AI-Driven Predictive Analytics in Telecom CRM for Customer Lifetime Value Enhancement

Abstract

Customer retention presents a critical and costly challenge within the highly competitive telecommunications sector. Traditional methods for calculating Customer Lifetime Value (CLV), which rely predominantly on historical data and generalized behavioral views, are demonstrably insufficient for managing dynamic market conditions and complex customer interaction patterns.2 This white paper details a strategic framework for leveraging Artificial Intelligence (AI) and Machine Learning (ML) techniques to transition from reactive customer management to proactive, predictive engagement integrated within Customer Relationship Management (CRM) workflows. The report synthesizes findings from recent research, illustrating the superior performance of advanced models such as Support Vector Machines (SVM), which have achieved accuracy levels of 97% in churn classification, substantially outperforming statistical baselines. Furthermore, it addresses the successful application of Gradient Boosting frameworks (specifically XGBoost and LightGBM) in forecasting future purchasing behavior and CLV.4 The analysis emphasizes that successful AI integration requires not only predictive power but also alignment with crucial business Key Performance Indicators (KPIs) and a commitment to model interpretability and strict ethical governance, thereby enhancing resource optimization, personalization strategies, and sustainable long-term revenue growth.