Customer Clustering Based on RFM Features Using K-Means Algorithm

Abstract

Offering targeted products and services to customers is the key driver to a successful business. In recent years and with the simplified access and gathering of data, companies are adjusting their marketing strategies to retain and attract new customers. One of the methods organizations adopt, is customer clustering. Customer clustering, as part of Customer Relationship Management, is useful when companies wish to offer services, discounts and targeted advertising campaigns to specific customers based on their preferences. One of the techniques widely used in this task is RFM based clustering using K-Means clustering algorithm. The clusters obtained by the algorithm are then further analyzed to set marketing strategies. In this research we cluster customers of a retail store based on RFM features using K-Means clustering algorithm. For the task, we use the available POS data of the store. Clusters obtained are analyzed using Silhouette analysis technique and compared to the observations in the retail store. We found that one of the clusters indicates possible customer churn while another showed potential loyal customers. These clusters can be used to set special marketing strategies to retain and win back customers.