Predictive Customer Segmentation in Retail Using Machine Learning and Behavioral Analytics

Customer segmentation is so important in business development as it offers profound understanding on customer behavior and customer preferences thus making it easy to carry out effective and personalized marketing plans. Nevertheless, conventional segmentation techniques do not tend to provide sufficient analysis of huge and complex data. Over the recent years the use of machine learning methods revolutionized the sphere of customer segmentation, and it is now possible to perform automated analysis, more accurately, and identify more complex patterns and the main trends. This offers a customer segmentation framework that can be used in predicting customers to the retail industry through combining machine learning algorithms with behavioral analytics. With the Retail_Sales_Dataset, the research uses data preprocessing, RFM (Recency, Frequency, Monetary) analysis, and state-of-the-art models such as the XGBRegressor to categorize the customers into actionable like the loyal, at-risk, and high-value group. XGB Regressor proposed gave better results as its R2 was 0.99, MSE was 1.64 and RMSE was 1.2832 which was better as compared to SVM Regressor and DT. The clustering of customers based on the RFM also provided a deeper segmentation of customers, providing valuable actionable information upon which to market and retain its customers. Altogether, the results suggest XGB Regressor accuracy, scalability, and reliability in retail analytics and the relevance of ethical considerations in the context of customer data utilization and the necessity of working with real-world data in the future.