Customer Segmentation based on the RFM Analysis Model using K-Means Clustering Technique: A Case of IT Solution and Service Provider in Thailand

Abstract

Customer segmentation is crucial for every business to better understand their customers, to keep customers satisfied, and to develop personalized products and services. In this research, a case study of using data mining techniques to segment customers for an Information Technology (IT) solution and service provider in Thailand is presented. The objectives of this research are to construct a customer segmentation model based on customer demographics and purchase behaviours and to help business better understand its customers and support their customer-centric marketing strategy. The proposed segmentation model is regarding to the customers demographic data and Recency, Frequency, and Monetary (RFM) values generated from purchase behaviours, customers have been segmented using the K-means clustering technique into numerous groups based on their similarity, and the profile for each group is identified based on their characteristics. Accordingly, recommendations are provided to the business on marketing strategy and further analysis. RapidMiner Studio, data mining tool, is used in this research.