Customer Churn Prediction Using Machine Learning

Abstract

Customer churn, the phenomenon where customers cease their relationship with a business, is a critical concern for e-commerce platforms striving for sustained growth and profitability. Predicting churn in advance can empower businesses to implement proactive retention strategies, thereby mitigating revenue loss and enhancing customer satisfaction. In this study, we propose a machine learning-based approach to predict customer churn in e-commerce settings. We begin by collecting extensive data encompassing various customer attributes, transactional history, browsing behavior, and engagement metrics. Leveraging this rich dataset, we employ state- of-the-art machine learning algorithms such as logistic regression, random forests, gradient boosting machines, and neural networks for predictive modeling. Feature engineering techniques are applied to extract meaningful patterns and insights from the raw data, enhancing the predictive performance of the models. By deploying the developed predictive model into production environments, businesses can proactively identify at-risk customers and tailor targeted retention strategies to mitigate churn. The results demonstrate the effectiveness of machine learning in accurately predicting customer churn in e-commerce, enabling businesses to proactively implement retention strategies and enhance customer engagement. Index Terms— Machine learning, E-commerce, Logistic re- gression, Decision tree, Random forest algorithm.