Explainable AI/ML Models for Customer Retention Optimization in the Telecom Sector

Customer churn remains one of the costliest operational problems for telecommunications providers. Predictive models that detect at-risk customers can materially reduce churn, but black-box algorithms hinder operational adoption because product teams, regulators and customer-facing staff need understandable reasons for automated recommendations. This paper presents an end-to-end, explainable machine learning pipeline for customer retention optimization that combines tabular usage/billing features with short support-call text, evaluates interpretable and high-performance learners (logistic regression, Random Forest, XGBoost), and applies both local (LIME) and global (SHAP/feature importance) explainers. We propose reproducible preprocessing, stratified cross-validation, and stability measures for explanations, and we show how explanations feed a policy simulator that converts predictions into intervention decisions (discounts, proactive calls, tailored bundles). Using a mixed tabular + text telecom dataset, the pipeline demonstrates strong discrimination (AUC) for ensemble models while retaining actionable local explanations for top-risk cases. We report mock quantitative results and human evaluation metrics (actionability, trust, explanation consistency) to illustrate typical outcomes and trade-offs. Finally, we discuss operational integration, ethical safeguards, and deployment recommendations for a hybrid scoring+ explainability architecture. Implementation details and methodology choices follow established enterprise XAI practices and the uploaded methodological reference.

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