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arXiv AI By Sandeep Gaddamwar

Explainable Artificial Intelligence for Customer Churn Prediction in Telecommunications: A Framework for CRM Integration

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The paper presents a framework for integrating explainable AI into customer churn prediction for telecommunications. It benchmarks four classifiers—Logistic Regression, Random Forest, XGBoost, and LightGBM—on the IBM Telco Customer Churn dataset, finding comparable performance with Logistic Regression achieving the highest AUC-ROC and LightGBM the highest accuracy. Explanations are provided via SHAP and LIME at both global and instance levels, and a four‑layer CRM integration architecture is proposed to translate risk scores and attribution vectors into actionable retention strategies, projecting a 3.3–5.3 percentage point reduction in churn and $199K–$319K savings per campaign cycle.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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