CUSTOMER CHURN PREDICTION USING MACHINE LEARNING

Abstract

Customer churn is a major concern for organizations in industries such as telecom, banking, e commerce, OTT platforms. Churn occurs when customers discontinue a service or shift to competitors, resulting in revenue loss and higher marketing expenses. Traditional methods for handling churn rely on rule-based analysis and manual monitoring, which are often reactive, less accurate, and costly. This project proposes a machine learning-based churn prediction system that analyzes customer behavior, service usage, and demographic information to identify individuals who are likely to leave. By detecting churn risks in advance, the system enables companies to design personalized retention strategies, reduce unnecessary promotional costs, and improve customer satisfaction. The proposed solution not only improves the accuracy of churn prediction but also enables organizations to take proactive measures, reduce and enhance customer satisfaction. By offering insights into the underlying reasons for churn, it strengthens customer loyalty, minimizes revenue loss, and ensures long-term sustainable growth.