worked on both sides, how do you look at this trend? Historically, telcos have used analytics extensively to run their businesses. However, when the environment gets challenging, objectives get harder and resources get constrained, you will always fall back on proper planning and analysis to achieve the desired goals. With telcos now exploring newer means of increasing revenues and optimising margins, analytics has gained center stage again. Though, expectations from analytics function are different now. Analytics is perceived as a game changer for telcos in the digital world. The sudden rise of ‘C-level data executives’ is evidence of how telcos are warming up to the cross-industry trends that are driving a shift in digital business.Telcos now wish to move beyond traditional descriptive and exploratory analytics, which was mainly used for postmortem of business decisions, to advanced analytics and machine learning driven automated decision making. These new big data analytics technology platforms are improving personalisation at a transformational scale by allowing telcos to manage customer expectations in the very moment of truths.The other expectation involves breaking down the traditional silo-based decision making. Instead of having fragmented data management and analytics, specific to different business units such as Marketing, Network and so on, big data analytics is allowing the telcos to have a single view of customers and the business. It is allowing them to bind together their operations and customer expectations tightly. Analytics has the caliber to be the nerve-center in the digital economy. This is possible through providing enterprises with consumer insights that enable them to make much more informed decisions that can generate higher incremental business value.It is important to understand that success in this new digital world not only depends on a telco’s ability to know their customer holistically but also with reference to different contexts. This means going well beyond traditional data sources and integrating other sources such as location, devices and OTT data, as well as acquiring the ability to make sense out of customer actions in real-time. There is also an increasing regulatory pressure on data privacy and security, which again modern analytics systems should cater to.The competitor landscape of telcos is shifting. How do you see them adapting to this new world of customer experience? Today digital disruption has brought almost all enterprises on the same footing when it comes to customer ownership. So telcos are going to have new competitors, especially, when they move beyond their core services to offer digital lifestyle services like mobile payment, m-commerce, etc. Every service and process in digital realm demand customer-centric thinking and execution. So a deeper understanding of customer’s persona and his changing needs is a must. However, the real challenge of managing customer experience is not in the analytics part. That is relatively easier part to fix with proper analytics tools and team. The harder part of it is to be able to do all the operational changes that are necessary to bring those insights at the right time to the right person. For example, how do you make the insight available to the agent in front of the customer in the moment of truth? That is the hardest part.Empowering the last mile brings its own set of challenges to the telcos as they won’t have much control, in the traditional sense. In many of the markets that we work, some divisions of our telco clients are contracted out to franchises, especially Sales & Distribution and Customer Care. Providing that much information and power to the channel executives and touch-point personnel is a sensitive proposition for telcos. However, many telcos are increasingly looking at shifting the focus from indirect channels to direct channels in order to have more control over the customer experience. Here again, if decision making can be automated across touch points, and channels are backed up with a deeper enterprise-wide view of the customer, telcos can nail it down without making drastic changes to their current processes. Analytics needs to be seen as an enabler for better decision making and not as a change agent for overhauling the processes.What are the skillsets required to get analytics right? How does Flytxt support telcos in creating business value through analytics?We did talk about how expectations from analytics function is changing for telcos. So traditional analytical tools and practices may not be enough. The focus of telcos has clearly shifted from IT-led reporting to business-led self-service analytics. Flytxt’s mission is to liberate telcos from worrying on how to get analytics right and allow them to just focus on business strategies. We take it upon ourselves to provide the required technology, business applications and services and help them transform their underlying data asset to significant business value through advanced analytics.Internally, we have evolved an analytics practice cutting across technology, business consulting and operations teams. There is a continuous focus around creating suitable analytical models for telecom business environment, leveraging advanced machine learning algorithms. We call this packaged analytics. The objective is reducing the time taken to create and deploy a model that can help in solving telcos’ specific business problems like churn mitigation, fraud detection, bandwidth utilization, etc. This calls for data scientists and decision scientists working together. However, you may still need some kind of fine-tuning when you deploy the model in live environment. But again, we are talking about 2 to 3 weeks kind of time frame for deploying a model in place compared to months in traditional approach. Decision scientists leverage Flytxt’s packaged analytics models for self-serve analytics and data discovery. The evolution of Flytxt's Big Data Analytics platform into a self-service platform for end users like decision scientists, IT operations team and data scientists offers significant benefits to these end users in terms of productivity improvements, faster decision making and optimal realization of economic value. Measurable economic value through faster and efficient decision making is the end goal of Flytxt’s analytics and consulting practice.What are the barriers telcos face in adopting analytics capabilities?The major challenge in adoption of analytics is the alignment of overall operational processes with the analytics objectives. Analytics gives actions and recommendations, which still needs to be executed within the window of relevance to realise desired business impact. The exponential growth in the volume, variety and complexity of data, has changed the paradigm of deriving business value from analytics. To meet the time-to-insight demands of today's competitive business environment, telcos need to democratise analytics with self-service capabilities. Another aspect is ensuring the quality and reliability of data. Telcos have built the traditional data warehouse in Frankensteinian approach as the business was growing organically or through mergers and acquisitions. And still data analytics teams spend a lot of time reconciling different definitions of KPIs across various analytics systems. It has to give way to a more nimble footed approach, where required data for analytics can be integrated and accessed in a short time. Thanks to the capabilities of new age analytics tools, the barriers of consumer privacy and data security seems to be no more a show-stopper now.  How do you see the analytics landscape evolving in the next few years?What analytics delivers to the telcos is the ability to make smarter decisions faster. Its scope could extend from in the moment of truth decisions taken on customer touch points, to the strategic decisions made by the CXOs. There are two important sides to the evolutionary landscape. On one side, an increasing emphasis will be laid on the ownership of analytics function and on the other side the usage of insights from analytics will become widespread across department/functions.As analytics is fast emerging as a core competency and competitive differentiator for telcos, its importance has risen to the executive level. CAO (Chief Analytics Officer) and CDO (Chief Data Officer) are emerging as senior business executives responsible for creating the analytics strategy to drive digital business transformation.  In near future, these executives will be seen directly reporting to the CEO with an organization-wide executive authority for data and analytics, having a clear mandate to foster collaboration across departments in making smarter and faster decisions. On the usage aspect, the analytics progression into the future will be guided by how the analytics practice is adopted by multiple teams for different objectives. The evolution of self-service analytics tools will allow people with very little knowledge of analytics to use advanced analytics and gain benefits from them. However, the need for specialists will not go away, in fact that will rise further to ensure they manage and provide the whole infrastructure and foundation for decision making across the organisation. Use cases will extend beyond telecom business to digital business and connected world, creating new business models and partnerships for telcos. Can you elaborate on some of those analytics driven monetisation opportunities for telcos?Telcos have predominantly focused on customer value management (CVM). It has given them measurable revenue uplift as analytics improved their ability to micro-segment the base and to personalise offers and services over touch points across customer lifecycle. We are definitely seeing use cases like contextual marketing and churn detection maturing across the markets. With new customer engagement channels emerging like social media, a set of new use cases like social network analysis and sentiment analysis are showing lot of promise. We also expect the other departments and functions to take a cue from marketing and increase their analytics focus for operational and strategic decision making. It could find applications across optimising network utilization, customer care efficiency as well as sales and distribution network.Going forward, analytics will transcend the realm of internal business workflows to enable telcos to profitably participate in the digital economy by offering innovative digital services on their own or in partnership with other enterprises.The economics of data monetisation is changing dramatically with new business models predicated on new data sources and external monetisation use-cases. Some use-cases involve scenarios where consumer data is analysed to extract insights that can be monetized with other verticals, such as advertisers, healthcare industry, transportation players, government and retailers. This Q&A was originally published in Vanillaplus Magazine

Blog Telecom · Apr 4, 2016

Rich Content Messaging: The phoenix rises

As mobile advertising continues to outpace its other counterparts in digital advertising landscape, mobile video ad, in particular, is experiencing a phenomenal growth. There are many factors that are working in its favour, like its powerful story telling capability and its potential to go viral when the content is engaging. Eyeballs for mobile video is still too low Today, consumers use multiple screens to consume video, and brands are recognising that mobile is likely to emerge as the most important screen of all, outpacing desktop and television in the coming days. However, in emerging markets, one of the major drawbacks of mobile video ads is that its reach is still limited due to relatively lower smartphone and mobile internet penetration. India is now second largest smartphone market, but penetration is still hovering at 15%. Again, only 20% of mobile users’ in India access internet. This leaves out a vast majority of mobile users who are either on feature phones or those who do not necessarily access mobile web and apps. Hence it is quite obvious that brands are missing a larger section of their target segments when it comes to mobile video ads. A one-to-one channel - Rich video ads straight to consumers For mobile video ads to truly reach its potential, this limitation of reach must be overcome. And this is possible only when brand advertisers unlock the potential of a hitherto untapped mobile channel – Rich Content Messaging (RCM), which was referred to as Multi-media Messaging Service (MMS) in its early days. The biggest advantage of RCM is that it can deliver rich, engaging, interactive audio-visual content, straight to consumers’ phones with a compelling user experience. With majority of mobile phones in use supporting rich content messaging and RCM having a significantly higher open rates, the reach is not a challenge anymore. When this enhanced reach is combined with the creative and visual appeal of video ads - one would clearly see the enormous potential and opportunities of this unique mobile ad channel for brands in improving consumer engagement. Right content to right segment! For video ads to really make sense to its audience and create an impact, advertisers need to ensure that the content is relevant and targeted to the right audience. While targeting has certainly improved in digital channels, it still isn’t very advanced. This is largely due to lack of availability of authentic data which is easily found in a telco’s ecosystem. By leveraging this data which is enriched with precise behavioural, location and demographic insights, RCM can be used to create and push relevant content to consumers. Another aspect that brands relentlessly pursue, in a bid to increase RoI, is the measurability. Among the existing digital channels, this pursuit is met with a jolt as there is a possibility of duplication of views. This means, there is no accurate way to ascertain unique viewers of a video ad on the web or an app. For example, a single user can visit multiple sites or apps and view the same ad but each time it is counted separately, resulting in erroneous viewership calculations. With RCM this aberration is eliminated as it uniquely and directly reaches its audiences through a direct channel. The success stories galore Many brands are seeing success with RCM such as organised retailers, automobile brands, FMCG brands, ecommerce players and the BSFI sector. While the FMCG sector leverage the medium to reach the rural audience, the BSFI sector regularly send product offers through this channel to urban and semi-urban populace. Such targeting is possible only if advertisers have in-depth location and demographic insights. Telco data delivers it and much more. We have seen around 7% uplift in engagement for many of our clients across these verticals with this promising channel – the RCM. Not just brand advertisers, telcos themselves can reap the benefits of RCM for their own marketing initiatives. Due to its interactive and audio-visual nature, RCM offers an ideal platform to promote premium services like polyphonic ring tones, wallpapers, screen savers and themes for mobile terminals and so on. The potential of this proposition is backed by the evidence that shows that consumers have been receptive to relevant, SMS-based marketing in the past. Telcos can even use this channel effectively to deliver a variety of information and entertainment nuggets. They can tap RCM for dispensing micro-rewards and loyalty points in the form of videos or music. RCM: The rising phoenix The rich potential of RCM is only beginning to surface and as big brands and advertisers are starting to recognise its power and reach, it is only a matter of time before it becomes one of the critical tools in a marketers’ digital advertising arsenal. In future, marketers will increasingly rely on RCM to deliver interactive ads to target audiences via Application-to-Person (A2P) mobile messaging. Multimedia messaging has risen like a phoenix in its new incarnation – RCM. If this channel is properly exploited, it has the calibre to reshape the landscape of mobile advertising by making it more personal, creative, and interactive than ever before.

Blog Telecom · Mar 21, 2016

MWC 2016 round up: Mobile is everything and digital is everywhere

In a word: MWC The premier telecom industry showcase has been wrapped up for another year. With Barcelona still (relatively) fresh in our minds, I take the opportunity to reflect upon the moments of truth, disillusionment and self-realisation. Internet of Things, digital economy, digital transformation , Big data analytics, mobile security, virtual reality, smart homes, autonomous cars, intelligent drones, wearable gadgets, enterprise mobility, 5G networks, app monetisation - these words simply summarise the Mobile World Congress 2016 edition. Is there a new ‘business as usual’? ABSOLUTELY. It was no surprise that digitalisation was the single most overarching theme of MWC 2016. And few trends that managed to cut through the noise and linger-on in our minds long after the curtains came down. IoT: Beyond a buzzword IoT emerged to be the most discussed theme. Almost every other stand included the IoT theme in one form or the other ranging from autonomous cars to programmed toothbrushes. Lots of demonstrations of real-world use cases were at display from telcos, technology giants, and consulting firms. Narrow-band IoT gained momentum with SK telecom showcasing the same and Intel supported IoTivity with demonstration of some real-world scenarios. M2M as a terminology was conspicuous by its absence and IoT seems to have taken over in true sense. IoT business model: Modelling the future IoT definitely was the limelight of the show; however, many organisations are still ambiguous about their approach to IoT. The IoT frenzy was so palpable that every participant appeared to be riding on the buzzword even without a direct play in the IoT world. However, we seem to have passed the stage of scepticism and experimentation and the time to validate the commercial viability of IoT use-cases across industries has drawn-in upon us. IoT is on track to disrupt conventional business models. This software-driven, connected world will need the right analytical solutions to effectively protect and remotely manage the multitude of IoT devices while maximising the value derived from the data and services in the ecosystem. Digital disruption: Device ecosystem gets ready MWC 2016 had lesser focus on the smartphone as a form factor than expected. Instead the device manufacturers seems to be busy building a constellation of personal devices, which ranged from smart home, smart watch, and smart appliances along with a spectrum of Wearables. As the volume of devices in need for connectivity is going through the roof, there is a new connectivity model emerging in the form of e-SIMs. OEMs as well as Telcos are collaborating to ensure the digital ecosystem flourishes further by providing seamless connectivity to new set of devices giving consumers the option to switch provider and plan without having to request a brand new SIM card. Digitalisation: Plan for customer-centricity and business agility Telcos need to compete on the basis of offering superior customer experience as growth through customer acquisition is almost over. The only viable strategy left for Telcos to survive is by selling more to existing and holding on to them. In addition, connectivity is becoming less and less about handsets, more about things like cars and gadgets and toothbrush. Consumers' accelerated adoption of connected-home services and devices will challenge the capabilities of any single provider, driving digital service providers to establish an ecosystem of partners. When everything gets connected, it is not enough to “get it.” It’s about execution which includes tackling some major bottlenecks that includes validating relevant use cases, testing scalability, and ensuring privacy. Virtual reality: ...is not virtual any more Put on virtual reality goggles and take a journey through a new world of mobility! There were some seriously long queues for experiencing – VR handsets, 360 degree videos, virtual games, portable augmented reality, full video tracking etc. Facebook CEO, Mark Zuckerberg, turned up on stage at the Samsung Galaxy Unpacked event to pledge his support for the Samsung Gear VR. Excited by the prospect of 3600 video and the chance for more people to watch it, especially on Facebook, the company has created a new "Social VR team" at Facebook that will focus entirely on exploring the future of social interaction in VR. Handset launches – Is the game of phones slowing down Unlike the previous years, this year at MWC, there were very few handset launches that managed to get the attention they are used to getting. MWC crowd flocked into the VR show, freebies, loud music and free food at OEM booths rather than to the new handset launches. The few handset launches that created some news during MWC 2016 are when Samsung announced their latest line of Galaxy S7 and S7 Edge and Sony showed off a new set of Xperia phones. Among the Chinese manufacturers, Xiaomi, Oppo and ZTE made a splash with high-power, low-cost handsets. Virtual reality gears were the most discussed handset accessory. Apart from the above mentioned megatrends below are few micro-trends that made an impression at MWC 2016: 5G: Focus shifts from speed to latency 5G was the dark horse of MWC 2016. Apart from the numerous demos and jargon-filled press releases about 5G, there were also some impressive demos and a palpable excitement in the air for what's to come next. It also seemed that most talked about use cases of 5G were focused on lower latency, higher coverage rather than higher speed. It seemed download speed more than 4G is not fancied at this point in time. Apps: Rise of the Planet of Apps This year's Mobile World Congress presented an array of increasingly intelligent apps that utilize the connected home prospect and big data, which will impact smart-device makers. Challenges include creating apps that connect ecosystems and deliver significant value beyond being a remote control. This year MWC also witnessed a much bigger presence of Ad tech companies and bigger battles between network ad blocker with advertising industry is likely to continue in the near future. Mobile service value add: Rich content messaging takes big strides It is not just handsets which got a revamp at MWC. Everything that goes inside it is in for a transformation too. Google, for example, heralded the arrival of a brand-new standard for text messages called rich communication services that would unite the entire Android ecosystem under one free, universal service, as iMessage does for iPhones. Phone cameras may be getting a boost as well, with Oppo's funky new stabilized image sensors. To sum it up The three words which came up most prominently amidst the myriad of themes in MWC, from the signage in the exhibitor halls, to video ads, to brochures and even T-shirts, gifts, handbags and caps are Digital, Connected, and Virtual world. The message that came out loud and clear - there is a significant potential for Big Data Analytics to emerge as the central nervous system in the hyper-connected digital World. We had a great time at MWC 2016 – and we can’t wait for MWC 2017. See you there!

Blog Telecom · Mar 15, 2016

Tap into mobile consumer insights to win big in digital economy!

There is increasing consensus in the industry that telcos need to redefine their businesses in order to stay relevant in the fast evolving digital economy. Today’s consumers are able to access a vast number of lifestyle changing value added services with ease because of high speed networks, smart mobile devices and new digital delivery channels. It is drastically changing their consumption behaviour and expectations. Industry figures reveal that telcos around the world have spent over $700 billion in infrastructure investment over the past ten years. But revenue growth has been almost flat since 2007, though data usage has shown growth. A large chunk of the telco revenue from voice and text services is now being diverted to the OTT players, who tap into the telco’s infrastructure to deliver their services. Wi-Fi offload is again a serious challenge to telco’s access revenue. Digitalisation brings new opportunities The new era of digitalisation is widely anticipated as an opportunity for telcos to get back to a time of profitable growth and, more importantly, deeper consumer ownership. With the number of mobile subscribers growing 2X the world population (source: Cisco ), telcos will potentially own the maximum attention span of consumers. Telcos are now in a unique position to capitalise on emerging trends in the industry as mobile traffic speed is expected to go up drastically and the smartphone ecosystem is set to evolve further to support a variety of novel digital transactions. They will potentially have the most holistic view of the consumer than any other enterprise. This provides them with an opportunity to play a bigger role in the digital economy. Analyst community holds the view that telcos could potentially create broad array of new product lines and services around consumer data and analytics. They can remain as an infrastructure provider or play the role of ‘mobile consumer behaviour specialist’, enabling enterprises to offer better digital experience with aggregated insights. And telcos can even launch completely new line of lifestyle products and services in multi-sided platform scenario beyond communication services to healthcare, payment, commerce, etc. Exchanging value in the digital world Beyond connectivity, if telcos want to extend their value chain in the digital and connected world, then leveraging mobile consumer insights is the way forward. It is tipped to emerge as the currency of value exchange between enterprises across the digital ecosystem. Telcos can enable a number of digital services that can bring better lifestyle experiences and benefits to consumers while creating new business opportunities for themselves and adjacent industries. Advanced analytics tools can excavate insights that inform the telcos on what the emerging trends are and where the shifts in consumer preferences and lifestyles are taking place. For example, insights on subscriber movement can help telcos determine the concentration of specific type of consumers in a particular location at particular time - young students in their preferred hang out place or middle aged women who shop during weekends on a specific street. This type of insight could benefit multiple industries like OOH advertisers and retail firms. A number of similar use cases are available and are being envisioned by telcos in an effort towards creating value for consumers and enterprises in the digital world. Moving towards a connected world With over 25 billion connected devices to be in use by 2020, the Internet of Things (IoT) will have a disruptive impact across all industries and all areas of society, says Gartner. As more and more devices get connected to the internet, telcos are provided with much bigger opportunity to monetise their data resources through third party partnerships. Virtually everything, from home appliances, transport, to wearable devices, will communicate with each other, exchanging data in one form or the other. Telcos will practically serve as insight factories as most of these communications will be channelled through a telco network. For a seamless connected experience for their consumers, telcos need to evolve new ways to collaborate with adjacent industries. The exchange and melding of cross-industry consumer data will create multiple revenue streams for telcos while enhancing the opportunities for other enterprises to engage with their customers. The benefits of deriving value from insights are not just limited to organisations, consumers benefit equally from them. A richer, 360 degree profile of the consumers will not only make the engagement highly personalised but also help them discover product and services that they could never have found otherwise. This, along with getting the right service at the right context, will improve the customer experience significantly and transform the consumers’ lifestyles. Addressing consumer concerns and creating value For years, telcos have faced several restrictions in utilizing consumer data for business improvement. The good news, however, is that telcos can now eliminate these obstacles with the help of advanced technologies and permission based engagement. These sophisticated technologies support anonymisation of personal data and ensure that the data does not leave the telco premises. Moreover, consumers will be more open to the use of their data when they begin to see real value in the exchange. This will require, on the part of telcos, to come up with innovations and partnerships that make consumers’ lives significantly more convenient through customised value added services. For this to happen, telcos will have to share consumer insights with other industries in a controlled and tactical way. Go deep in consumer analytics to push ahead In digital economy, telcos have a different competitive landscape. They will be pitted against the likes of Apple, Google and Facebook in this race for consumer ownership. Many of these players have established their prowess in creating brand loyalty by providing better digital experience to consumers with intelligence delivered through mobile consumer insights. Telcos so far have seen limited success in leveraging and monetising consumer insights due to lack of agility and reluctance to collaborate and bring in new business models. However, telcos realise this and many of them have already taken first steps towards creating an open platform for launching innovative digital products and services. They have unique advantages to bank on like strong regulatory compliance and proven infrastructure as well as consumer trust gained from serving them for years. However, the biggest advantage to them is their access to ever increasing volume of data generated by consumers. To gain better pace in securing their place in the new digital ecosystem they must fully embrace the power of analytics and tap its potential to open new doors of opportunity for them.

Blog Telecom · Mar 10, 2016

Tame the multi-SIM hydra with a razor sharp focus on customer behaviour analysis

With Multi-SIM hydra rearing its ugly heads, the battle for customer retention is slated to get more aggressive, especially in prepaid dominant markets. It is imperative for telcos to learn the art of being nimble-footed in ensuring proactive and pre-emptive care for customers throughout the lifecycle across touch points. Multi-SIM phenomena is posing new threats to erode the traditional revenue streams even further for telcos in the prepaid dominant markets. The rapid rise of multi-SIM adoption is a result of several supply-side undercurrents and demand-side dynamics. Supply-side undercurrents The foremost trend is the snowballing dominance of prepaid segment, especially in prepaid dominant markets. The price-war and aggressive promotions have led to opportunistic usage patterns.  The handset market has witnessed the emergence of dual SIM phones since 2011. Device ownership pattern shows more than 70% of the subscribers use multi-SIM handsets. Consumer-driven demand-side trends Nielsen survey confirms a growing inclination among subscribers having dual SIM capability to turn multi-SIM users.  Many customers in the prepaid segment gradually become deal seekers and rotational churners, who comprise anywhere between 25-33% of new acquisitions; their preference only being dictated by the choice of benefits and discounts available to them. The intention to separate work and personal activities, in some instances, also lead to the use of multiple SIMs by individuals. This scenario has now gained prominence even in prepaid dominant markets with almost 5-8% of subscribers preferring to maintain independent connections for their personal and work related calls. Multi-SIM syndrome posing new threats Multi-SIM proliferation poses multiple threats to telecom business. The net subscriber addition gets adversely affected and ARPU, the sacrosanct KPI, heads southwards. This is due to the fact that subscriber growth largely happens on account of customers acquiring a second or possibly a third SIM. As telcos continue to engage in unhealthy customer acquisition wars, the battle for customer wallet share keeps growing fiercer. Telco customers use these multiple SIMs opportunistically. So, they have dedicated SIMs for outgoing calls (primarily influenced by deals), for incoming calls (primarily influenced by network coverage) and for data and other services (primarily influenced by QoS). There is a clear segregation of primary and secondary usage, which gets established within a few days of a subscriber taking a new connection. The telco’s attention clearly needs to shift towards ways to hold onto customers by continuous engagement and pre-empt any change in their needs and address them proactively before they start to look out for alternatives. This can only be achieved through underpinning excellence in customer experience as the differentiator in prepaid dominant markets. Product differentiation and tariff discounting in these markets can only give short term gains, which are neither profitable nor sustainable. Telecom players need to invest more on technologies and processes to help them map, interpret and respond to all customer interactions across touch points, hence, driving a more meaningful and effective customer engagement. Tackling Multi-SIM conundrum with Analytics-driven actionable insights In dealing with multi-SIM syndrome, the best weapon in a telcos arsenal is the ability to track down multi-SIM devices and understand consumers’ usage pattern by leveraging huge volumes of data. Telcos need platforms to handle big and fast data with objective-driven analytical models to accurately identify multi-SIM users and then pre-emptively act to enhance stickiness. While all telcos have access to huge volumes of data, it is their ability to transform the data into actionable insights that gives them the edge. Flytxt offers a comprehensive framework with packaged analytics models to establish multi-SIM behaviour. The program has high accuracy levels and it leverages advanced analytical models like product affinity and adaptive cognizance to mitigate the business impact brought on by multi-SIM usage. Flytxt solution has been able to generate actionable insights on factors responsible for the multi-SIM behaviour and recommend corrective action in line with telcos’ objectives. The deployment of the framework is preceded by primary research to validate the various recommendations and provide qualitative insights, which influence the actions to be taken. The data from primary research helps to link the outcome of the model to the key factors that influence customers to look out for alternative/multiple connections; it also reveals insights on how competition is playing a part in driving Multi-SIM behaviour by inducing customers to shift their usage on specific legs to the second SIM. With the help of the comprehensive framework that uses analytics models to identify multi-SIM usage patterns and take corrective actions, we have unearthed actionable insights for our clients, some of which are specific to certain markets as captured below: The Virtuous Cycle of Analytics-Insights-Actions As evidenced by above insights, multi-SIM syndrome can no longer be denied or avoided and the only way to handle the challenge is by understanding consumer needs better and taking proactive and pre-emptive steps in the right direction to deliver on their expectations.

Blog Enterprise · Mar 9, 2016

Omni-channel Customer Service

Mobile phones are now an integral part of consumer lives but loyalty to service providers is not an assured outcome of that fact anymore. Telcos are constantly challenged to ensure consistent customer experience through service innovation and customer service excellence. Customer touch-points are of strategic importance to telcos in maximising opportunities to engage and strengthen the relationship with customers. Today’s customers interact over a variety of touch-points both digital and non-digital, depending on their convenience and choice. No matter which channels they choose to communicate over, they must be able perceive a continuity in the conversation they have with their service provider. With this in mind, telcos need to ensure an Omni-channel experience for their customers, that is, a seamless experience that spans across every customer touch-point.  Achieving this is not easy as a telco customer base can go up to several millions to hundred millions and ensuring a personal touch for every customer requires deep understanding of their needs, preferences and interests on a one- to-one level. Legacy systems and traditional technologies are not equipped to handle such complexity. Crunching enormous volumes of data to discover hidden insights and using them appropriately as opportunity arises requires advanced analytical capabilities. It enables telcos to process data stored in data warehouses as well as analyse, on the fly, streaming data coming in from billions of transactions and interactions taking place every moment. The historical data on the customer provides insights about their usage patterns, affinities and persona while the streaming, real-time data allows the telco to understand their current context. When these insights are combined, they create a holistic picture of each customer. However, the complexity doesn’t end with the volume, variety and the ability to process real-time data. Analytics technology must be adaptive enough to understand the dynamic needs of customers and their ever changing persona. A truly superior customer experience is delivered when a telco is able to anticipate the customer’s need based on a complex combination of behaviours – be it interest, most recent activity, location and so on. When analytics is able to produce such data-driven insights, it offers telcos valuable opportunities for improving sales, boosting loyalty and growing the customer wallet share. To provide an instance, when a customer calls the Call Centre of a telco and Customer Care agent brings up the details of that particular customer, the behind-the-scene analytics populates the screen with the best fit offers, which it selects from hundreds of offers that are mapped to this customer. In this case, analytics helps to discover trends like the customer has browsed latest smartphone deals on the service provider’s web portal and has been a regular viewer of sports content. So, the offers related to handset-data pack bundle and sports pack are recommended, which have high likelihood for acceptance due an expressed intent earlier. In this case, analytics significantly reduces the time taken by the Customer Care agent to select and extend the right offer from hundreds of options. It further improves the chances of acceptance and optimises the opportunity with the customer on that touch point. Similarly, context adds another level of personalisation. For example, when a customer is on roaming and checks-in to a self-service kiosk, the offer is refined and prioritised in accordance with the current context - location, and a roaming pack is featured on the kiosk screen. Further fine-tuning makes personalisation even more compelling when analytics learns from dynamic behaviour of customers across touch points and deprioritises offers that they have rejected in the past. All these instances show how a well-tailored, timely communication, delivered seamlessly across touch points, increase the likelihood of conversion of opportunities. A leading Asian telco was able to reverse the heavy offer rejection rates it was experiencing by integrating our analytics solution with automated recommendation and offer prioritisation capabilities. The telco was able to identify customer needs in real-time and provide highly personalised service recommendations across various touch points. This resulted in conversion rates shooting up from 2.1% to 11%, call hold times improving by 40% and offer decline rates reducing from 5% to 1.7%. Going forward, we will see a variety of advanced analytical techniques like text analytics being employed to derive insights from unstructured sources of data from customer care and social media interactions that can help in proactive customer care. A well thought-out and properly implemented Omni-channel experience and service personalisation lends telcos enormous competitive advantage and enhances the customer’s lifestyle experience. This article was originally published in CommsMEA Magazine

Blog Telecom · Feb 16, 2016

Personalized Services, Advertising, M2M and Identity Verification Drive Telco Co-Monetization Trends for 2016

By the turn of this year it is a common wisdom in telecom industry that telcos need to reinvent themselves to remain relevant in the digital landscape that is taking over all spheres of business and consumer lives. Telcos are increasingly recognizing the need to transform themselves from provider of basic communication services to digital service providers by leveraging their existing assets. Co-monetisation of assets, where telcos collaborate with vertical industries as well as extend their expertise to other industries, is one way of achieving that goal. This is a good time to take a look at the co-monetisation trends that are likely to prevail in the 2016 and beyond as this is going to be the ‘make or break’ year for telecom service providers: M2M will present the next frontier of growth for Telcos - As M2M technology picks up pace in 2016, spurred by the growth in connected devices, sensors, machines and appliances and so forth, it is going to present vast monetization opportunities for telcos. The sheer volume of data generated by M2M has the potential to create insights that can benefit both enterprises and consumers. As telcos realize the need to transform from mere voice and data services to digital enablers and further to value creators in the connected world, they will take more strident steps this year towards building business models based on data insights and providing end-to-end solution. In early initiatives towards M2M monetisation, telcos will be seen exploring business models around industrial and health monitoring data as well as risk assessment for insurance companies. They will also be seen capitalizing on their infrastructure assets, consumer insights, marketing reach, and proofs of packaging and marketing of M2M use cases for consumers to improve their lives. Telcos will ditch 'Doing -It- Alone' and embrace the acquisition or partnership path to bolster their digital transformation - One of the key hurdles that remain for telcos in monetizing their assets externally is converting assets into business value. Telcos have the infrastructure in place, wealth of consumer data and marketing reach, but they face challenges in exploiting these assets due to the lack of sufficient expertise beyond their own domain. In 2016, telcos will be increasingly looking outside to find solutions to these challenges. They are likely to partner or acquire specialized analytics firms, marketplaces and product/services companies that offer innovative business models for data monetization. The partnerships and acquisitions will accelerate the telcos’ digital transformation initiatives and give them the much needed agility in adapting to new industry and business models. Telco Participation in Identity verification and credit scoring will drive next level of digital engagement - Customer centricity continues to dominate the strategic goals for enterprises in the year 2016. Providing a seamless and personalized experience across channels, locations and off-site interactions will lead the customer-facing mobile enterprises to seek ways to link the online-offline persona of customers. Telcos, with their vast network and large customer bases, will prove be a key partner in enabling these enterprises in tracking and relating the online identity with the offline persona for consistent customer experience anywhere, anytime. Another area where telcos will be seen expanding their collaboration with other industries and OTT players is the analysis of customer data to provide anonymized scoring for credit worthiness, loyalty, and purchasing power of individuals. To provide an example, in developing countries where mobile penetration is much higher than the reach of banks, telcos will enable banks and financial institutions to assess the credit worthiness of borrowers in micro-financing and related schemes. Location insights will transform the way business planning is done - Location based services will prove to be a major area of growth for telcos in 2016. One of the key advantages for telcos in providing mobility insights on population is the fact that it offers enormous scale and does not require an explicit action from its users like downloading an app or reporting location on social media. In addition, the data is real-time and can be shared in an anonymized and secure manner. Telcos will be seen offering enabling services to a variety of businesses, like those seeking to understand the demographic profile or visitors' profile of a certain location for planning new outlets or retail stores; brands looking to attract nearby customers; sports and entertainment outfits trying to understand which localities their fans flow-in from in order to achieve better targeting and so on. With Government bodies slowly recognizing the need for incorporating innovative methods in their developmental efforts, they too will explore the possibility of using telcos’ location data for town and traffic planning. Telcos will emerge as the most evolved DMP - As mobile penetration exceed half of the world's population and the world sees rapid adoption of smartphones accelerated by the availability of cheaper handsets, mobile phones will start to function as a chief conduit for everything digital. This will get added boost with the expansion of 4G services and increasing M2M connections in 2016. This will generate unprecedented amount of data and make telcos a storehouse of insights on upcoming consumer trends and preferences. Telcos will capitalize on the development for improving personalization and relevance to their customers. Further, these insights will help telcos to forge innovative partnerships with all industries wanting to improve their consumers’ digital experience and build new digital products. This will lead to telcos evolving as the ultimate Data Management Platform. Telcos will finally cement their role as mobile ad media owner and provider of ad solutions - As the need for marketers to gain better conversions on promotions and higher returns on ad spend become more critical, brands will increasingly focus on mobile advertising. Consumers are spending more of their time on mobile, consuming all kinds of media. Mobile advertising saw a steady growth in 2015 and is soon expected to outpace other digital ad formats in many markets. The range of touch points available with telcos is unmatched in the industry. Telcos who have put in place the right technology and partnerships will offer advertisers a very attractive medium for reaching consumers in the right context and at scale. As a result, they will see more and more brands turning to them for targeted advertising solutions in the coming years. Telco insights will lead to market research 2.0 - Market research has so far mostly depended on sample consumer surveys and statistics available from point of sale data. Market researchers are constantly seeking ever more authentic ways to identify trends and validate their findings, based on which they make future industry predictions. With the fast-evolving digital age, the consumer data that telcos collect will be a dependable source of insights on consumer usage behaviour with respect to various brands. In 2016, telcos and market research firms can be expected to come together to break into this untapped opportunity and create more accurate predictions and estimations on industry trends. The telcos capability to crunch big data and derive industry specific demographic, psychographic and location insights will add another dimension of accuracy to the insights. While the future is full of promising new opportunities, three key factors will determine their success going forward. The first is how they manage the concerns around data privacy and security. Telcos that manage to implement the right technologies and institute proper data anonymization techniques will succeed in leveraging their data and lead the co-monetisation initiatives in the future. Second, the agility with which telcos derive market-centric consumer insights and turn them into business opportunity will determine which of them will get a head start in their co-monetization journey. This will require building capabilities in big data technology, acquiring domain specific decision sciences competences and developing data sciences expertise. Finally, one of the key determinants of success will hinge upon the business model telcos adopt for the new product and services that result from their co-monetization efforts. Telcos that manage to acquire and/or collaborate with the right partner – those having significant experience in enabling a variety of co-monetisation use cases – will be able to successfully scale and sustain their co-monetisation efforts in 2016 and beyond. This article was originally published in The Fast Mode

Blog Telecom · Jan 11, 2016

2016 Predictions for the Telecom World – Digital Transformation Leads the Way

The telecom industry will continue to face some real challenges of eroding revenues, increasing traffic, and higher costs. 2016 promises to bring some new factors in the market dynamics to enable Telcos to deal with these challenges and equip them with advanced solutions, frameworks and methodologies to embrace new opportunities in the fast-emerging connected digital world. Regulatory pressure will continue and intensify even further - As regulatory bodies across the globe rethink their approaches to regulate telcos in the connected digital world they will continue to take precautionary and restrictive measures in the name of security, anti-monopoly and consumer privacy. Regulatory roadmaps will include the measures to restore and prioritise trust and confidence for all stakeholders in the value ecosystem. 2016 will be the year where data-privacy and net-neutrality debates will intensify even further. We will see Telcos take the leadership in driving transparency and managing customer expectations on multiple fronts such as privacy, convenience, and personalisation, simultaneously. Next wave of consolidation among Network Equipment Providers will drive M&A -NEPs are witnessing significant consolidation, a trend we expect to continue, even accelerate in 2016. As common IT standards become the norm, consolidation and convergence are sweeping the industry, blurring the lines between the telecom, media, and entertainment players. These converged service providers will gain more control of the value chain and they will need to offer consumer-friendly services at cost-effective rates. This will put more pressure on equipment prices resulting in continued consolidation through M&A in the NEP market. New entrants, especially the Chinese manufacturers, will gain ground, quickly - As more and more handset manufacturers look to cross-over the traditional borders, the game of thrones will witness more challengers and new kings. Market saturation at home is pushing Chinese smartphone makers to seek new growth abroad. These Chinese players like Xiaomi will actively pursue innovative and aggressive strategies for world domination like extremely flashy product lines at affordable prices. Carrier-grade WiFi and standardization adoption will lead the way - Increasing demand from customers on consistent experience anytime, anywhere is intensifying the burden on licensed spectrum bandwidth. As new Wi-Fi standards emerge Telcos will have enough motivation to adopt these standards and implement carrier Wi-Fi offloading approach to supplement the existing spectrum, to provide stable “cellular-like” experiences to consumers. 2016 will see Telcos increase capacity in a cost-effective way and enhance customer experience through ubiquitous Wi-Fi and hotspots. I would not be surprised if we see emergence of Wi-Fi only operator using VO- Wi-Fi. OTT-Operators partnerships will flourish - OTT players and telecom operators have largely remained on a collision-course since the emergence of the former. 2016 promises to be an inflection point in the relationship of OTT players and Telcos. Telcos would look for collaboration opportunities with OTTs to offer new services to consumers through mobile money, storage, mobile advertising, etc. The challenge will be to design business models and associated use-cases for insight monetisation that will create win-win outcomes for both the partners. 5G adoption will hinge-upon several technological breakthroughs - The advent of 5G networks will strengthen the upcoming wave of connected digital world. 5G is expected to enable new use cases on enhanced mobile broadband, the Internet of Things (IoT) and unified and reliable communications. However, Telcos need to keep an eye on the technology expectation of 5G, such as IOT Support, Self-organizing Network (SON), Multiple-input Multiple-output (MIMO), Quality of Service (QoS) and spectral efficiency to support more than thousand-fold capacity increase that will accompany 5G adoption in the Telecom industry. SDN adoption should catch-up NFV rollouts - Telecom industry will see accelerated adoption rates of virtualisation technology. 2015 saw initial commercial NFV roll-outs from several telcos through the virtualisation of LTE network. In 2016, Telcos are expected to lead the SDN adoption drive. SDN's control together with NFV's flexibility will enable the Telcos to develop new business models, reduce network complexity, increase levels of service flexibility and reduce operational costs. Vo-LTE, Vo-WiFi will enable Telcos to rollout rich communication services (RCS) - 2016 promises to be the year when the transformation of voice transmission through VO-LTE and VO-WiFi will enable the emergence of rich communication services (RCS) like video calling, video messaging, conferencing etc., as network services rather than as apps. This will equip the telcos with much needed ammunitions to compete with the OTTs. This will also allow the Telcos to regain the control on customer relationship through better coverage at first, opening doors towards better network monetization via non-commoditized, advanced services in future. Leap from Business as Usual to Business as Digital - In 2016, Telcos will focus on ‘digital initiatives’ by bringing a modern digital experience to customers through mobile consumer analytics. Telcos will prioritise the transition from Telco services to digital Services provider to media owner. Non-traditional revenue streams through comprehensive data monetisation would be the imperatives for the telco world in 2016, to deal with the margin pressure and declining traditional revenue streams. The digital transformation will enable the telcos to offer differentiated and delightful experiences to consumers as well as harness the extended ecosystem including IOT and M2M trends. Telcos as IOT Service Provider - IoT consumer demand has really started to take off in 2015, and is expected to accelerate even further in 2016. We believe that 2016 will be the year when Telcos, especially in the most advanced telecom markets, are expected to emerge as the key catalyst in the rapidly evolving Internet of Things (IoT) ecosystem. Telcos with an inherent advantage with their connectivity layer, will look to advance the application layer in order to gain flexibility and agility required to become a multi-service provider in other vertical markets in the IoT ecosystem. 2016 promises to be the year when most of the industry-altering trends will gain further momentum and telcos would gain prominence as new ecosystems flourish. The emerging multi-sided business models, technological advancement and innovative mobile consumer analytics are creating the sweet-spot for telcos to transform into a digital service provider and enable connected living. Big data analytics platform with open APIs to deliver packaged insights, within an ecosystem without the risk of running into regulatory, security or privacy issues, will propel the operators from being bystanders, to a true enabler of multi-industry convergence. This article was originally published in Total Tele Magazine

Blog Enterprise · Dec 7, 2015

Self-Service Analytics Platforms Simplify Storytelling

The new generation Big Data Analytics solutions have come a long way in the last five years. They are getting deployed by enterprises where they are either complimenting or incrementally replacing their traditional analytics platforms on Data Warehouse. The big data analytics projects are increasingly driven by business users as they look for faster decisioning to create business value for enterprises in this dynamic, highly competitive and customer-centric world. I would like to call these business users, ‘the Decision Scientists’ as they apply domain knowledge and experience to make sense of insights discovered by data scientists and machine learning algorithms in order to make right decisions based on given context and business objective. They are not merely decision takers as they like to dig deeper into the data and insights and play with it before taking decisions. The current trend in the industry is to add a data discovery framework on top of Big Data analytics platforms to enable even the Decision Scientists to experiment with data or sometimes insights to arrive at better and deeper intelligence while significantly reducing the time taken to get at those decisions. Data Scientists and Analysts would still be required to create algorithms, analytics models and event parsers, however these would be packaged for data discovery, with configurable input data sources, event parsing logic, KPIs and model parameters. Above all, data and IT governance should also be simplified. These new capabilities require the following key changes in the functionality of contemporary Big Data analytics platforms: Big Data Platforms have to evolve into self-service tools - Big Data platform should allow agility, fast time to decisions and an increased productivity for Decision Scientists, and to achieve that, it has to be self-service. Apart from being robust, the platform should abstract all technology layers from the business user. Data models, technology stack, and analytics algorithms should be abstracted and available to the business users as a service or configurable module. For instance, all data integration workflows and analytics algorithms should be packaged into customizable models. Decision Scientists should themselves be able to customize the inputs to these models and improve performance and results by fine tuning model parameters. Decision Scientists should themselves be able to configure new data sources with little external help, view a catalog of integrated data rather than the data model, experiment with different packaged analytics algorithms and visualization for data discovery and then create dashboards and visual reports. Data governance needs to have more focus than day to day operations - Managing data manually is difficult and is not a scalable option, especially for big data sets. Decisions like what data has to be retained and for how long, what data has to be shelved, what level of access is allowed to users in the chain from data to decisions, accounting for data usage etc. continue to be based on manually configured rules, but bulk of the processes should be automated. Data quality control and correction is another important aspect that should be automated. A full automation might not be always possible, a Data Steward will have to certify data quality or take a decision/action occasionally when an alarm is raised, but the process of collecting information at check points in process flows and reporting them, and further, detecting and alerting a deviation either in data quality SLAs or process guidelines, should be automated. All other IT processes should be automated - All day to day operational processes that follow a predefined standard operating guideline are prime candidates for automation. Any necessary follow up action should also be either fully automated for self-healing, or in the case where manual intervention is inevitable, should be assisted by automated sub-processes or be backed with enough information to aid a quick RCA and correction. This calls for an operations management framework that collects data from check points and analyses them to detect any deviation in service SLA or quality and take or facilitate corrective actions. Simplified workflows for configuration, reports and analytics - An intuitive and powerful front end is an inevitable component of a self-serve platform. A user should be able to do all configurations, analytics, reporting and further actions from an App or a GUI from a hand held device. The front end should also allow collaboration between Decision scientists, where ideas are exchanged and results from analytics are shared among peers for review and discussions. Different business users require different views and reports, the front end should allow customizable dashboards and support a host of visualizations from which to choose from. It should also feature workbenches that aids Decision Scientist in the design for data ingestion, data preparation, analytics and experimentation, visualization and further actions, all in a configurable workflow. A good self-service platform should provide built-in data integration workflows for big and fast data acquisition, packaged analytics models offering insights, KPIs and recommendations on the fly, and built-in internal and external monetization workflows that can consume these insights to derive optimal economic value. This article was originally published in Information Management

Blog Telecom · Sep 23, 2015

Improving Marketing Agility through Real-time Decisions

Mobile subscribers expect value-driven engagement at every touch point. Communication Service Providers (CSPs) need to proactively manage subscriber expectations and offer personalized services for a seamless experience.  This is where the marketing agility of a CSP is being questioned and the value of Big Data Analytics is getting established. The staggering volume of subscribers is not a challenge for CSPs anymore. However the real task at hand is managing multiple layers of complexity brought on by the volume and variety of information available with them in networks, enterprise systems and partner ecosystem.  Within this data deluge lies a goldmine of opportunities that Big Data Analytics manages to glean out. However, Big Data will complete only half the story if it isn’t combined with Fast Data. With mobile phones being constant companions, there are myriad opportunities that are created every moment which the CSP can capitalize on. In order to do that, constant monitoring and faster response cycles are a pre-requisite. When Big Data combines with Fast Data, those results can be achieved. While traditional Big Data tools effectively store and analyze historical data in the “data lake,” Fast Data captures data as it streams in especially from the network. In an offline system, data is pulled into a big data system and the analytics engine can only detect trigger once it has happened (in the past); by the time a CSP reaches out to the subscriber, it may be too late to act. In contrast, real-time decisions or recommendations that are event-triggered result in much higher response rates, because they lead to contextually relevant actions. As a result, real-time decisions improve CSP’s marketing effectiveness. There are a number of use cases in Telecom vertical that prove the value of integrating Big Data-Fast Data streams for analytics and decisioning. For example, real-time recommendations can be configured as part of automated targeted marketing campaigns. If the subscribers meet a certain usage criteria or reach required loyalty points, a trigger can be generated to send them special offers. Similarly, when subscriber opts for a certain product, a complimentary product can be extended after making sure that she has enough in her balance to spend. Another example could be based on events that trigger action. For example, if a subscriber enters a certain geo-location, relevant advertising offers can be sent to increase footfall to a mall or an outlet in the location. Any privacy concerns that may arise here can be eliminated if the right analytics technology is used that honor privacy and permission, in addition to anonymizing subscriber data to make it ‘Non- Personally Identifiable Information (Non- PII). Real-time analytics with streaming data can also be very effective in arresting churn and winning back subscribers showing churn symptoms. The CSP benefit from long-term sight (Big Data), such as “this subscriber is likely to return and needs to be watched,” as well as a real-time insight, such as “this subscriber has made fewer calls today than the average calling pattern.” The combination of historical/real-time insight helps the CSP to reach out immediately with relevant offers. Event-driven triggers and learned intelligence are combined to build an offer for the subscriber that is delivered in real-time through his/her preferred channel. Personalization of products and services based on the context could also have a significant impact on improving subscriber experience. Big Data analytics, based on historical data, informs the CSP about the inclinations and preferences of the subscriber whereas real-time data tells the CSP what the subscriber is engaged in at the present moment. When these two are combined, tailored promotions could be extended that suit the current context of the subscriber. For example, a high value subscriber is watching videos on mobile and experiences data throttling. With real-time analysis, this is detected and the subscriber is immediately upgraded to a higher speed network. This will have immediate positive impact on the subscriber’s experience, which in turn will benefit the CSP. To understand the impact of real-time processing, let’s look at its application in a practical scenario. Flytxt offers Big Data analytics solutions to its CSP clients across globe. In one of its deployments, Flytxt processed events streaming each day from more than 200 million mobile subscribers. These numbers – from just one provider – highlight the data management challenges within the telecommunication sector, which demands more capability than traditional Big Data platforms offer. Flytxt has partnered with VoltDB, an in-memory operational database and leader in fast data, to enhance its real-time capabilities. Using VoltDB, Flytxt processes nearly four (4) billion events every day.  The results are visible in numbers. Real-time trigger campaigns yield a 40 percent to 300 percent higher conversion rate and a reduction in response time to customer events – 24 hours vs. 30 minutes – compared to non-real-time campaigns. Big Data insights coupled with in-stream data processing empowers the CSP to remain prepared for any opportunity that may arise in the future, no matter how small is the window or how perishable is the opportunity. It allows the CSP to have a 360 degree view of the subscriber that is essential for improving subscriber experience, personalizing communication and maintaining quality of service. With faster decision cycles and time-to-market products and services, the CSP can generate significant incremental revenue.

Blog Enterprise · Jul 27, 2015

Connecting Brands to Niche Markets, Nothing Better Than Mobile!

Gone are the days when mobile advertising meant sending blanket messages to a wide subscriber base. With advanced mobile consumer analytics, mobile advertising is fast emerging as an important component of digital advertising, with a high return on investment. What makes mobile channel very attractive is its reach as well as precise targeting and impact measurement capabilities. In this blog, I am going to discuss the case study of a heavy machinery brand, where they successfully exploited the power of mobile in reaching niche audiences scattered across both urban and rural regions of India to increase their brand awareness and generate sales. The Task Put Forward By the Client A leading name in the global heavy machinery market, this brand that manufactures equipment for construction and agriculture was entering the Indian market. They were looking to create brand awareness in their target markets and generate leads for their outlets to follow up for sales conversion. They realized that only mobile channel can give them desired reach and targeting accuracy in reaching such niche markets. Hence, the brand and their advertising agency partnered with Flytxt to run this campaign on mADmart, operator-anchored mobile advertising marketplace in India. Challenges Faced The market for construction equipment offers very few returns and is of the niche category that finds clients only of a certain demographic and a certain background. In this respect, it would be futile to run a countrywide campaign. Costs would also increase indeterminately as interested customers would be far and few in between. It was a challenge to get the message across to the right audience and the right group of people who would be spread across the villages, towns and cities of India. Identifying the right target segment This brand started the campaign with its decision to focus on regions where it already has its presence in the form of outlets across the country. The target segment was clear – small business owners and affluent farmers who would require their heavy machinery equipment for construction and farming. Using exploratory analytical capabilities of QREDA platform, Flytxt could leverage its mobile operator partner’s data available on its subscribers, to segment them based on multiple factors like age, gender, location, income, occupation, use of data product, etc. The Course of the Marketing Program The campaign was executed over SMS and WAP Push as per its relevance to the target audience. Initially, the promotional messages were sent to the specifically identified target group, requesting them to give a missed call to a number or click on the link to go the landing page if they were interested in procuring any equipment. Language barriers were removed as the messages were sent in both Hindi and English language. The responses were directly sent to the client’s CRM system, which in turn were used by call centre to follow up with the interested parties for converting them to sales. Business Impact Target group identification took place while maintaining the privacy of the subscribers and also within the first 2 to 3 weeks itself. The campaign was financially feasible as its entire cost was less than the price of a single machine. The campaign had a 25X RoI on each lead generated. Conclusion As it can be seen, with right analytics, targeting and conversion on mobile produces far superior results than traditional advertising efforts. No matter what your brand or industry and howsoever scattered your audience, mobile advertising can prove to be effective and efficient if it is based on authentic data and deeper consumer insights. With the right analytics technology this data can be converted into precise insights that deliver maximum results for every advertising buck spent on it.

Blog Telecom · May 11, 2015

3 Steps to Go Deeper in Touch Point Personalization for Telcos

Customer touch points are arguably the most important interfaces to Communication Service Providers (CSPs) for subscriber engagement. It is not just a platform to provide excellent service, but also an opportunity to enhance the relationship and increase the Customer Lifecycle Value. This is where it becomes extremely important how the subscribers perceive a CSP’s marketing efforts on any touch point. If the marketer’s action or communication does not appeal to a subscriber’s preferences, it runs the risk of irritating them, leading to dissatisfied and angry subscribers. On the other hand, if the actions are personalized based on anticipated needs of the subscriber, they walk out with more value than they expected. A happy subscriber is bound to stay loyal and keep coming back for more. Marketing actions over a touch point typically relates to extending offers. This blog will explore how managing subscriber intent, through machine-driven offer recommendation and offer prioritization, can potentially take subscriber experience to a whole new level. Step 1: Offer basket recommendation based on historic persona With advanced packaged analytics it is now possible to discover hidden insights by analyzing a subscribers past behavior. A holistic view of the subscriber enables designing marketing communication that are well-tailored to a subscriber’s persona and addresses his or her likes and dislikes. With increasing complexity and myriad products and services, there are hundreds of relevant offers that are mapped to a single user. When a subscriber approaches a subscriber touch-point, there is a very small window of opportunity within which the best matched offers must be communicated to them. If the offers fail to grab the subscriber’s attention in the short span, that opportunity to serve and provide a superior experience is lost. This challenge is tackled by offer basket recommendation, which uses machine-learned algorithms to filter out only the best fit offers that have the highest likelihood of acceptance by a subscriber. The resultant offer basket is a set of uniquely personalized offers that are most suited to a subscriber. For instance, suppose a subscriber, with a non-data enabled handset, logs in to the service provider’s web portal for voice recharge. The browsing history tells the system that he or she has been looking at bundled smartphone offers in the recent past. The subscriber’s webpage will be populated with various data pack offers as offer basket recommendation algorithms comprehend that the subscriber may soon graduate to a data user. Step 2: Offer refinement based on current context Subscriber behavior is dynamic and contexts change within minutes. While in one moment the subscriber is engaging in voice calling, the next moment he is browsing cricket scores. Yet at another time he is busy interacting on social media. For marketing communication to be truly effective and experience enhancing, it is important to keep track of these in-the-moment behaviors. To further align marketing communication to changing subscriber behavior, offer refinement algorithm comes into the picture. This algorithm dynamically changes the subscriber’s offer basket based on the constantly changing consumption pattern of the subscriber. To illustrate this with an example, let us consider a subscriber who is categorized as a heavy local caller among many other attributes. The subscriber approaches the customer care call center with an inquiry. The call center executive, in addition to addressing the subscriber’s query, has a list of best fit offers to read out to the subscriber. In normal circumstances, the offer recommendation basket will feature a ‘local call special package’. However, on checking the real-time context, the machine detects that the subscriber is on roaming. This new status will trigger the automated machine-driven offer refinement algorithm to de-prioritize the ‘local call offer’ with a ‘roaming pack offer’, adapting to subscriber’s current context. Step 3: Adaptive ranking based on recent touch point behavior Offer recommendation basket and offer refinement algorithms ensure that the subscriber receives well-tailored as well as contextual and relevant communication across customer touch points. However, at times even the most relevant offers based on subscribers past behavior and anticipated needs may not find favorability with the subscriber. Human beings are impulsive in nature and their response may be triggered by a momentary dislike or a changed circumstance, among innumerable other reasons. In such a situation, badgering the subscriber with the offers he or she declined may ultimately hurt the subscriber experience. Adaptive offer ranking based on recent touch-point behavior enables the machine to learn preferences and re-arrange options accordingly. For instance, if a subscriber uses only to 2G data services on his or her 3G-enabled smartphone and has declined 3G data pack offers in the past, the machine will direct the offer ranking algorithm to move 3G offers down the list and limit recommendations only to 2G data offers for certain time period. This ensures that subscriber’s preference or non-preference for a certain product is honored. Machine-driven automated offer recommendation, prioritization as well ranking through packaged analytics enables CSPs to keep constantly in tune with the subscribers needs and delivering intuitively what they want, ensuring proactive communication at all times. A Case Study A global CSP with more than 170 million subscribers in India was troubled with high offer-rejection rates at its various touch-points. To address the issue, it integrated Flytxt’s Intent Management Application with its customer care portal. The application uses advanced behavior analytics – automated recommendation and offer prioritization capabilities to identify customer needs in real-time and provide highly personalized service recommendations across various touch-points. With relevant, contextual and dynamically adaptive offer communication, the CSP was able to provide significantly superior customer experience and as a result, their marketing offers raked in high acceptance rates. Conversion rates shot from 2.1% to 11%, call hold time improved by 40%, and offer decline reduced from 5% to 0.17%. The application also enabled the preparation of in-depth performance reports of agents and products so that a profit-based evaluation could be done more precisely and informed business decisions could be made. Understanding subscribers at a deeper level and enabling a high degree of touch-point personalization helps elevate subscriber experience, which in turn paves way for stronger relationships. And the stronger relationships CSPs build, the better is success rate.