Machine Learning Methods for Proactive Customers Churn Prediction in Mobile Networks: A Case Study

Customer churn is one of the most critical and cost-intensive challenges facing the telecommunications industry today. With the rapid expansion of mobile networks and the saturation of the digital market, retaining existing customers has proven to be significantly more cost-effective than acquiring new ones. The present study proposes a comprehensive, data-driven framework utilizing Machine Learning (ML) techniques to predict customer churn proactively within the Cameroon National Operator (CNO) mobile network. By extracting and preprocessing real-world data from the Evolved Packet Core (EPC), specifically the Home Subscriber Server (HSS), a dataset of 13,937 valid customer records was established from an initial pool of 797,508. To address the inherent class imbalance between active and churned users, the Synthetic Minority Over-sampling Technique (SMOTE) was applied. Four supervised machine learning algorithms were trained and evaluated namely: Logistic Regression, Random Forest, LightGBM, and a heterogeneous Stacking Ensemble classifier. Evaluation metrics demonstrate that the Stacking Ensemble model achieved the highest predictive performance. The model yielded an accuracy of 97.51%, a precision of 97.46%, a recall of 97.54%, and an F1-score of 97.50%. Furthermore, Kaplan-Meier survival analysis and feature importance evaluations revealed that early subscription phases (the first 50 days) and specific financial dynamics (monthly charges and recharge frequencies) are the most critical determinants of customer loyalty. The developed system empowers telecommunication decision-makers to identify at-risk subscribers in real-time, facilitating the deployment of personalized, proactive retention strategies.