An Explainable Customer Churn Prediction and Retention Framework Using Ensemble Learning and Survival Analysis

This paper has introduced a novel intelligent and explainable customer churn prediction and retention framework that combines machine learning, explainability and business intelligence. Integrating ensemble learning, SHAP based model interpretation and survival analysis, the proposed framework not only achieves higher prediction accuracy but also provides decision making ability, which could also predict customer churn in addition to proposing customer retention strategies based on AI recommendations and estimation of revenue risk. These capabilities ensure its applicability for practical business environment. The proposed framework was able to identify high-risk customers along with increasing model interpretability and operational utility as evidenced in the experiments. In addition, deploying the framework on a Streamlit application provided easy accessibility for business users and supports real time single-customer prediction as well as mass customer analysis.  

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