Literature Review on Customer Churn Prediction in Telecom Industry

Customer churn is a critical issue in the telecommunications industry. With digital transformation and the growth of customer data, churn prediction models have become an indispensable tool for telecom operators. This paper comprehensively reviews recent research on customer churn prediction in the telecommunications industry. Common datasets are examined, including public benchmarks such as IBM Telco and Cell2Cell, as well as large-scale proprietary datasets. Four categories of approaches are discussed: traditional machine learning, ensemble and hybrid methods, deep learning, and explainable artificial intelligence (XAI). Ensemble models and deep learning hybrid methods maintain excellent performance, in some cases reporting accuracies exceeding 90%. However, deployments still face common challenges such as class imbalance, overfitting, and limited interpretability. Furthermore, research gaps are identified, including the need for multimodal data integration, real-time prediction, improved interpretability, and privacy-aware methods. By summarizing state-of-the-art techniques and remaining challenges, this review provides guidance for future research and practical implementation of customer churn prediction in the telecommunications sector.