Explainable Machine Learning for Telecom Customer Churn Prediction and Actionable Retention Strategies
DOI:
https://doi.org/10.65455/1z4v1627Keywords:
Explainable Machine Learning, SHAP, Logistic Regression, Telecom Operations, Customer Retention StrategiesAbstract
Customer churn is a critical issue in customer relationship management (CRM) in the telecommunications industry. Accurately identifying high-risk customers and providing actionable intervention recommendations is crucial for improving customer lifetime value and reducing operating costs. This paper addresses the task of predicting customer churn in the telecommunications industry by constructing an interpretable machine learning analysis framework of "prediction-intervention." On the Telco customer dataset containing 21 customer features, the discriminative performance of three models—XGBoost, Random Forest, and Logistic Regression—is compared. SHAP (SHapley Additive exPlanations) is introduced into the optimal model family to achieve interpretability analysis at both the global and individual levels. Experimental results show that the performance of the three models is similar, with Logistic Regression achieving the highest AUC (0.835) and F1 (0.593), while XGBoost and Random Forest have AUCs of 0.833 and 0.829, respectively. Confusion matrix analysis reveals that the main bottleneck under the current setup is false negative reporting (FN), indicating the need for threshold and cost-sensitive optimization. SHAP results indicate that contract type, tenure, online security, monthly charges, and technical support are the most critical churn drivers. Furthermore, this paper proposes a three-tiered, precise retention strategy targeting different risk levels and driving factors, providing a practical reference for deploying explainable AI-driven churn management systems in real-world telecom operations.
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