RFM-AR Model for Customer Segmentation using K-Means Algorithm

Faculty of Engineering, Universitas Krisnadwipayana Jakarta, Indonesia

^* Corresponding author: alikhumaidi@unkris.ac.id

Abstract

Competition in the business field is getting tougher, business people are required to carry out various strategies and innovations in order to compete with their competitors. Business actors are not only focus on transaction convenience and product centric strategies, but also need to carry out customer centric strategies. Segmentation is part of a customer centric strategy by knowing the characteristics of customers with similarities. In conducting customer segmentation, previous studies mostly used RFM (Recency, Frequency, Monetary) and clustering methods. This research will add AR (Age, Return) to the model, so the method used in this research is CRISP-DM (Cross Industry Process for Data Mining) with a combination of RFM-AR model and K-Means clustering. The result of this research is a data clustering modeling with 3 types of customer clusters with different characteristics. Determination of the best number of clusters with the elbow method can produce the same number of K clusters on different amounts of data. The optimal K value for each RFM-AR variable is K=2. Clustering is divided into 3 grades are high, middle and low.