Churn Prediction In Telecom Industry: A Systematic Literature Review

The telecommunications industry is particularly competitive and faces high churn rates, making customer retention a critical challenge. Despite a vast body of research, especially in recent years, studies on churn determinants remain fragmented, lacking a comprehensive synthesis. This study conducts a Systematic Literature Review to consolidate existing knowledge, identifying key churn predictors such as contractual, financial, and behavioral factors. It reviews 50 articles on the topic between 2019 and 2024 from the Web of Science (WoS) database. The review highlights advance in machine learning models, particularly random forest, XGBoost, and neural networks, while also uncovering gaps in explainability, dataset generalizability, and the integration of realtime data. Additionally the study maps research trends, methodological limitations, and emerging approaches, offering a structured foundation for future work. By addressing these gaps, this review guides the development of more robust, interpretable, and industry relevant churn prediction models.