Customer churn is a very big challenge for organizations because the loss of already available customers can negatively affect revenue, customer lifetime value, and long-term business growth. The increasing the already available customer demographic, transactional, behavioral, and service-related data has created opportunities for machine learning techniques to identify customers who are dislike a product or service. However, conventional machine learning models often focus on predictive performance and may provide limited insight into the reasons behind individual predictions. This lack of interpretability can reduce user trust and make it difficult for organizations to convert churn predictions into effective retention strategies. This paper proposes an Explainable Artificial Intelligence (XAI)-based customer churn prediction and retention decision support framework that combines machine learning prediction, model explainability, customer risk profiling, and retention recommendations. 

The proposed approach evaluates suitable machine learning algorithms for predicting customer churn and employs SHapley Additive exPlanations (SHAP) to identify the contribution of individual customer features to model predictions. The system provides both global explanations, which identify the most influential factors across the customer population, and local explanations, which describe the factors contributing to an individual customer's churn risk. Based on predicted churn probability and influential characteristics, customers can be categorized according to their level of risk and appropriate retention strategies can be considered. The proposed framework aims to bridge the gap between predictive analytics and practical customer relationship management by transforming model predictions into interpretable and actionable decision support. The approach can assist organizations in identifying high-risk customers, understanding major churn drivers, prioritizing retention efforts, and improving data-driven customer management.