c-suite

Humanizing personalization in Telecom– agents & knowledge-led AI Playbooks

insight
February 03, 2025
9 min read



Rahul Mahajan is a VP & CTO of Digital Business Transformation at Nagarro. He specializes in AI, machine learning, cloud, and IoT, driving innovation in eCommerce, customer experience, and enterprise modernization.

With competition intensifying, telecom customers have growing expectations and adapt quickly. Both B2B and B2C customers demand personalized, seamless, and meaningful interactions during their purchase and usage journeys. With their diverse offerings, such as mobile services, IoT solutions, entertainment bundles, and enterprise connectivity, telecom providers have an immense opportunity to differentiate themselves by humanizing experiences that meet and anticipate customer needs. 

However, humanizing personalization at scale requires an intelligent convergence of data, advanced analytics, and human-like AI interactions. This is where Agentic AI — the fusion of knowledge graphs, AI agents, and large language models (LLMs) — comes into play. By using AI-driven, connected experiences, telecom companies can transform their sales strategies to improve revenue, customer satisfaction, and long-term loyalty. 

Humanizing personalization to unlock the next level of sales engagement

In the context of telecom sales (B2B/B2C), an interesting example of humanizing personalization could be an AI playbook where AI agents seamlessly advise the sales teams with highly contextual real-time recommendations for the next best actions—tailored product bundles, personalized marketing nudges, contextual discounts, and predictive sales strategies. Such advisory playbooks are enhanced through generative AI, which acts as an intuitive, human-like interface, enabling natural, interactive conversations that improve customer engagement. Once advisory actions are activated, these actions are deployed across the telecom network, driving higher sales revenue, improved customer satisfaction, and long-term loyalty.

This AI-powered transformation is more than just an evolution—it’s a game changer. By revolutionizing sales and customer engagement through AI-driven personalization in telecom, providers can unlock unprecedented opportunities and shape the future of personalized, connected experiences. 


Key building blocks for Telecom’s next big shift: knowledge, agentic, and generative as key ingredients for Humanizing personalization

With the new technology-led disruptive shifts, telecom companies have the opportunity to demonstrate the transformative impact like never before. For transformation across sales and customer experience, here are some of the key building blocks: 

Customer Knowledge Graph

A dynamic real-time knowledge graph maps B2B and B2C customers and captures their product usage, preferences, behavior, and interactions across all channels. By integrating CRM, product datasets, device details, billing datasets, call recordings, support tickets, and social listening data, telecom providers get a continuously evolving unified customer profile. 

How does it help?

This comprehensive view enables hyper-personalized engagement strategies and predictive modeling to identify churn risks and upsell opportunities, allowing businesses to enhance customer satisfaction and drive revenue growth proactively. 

AI-driven product analytics

AI analyzes trends in product and service usage, including data consumption, subscription patterns and feature adoption, to enable precise customer segmentation. These insights help identify leads with high potential for targeted upselling, bundling additional services, and developing personalized retention strategies. 

How does it help?

By using AI-driven analytics, telecom providers can enhance customer satisfaction, optimize customer retention, and achieve higher revenue growth through smarter, data-driven decisions. 

Next-Best-Action (NBA) Advisors for Sales Agents 

With every customer interaction, LLM-powered AI agents deliver real-time, data-driven NBA recommendations to sales reps. By analyzing historical sales data and successful interaction patterns, these AI advisors suggest effective actions, such as cross-selling IoT solutions, recommending plan upgrades, or offering personalized loyalty rewards. 

How does it help?

This AI-driven approach boosts sales rep performance, improves deal closure, and optimizes customer engagement so that every interaction is strategic, relevant, and revenue-driven. 

Dynamic Sales Playbook generation

An LLM-supported sales playbook adapts continuously and in real-time to market trends, competitor activities, and customer preferences. Providing up-to-date insights ensures that sales agents stay agile, informed, and competitive in rapidly evolving markets. 

How does it help?

With AI-driven intelligence, sales teams can proactively respond to industry changes, refine customer engagement strategies, and maximize conversion opportunities, keeping them one step ahead of the competition. 

Predictive Cross-Selling/Up-Selling Using Customer Genome 

The Customer Genome Framework uses demographic, behavioral, and psychographic insights to predict the most relevant product combinations for each customer. This data-driven approach enables precise marketing with targeted recommendations, such as connected home solutions for IoT users or device insurance for high-end users. 

How does it help?

By matching products to customer needs, telecom providers can increase conversion rates, boost revenue and enhance customer satisfaction through hyper-personalized offers. 

Real-time sentiment analysis

AI-powered sentiment analysis tools capture customers' emotions in real time during calls, chats, and digital interactions. By analyzing tone of voice, word choice, and response patterns, these tools give sales and support staff instant feedback so they can dynamically adapt their approach. 

How does it help?

This real-time adaptability helps defuse frustrations, reinforce positive experiences, and personalize interactions, ultimately leading to higher customer satisfaction, better engagement, and improved conversion rates. 

Omnichannel sales engagement

AI agents enable seamless, consistent interactions across all in-store, online, and mobile channels. By leveraging insights from past customer activity and preferences, telecom providers can ensure a personalized, frictionless experience at every touchpoint. 

How does it help?

This unified approach increases customer satisfaction, strengthens brand loyalty, and optimizes revenue through tailored recommendations and interaction strategies, regardless of channel.

Proactive churn prevention

AI-driven models analyze usage patterns, payment history, and dissatisfaction signals to predict churn likelihood accurately. By identifying at-risk customers early, retention teams receive tailored recommendations to proactively re-engage them through personalized offers, discounts, or exclusive perks. 

How does it help?

This predictive approach helps reduce attrition, strengthen customer loyalty, and maximize lifetime value, ensuring a more engaged and satisfied customer base. 

“AI Investment in Telecommunications- Over 50% of Communication Service Providers (CSPs) have allocated IT budgets for generative AI in 2024, with many reallocating funds from existing digital transformation or AI initiatives.” 

Source: IDC, Generative AI Readiness: Are Telcos Geared Up for the Generative AI Revolution?

Design goals and building blocks for Humanizing personalization 


To deliver humanized personalization at scale, telecom providers need to adopt the following design principles and technology enablers: 

Higher level of personalization across ecosystems

Seamless ecosystem integration 

Integrate first party and partner services to create a unified platform that delivers diverse, connected offerings to maximize customer value. 

Intelligent offers and promotions 

Use contextual, AI-driven promotions — such as dynamic bundling and personalized discounts — to foster emotional connections and build brand loyalty. 

ML-supported next-best-action (NBA) advice 

AI-driven NBA models analyze customer behavior and preferences in real-time and provide precise, personalized recommendations to enhance retention and increase loyalty. 
Humanizing personalization in telecom (2)

Deep knowledge-led data structures

IoT-controlled micro-signals 

Real-time data from device usage patterns and customer interactions help to create detailed customer and product profiles that enable precise targeting and personalization. 

Embedded AI and ML pipelines 

Multimodal data analysis based on AI and machine learning anticipates customer needs, recognizes patterns, and optimizes sales strategies to ensure proactive customer retention. 

Knowledge-led RAG (Retrieval-Augmented Generation) 

Modular AI/ML pipelines support agile adaptation to new data, iterative knowledge formulation and changing customer preferences. The knowledge structures are one of the most effective ways to ground generative AI models.  The ultimate goal is to ensure continuous improvement of recommendations and personalization strategies. 

Deep knowledge-telecom personalization

Agents enabling AI virtualization 

Embedded AI and ML pipelines 

Multimodal data analysis based on AI and machine learning anticipates customer needs, recognizes patterns, and optimizes sales strategies to ensure proactive customer retention. 

Pluggable & modular knowledge development

Modular AI/ML pipelines support agile adaptation to new data and changing customer preferences and ensure continuous improvement of recommendations and personalization strategies. 

Semantic layer across the ecosystem

An enhanced semantic layer, beyond data taxonomy, is critical for developing an effective interface across AI/ML models, data, and existing APIs. Grounding and contextualizing are key to humanizing the experience.   
Telecom personalization- across customer lifecycle

AI Playbook-level governance

Human-friendly interaction 

LLM-driven interactions should be designed to align with the brand’s tone, style, and values. This ensures that AI-generated responses feel engaging, natural, and on-brand while enhancing the customer experience. 

Governance of the enterprise-class 

Governance and security that span across the entire AI ecosystem and include data, APIs, ML models, and LLMs. Integrated observability and explainability ensure transparency, compliance, and continuous model improvements. LLM operations and explainability pipelines are critical components of such governance.  AI-governance must be externalized outside native LLM providers to ensure interoperability.  

 

Read how you can supercharge your enterprise strategy with— Generative AI Playbooks.
AI Playbooks for Telecom

The future of telecommunications is AI-driven, personalized and intelligent.


As telecom providers operate in an increasingly competitive landscape, AI-driven personalization is no longer optional but essential. By integrating agent-based AI, real-time analytics, and human-friendly AI interactions, telcos can go beyond traditional interaction models and create seamless, hyper-personalized experiences that drive customer loyalty and revenue growth. 

With deep, knowledge-based data structures, AI-powered sales intelligence, and enterprise-wide governance, telcos can unlock new opportunities, anticipate customer needs, and optimize interactions at every touchpoint. The transition to intelligent, AI-driven ecosystems is not just about automation but about creating meaningful, data-driven relationships that increase customer satisfaction and long-term value. 

Humanizing personalization at scale

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