Developing Predictive Models for Customer Behavior in ECommerce

Abstract

This manuscript presents a comprehensive study on developing predictive models to understand and forecast customer behavior in the dynamic realm of ecommerce. With the exponential growth of online transactions and the availability of granular customer data, businesses are increasingly turning to machine learning and data analytics to predict customer preferences, purchasing patterns, and churn likelihood. Our research integrates data preprocessing, feature engineering, and the application of various predictive algorithms such as logistic regression, decision trees, and ensemble methods to evaluate model performance. The study demonstrates that predictive analytics not only enhance customer segmentation and targeted marketing strategies but also improve overall customer satisfaction by anticipating user needs. A detailed statistical analysis, including performance metrics summarized in a comparative table, underscores the reliability of the proposed models. We conclude with an evaluation of key findings, limitations of the current approach, and directions for future research in the field.