Survival Analysis for Churn Prediction in Telecom Sector Using Machine Learning

Churning customer causes a significant concern in the telecommunication department, where the loss of customers directly impacts revenue. Predicting churn accurately enables telecom companies to implement effective retention strategies, reducing potential revenue losses. Survival analysis, traditionally used in medical research to model time-to-event data, has gained traction in the telecom sector for predicting when a customer is likely to churn. This survey reviews the application of survival analysis in conjunction with machine learning models for churn prediction, outlining key methodologies, datasets, performance metrics, and challenges.