Explainable AI and Machine Learning Approaches for Telecom Churn Prediction and Business Intelligence

Abstract

This study explores the integration of explainable AI (XAI) with advanced machine learning models to enhance both predictive accuracy and interpretability in telecom churn prediction. The comparative analysis of machine learning models demonstrates that advanced algorithms such as XGBoost and neural networks outperform traditional models like logistic regression and random forests. Neural networks achieve the highest prediction accuracy, indicating their ability to capture complex nonlinear relationships in customer behavior. However, these models often lack transparency, limiting their practical application in business environments. To address this challenge, the study incorporates explainable AI techniques, specifically feature importance analysis using SHAP values. The results reveal that key factors influencing churn include contract type, monthly charges, and customer tenure. This insight provides actionable information for decision-makers, enabling targeted customer retention strategies. Additionally, the analysis of churn rates across customer segments highlights that low-usage customers exhibit significantly higher churn rates compared to high-usage customers. This finding underscores the importance of segmentation-based strategies in reducing churn and improving customer engagement. Overall, the integration of explainable AI with machine learning models provides a comprehensive approach to telecom churn prediction. By combining high predictive accuracy with interpretability, the proposed framework enhances business intelligence capabilities and supports data-driven decision-making. The study contributes to the development of transparent and effective AI systems for customer analytics in the telecommunications industry.