Customer Segmentation Based On Recency Frequency Monetary Model: A Case Study in E-Retailing

Abstract

Marketing studies have often drawn attention to the importance of customers for businesses that aim to endure in a harsh competitive environment. Customer Relationship Management (CRM) has been a prominent approach in marketing management that aims to improve relationships with customers. A practical implication of the CRM approach is the analysis of customer data to extract value for businesses, as well as customers. In this context, customer segmentation has been a useful task that helps to group customers with similar attributes and designate better-tailored marketing strategies for customer groups. Among a variety of approaches for customer segmentation, Recency Frequency Monetary (RFM) Model stands out as an easy-to-adopt and effective technique. Based on three dimensions regarding the sales data, the RFM Model depends on scoring customers with different approaches. In this study, a prototype software is introduced that helps to apply the RFM technique with two scoring approaches. Moreover, the sales data obtained from an e-retailer has been analyzed for clustering using the prototype software, and clusters discovered with RFM variants were compared using cluster evaluation metrics. Finally, the segments were presented along with relevant offers for marketing strategies.

A Comprehensive Integration of RFM Analysis, Cluster Analysis, and Classification for B2B Customer Relationship Management

“…Score-based classification tables have been widely proposed for RFM to get more analytical approaches. A two-attribute table with R-F and M score columns may segment customers may divide customers into 11 segments with names "Champion", "Loyal", "At Risk", etc (Kabasakal, 2020). In the literature, RFM has been proposed mainly hybrid or integrated with additional methods to achieve more robust analysis.…”