Strategic Customer Lifetime Value Prediction: Leveraging Machine Learning to Maximize Profitability in Retail

Strategic Customer Lifetime Value Prediction: Leveraging Machine Learning to Maximize Profitability in Retail

This paper introduces a machine learning-based framework for predicting Customer Lifetime Value (CLV) and performing strategic customer segmentation using real-world online retail data. By combining RFM analysis with K-Means clustering and advanced models such as XGBoost, the study demonstrates how data-driven insights can identify high-value customer segments, improve retention strategies, and drive sustainable profitability.

📄 Read the full paper on SSRN: https://dx.doi.org/10.2139/ssrn.5291821

📊 Online Retail Data Set — UCI Machine Learning Repository: https://archive.ics.uci.edu/dataset/352/online+retail