Data-Driven Customer Segmentation & E- Commerce Retail Optimization

The growth of e-commerce businesses is heavily reliant on data-driven decision-making, where customer segmentation and personalized marketing strategies play pivotal roles. This paper proposes a framework for leveraging machine learning and data analytics to optimize e-commerce operations, focusing on customer segmentation and churn prediction. We explore the application of the Random Forest algorithm for predicting customer churn, and RFM (Recency, Frequency, Monetary) analysis for effective customer segmentation. The proposed system also incorporates Customer Lifetime Value (CLV) calculations to forecast the potential revenue from each customer, aiding businesses in resource allocation. By combining machine learning models with a robust Flask-based platform, the system enables real-time analytics, personalized product recommendations, and automated insights for administrators and customers. Security features, including role- based access control, ensure secure data management. Through this framework, e-commerce businesses can enhance customer engagement, reduce churn, and drive revenue growth by making informed, data-backed decisions.