Customer Segmentation based on RFM model and Clustering Techniques With K-Means Algorithm

Every day there is a transaction process performed by Customer. The process generates a lot of data where there are 82,648 transactions from the month of January-December 2017. This study aims to perform customer segmentation on Nine Reload Credit by utilizing data mining process based on RFM model and by using techniques Clustering. The algorithm used for cluster formation is K-Means algorithm. K-Means produces a visual cluster model with the Rapidminer 5.2 tools that represent the number of customers in each cluster by using RFM (Recency, Frequency, and Monetary) attributes. From 82,648 transactions that were then processed, based on RFM model it resulted in 102 Customers. Furthermore, we analyzed cluster by using K-Means algorithm with the result of 63 Customers in Cluster 1 and 39 Customers in Cluster 2. The result of this research can be used by company to know customer category, and then the company will know how to maintain the customer owned.