A Machine Learning-Based Customer Lifetime Va...

A Machine Learning-Based Customer Lifetime Value Prediction System Using RFM Analysis and Full-Stack Web Architecture

Author Name : P. Pavan Kumar, Konchada Bhargav, Kanchipati Sri Rama Chandra Murty, Mala Dasara Rakesh, Mr. V. Govind Rao

DOI: https://doi.org/10.56025/IJARESM.2026.1403260057

ABSTRACT The ability to predict Customer Lifetime Value (CLV) is a strategic asset for businesses seeking to optimize their marketing investments and improve customer retention. This paper presents a comprehensive web-based system that predicts Customer Lifetime Value using machine learning techniques grounded in Recency, Frequency, and Monetary (RFM) feature analysis. The system processes retail transaction datasets, extracts meaningful behavioral features, and applies regression models to estimate the long-term value of individual customers. Two primary machine learning algorithms, namely Linear Regression and Random Forest Regression, were implemented and evaluated using Scikit-learn. Based on the predicted CLV, customers are segmented into High, Medium, and Low value categories to support targeted business strategies.