Published Paper Details:

Customer retention is a critical metric for business sustainability and growth, particularly in competitive market environments where acquiring new customers incurs significantly higher costs than retaining existing ones. This study presents a data-driven approach to enhancing customer retention strategies using RFM (Recency, Frequency, Monetary) segmentation, integrated with predictive analytics. By analyzing historical transaction data through RFM metrics, customers were segmented into distinct clusters to uncover behavioral patterns and purchase tendencies. Predictive modeling techniques, such as decision trees and logistic regression, were employed to identify key drivers influencing customer churn and loyalty. The outcomes were visualized through interactive dashboards using Tableau to support strategic decision-making. This research contributes to the field of business analytics by demonstrating how predictive RFM segmentation can provide actionable insights for improving marketing effectiveness and increasing customer lifetime value. The approach is particularly valuable for organizations seeking scalable solutions to personalize retention strategies and foster long-term customer engagement.