Explaining customer churn prediction in telecom industry using tabular machine learning models

Abstract

The study addresses customer churn, a major issue in service-oriented sectors like telecommunications, where it refers to the discontinuation of subscriptions. The research emphasizes the importance of recognizing customer satisfaction for retaining clients, focusing specifically on early churn prediction as a key strategy. Previous approaches mainly used generalized classification techniques for churn prediction but often neglected the aspect of interpretability, vital for decision-making. This study introduces explainer models to address this gap, providing both local and global explanations of churn predictions. Various classification models, including the standout Gradient Boosting Machine (GBM), were used alongside visualization techniques like Shapley Additive Explanations plots and scatter plots for enhanced interpretability. The GBM model demonstrated superior performance with an 81% accuracy rate. A Wilcoxon signed rank test confirmed GBM’s effectiveness over other models, with the $p$-value indicating significant performance differences. The study concludes that GBM is notably better for churn prediction, and the employed visualization techniques effectively elucidate key churn factors in the telecommunications sector.