Customer Segmentation Based on Recency Frequency Monetary (RFM) and User Event Tracking (UET) Using K-Means Algorithm

Business competition in e-commerce today is very tight, so every company is competing to increase sales. One is by providing the best customer service, so customer loyalty is formed to continue transacting again. Therefore, companies are required to be able to recognize and establish closeness with their customers. Customer segmentation is an effort to group customers based on customer characteristics and behavior in conducting transactions. With customer segmentation, companies can provide more targeted treatment and services. However, producing the proper customer segmentation requires a complicated data analysis process. In this study, customer segmentation was carried out using the K-Means clustering method based on the Recency, Frequency, and monetary (RFM) model combined with the User Event Tracking (UET) parameter. Based on the results of tests that have been carried out on 1,447,984 transaction data and 932,021 user tracking data, the resulting customer segmentation is divided into 3 (three) groups, namely Platinum (43.9%), Gold (9.5%), and Silver (46.6%). Companies can run different marketing strategies for each of these customer groups.