IMPLEMENTATION RFM ANALYSIS MODEL FOR CUSTOMER SEGMENTATION USING THE K-MEANS ALGORITHM CASE STUDY XYZ ONLINE BOOKSTORE

Abstract

XYZ online bookstore is one of the companies engaged in the online book sales industry that located in Jakarta, Indonesia, but the marketing strategy given to customers has not been maximized, so it has not been able to increase book purchase transactions. Therefore a customer-centered marketing strategy is needed by implementing Customer Relationship Management, One of the methods that can be applied is customer segmentation. Customer segmentation can be done by implementing a data mining process which carried out by using the K-means clustering algorithm and based on the RFM (Recency, Frequency, Monetary) model. . Determining the number of clusters in the clustering process using the elbow method. Performance tests on cluster results using the silhouette method, and the Calinski-Harabasz index. The results of cluster analysis based on customer value using the RFM Combination and Customer Value Matrix methods show that based on the RFM Combination method produces 3 types of customer characteristics namely loyal customers, new customers, and lost customers. Meanwhile, based on the customer value matrix method, it produces 2 types of customer characteristics namely best customer and uncertain customer.