Customer Churn Prediction Using Machine Learning: A Data-Driven Approach for Customer Retention

Customer churn is becoming a serious concern for companies that operate on subscription-based models, including banking, telecom, e-commerce, and streaming services. Even though many current solutions use machine learning to predict whether a customer may leave, they usually stop at prediction and do not clearly explain the reasons behind it or suggest how to prevent it. Because of this gap, such systems are not very effective in real-world scenarios, where businesses need both accurate predictions and a clear understanding of customer behaviour to take meaningful action.
To tackle this problem, this paper presents SmartChurn, a customer churn prediction and analysis system developed to be both practical and informative. It integrates machine learning with explainable AI to not only estimate the likelihood of churn but also uncover the key factors influencing each prediction through SHAP-based analysis. In addition, the system provides tailored retention strategies based on these insights, helping organizations respond quickly and make better decisions to improve customer satisfaction and reduce churn rates.

Customer Churn Prediction, Explainable AI, Machine Learning, SHAP, Retention Strategies.

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