Explainable Ensemble Machine Learning Framework for Customer Churn Prediction: An Intelligent Business Analytics Approach

Abstract

Customer churn prediction has emerged as one of the key challenges for companies, which aim at increasing their revenue and retaining their customers in long term. Conventional statistical methods usually lack the ability to model complex nonlinear behaviors in contemporary industrial data. Machine learning capabilities offer strong prediction, but often are not transparent enough for the kinds of management decisions involved. The framework presented in this work is an interpretable ensemble machine learning technique for intelligent churn prediction in business intelligence systems. Logistic Regression Decision Tree Random Forest Gradient Boosting XGBoost Voting and Stacking are integrated into the framework. Model’s stability and generalization are leveraged by performing categorical encoding, feature scaling and stratified sampling as well. The evaluation of the model’s performance are Accu-racy, F1-score, ROC-AUC, Cross validation, Confusion matrix analysis and Fea-ture importance interpretation. Our empirical studies show that stacked ensemble models with Gradient Boosting ensemble learners can lead to better classification accuracy than using only single learners. Explainability analysis suggests tenure, monthly charges and total charges are most influential in churn prediction. The resultant method increase predictive confidence and interpretability for strategic decision-making. These results demonstrate the potential expressiveness of explainable ensemble learning for data-driven customer relationship management applications.

DOI: https://doi-ds.org/doilink/04.2026-69377646