Explainable AI Boosts Telco Churn Prediction and Retention

Customer churn poses a significant and costly challenge for telecommunications providers. While predictive models can accurately identify at-risk customers, their "black box" nature often prevents their integration into frontline CRM, as retention specialists need to understand *why* a customer is likely to churn to design effective interventions. This research addresses this gap by proposing a framework for integrating Explainable AI (XAI) into CRM. The study benchmarked four classifiers (Logistic Regression, Random Forest, XGBoost, LightGBM) on a telco churn dataset. All models performed similarly, with Logistic Regression achieving the strongest AUC-ROC and LightGBM the highest accuracy. Crucially, the framework provides explanations at two levels: global SHAP rankings identified tenure, total charges, and month-to-month contracts as key churn signals, while instance-level SHAP and LIME decompositions revealed the specific drivers behind each individual prediction. Building on these explanations, a four-layer CRM integration architecture is introduced. This architecture translates risk scores and attribution vectors into tiered customer segments, maps key features to structured retention action templates, and incorporates campaign outcomes into a retraining feedback loop. Targeting the highest-risk customers is projected to reduce overall churn by 3.3-5.3 percentage points, saving an estimated $199K-$319K per campaign cycle.