Customer Churn Prediction Using Logistic Regression and Behavioral Features

Abstract

Customer churn prediction is important across telecommunications, subscription platforms, retail, banking, and digital services because retaining existing customers is often less costly than acquiring new ones. This paper provides a review of customer churn prediction using logistic regression and behavioral features, aiming to demonstrate how statistical modeling can support customer risk identification and behavioral data interpretation. The review first clarifies the basic concepts of churn, behavioral features, and binary classification, then introduces the mathematical principles, modeling procedures, and evaluation metrics of logistic regression. Through a comparative analysis of four representative empirical studies across banking, telecommunications, retail, and digital subscription services, the paper finds that the applicability of logistic regression depends heavily on feature engineering quality and the degree of linearity in business contexts. Key challenges identified include inconsistent churn definitions, varying data quality, the confounding of correlation with causation, and privacy constraints on data usage. Future research directions such as dynamic behavioral modeling, causal inference, and privacy-preserving analytics are discussed to further enhance predictive reliability and interpretability. This review contributes to understanding how fundamental regression methods can serve as interpretable baseline models in business data science, offering a reference for both researchers and practitioners in customer retention and risk management.