Customer Churn Prediction in the Telecommunications Sector Using Explainable Artificial Intelligence

Abstract

: Customer churn poses a significant threat to profitability in the saturated telecommunications industry, yet accurately predicting churn remains challenging due to high-dimensional data and class imbalance where churners represent a small minority. While complex ensemble methods achieve high accuracy, their ”black-box” nature limits business adoption, as practitioners require transparent insights to design effective retention campaigns. This paper proposes a comprehensive Machine Learning pipeline that bridges the gap between predictive performance and model interpretability. We implement and compare five classifiers—Logistic Regression, Random Forest, XGBoost, LightGBM, and CatBoost—optimized via Bayesian hyperparameter tuning and evaluated using the recall-focused F2-score to address class imbalance. Our results demonstrate that gradient boosting models, particularly XGBoost, outperform aggregation-based ensemble strategies, achieving the highest F2-score of 0.7500 with a recall of 0.9385. Crucially, we integrate SHAP-based Explainable Artificial Intelligence to provide both global and local interpretability, revealing that contract type, tenure, and technical support subscriptions are the primary churn drivers. This dual-layer transparency enables targeted retention strategies while preserving high recall in imbalanced telecom data.