Investigation in Customer Value Segmentation Quality under Different Preprocessing Types of RFM Attributes

Abstract

Customer value segmentation helps retailers to understand different types of customers, develops long term relationship with them, and hence increases their value and loyalty. This study aims to evaluate the quality of customer value segmentation based on two methods of preprocessing the RFM attributes. K-means clustering algorithm is used for the customer value segmentation based on the scored RFM and the actual value of RFM. The quality of the clustering results is tested using the Sum of Squared Error (SSE). Results obtained show that using the actual value of RFM in customer segmentation reduces the clustering error (SSE) and enhances the accuracy of segmentation than using the scored RFM.