Abstract
Companies or entrepreneurs must better understanding customers data in all aspects, including detecting similarities and differences among customers, predicting their behavior, and offering better options and opportunities to customers. Customer segmentation is carried out to obtain this information, which is part of CRM (Customer Relationship Management). One of the general models in the application of customer segmentation is the RFM (Recency, Frequency, and Monetary) model. This research method uses a combination of the RFM model and clustering. RFM is used as a description of customer behavior in conducting transactions. Clustering is a process that is widely used and is designed to categorize data. Clustering uses the K-Means Algorithm to determine the number of clusters using the Elbow and Silhouette methods. The application of RFM analysis and the K-Means resulted in two customer segments, namely potential customers and non-potential customers. Potential customers have the characteristics of frequent transactions and also large expenses. Non-potential customers have the characteristics of infrequent transactions and also standard expensesKeywords: Customer Segmentation, RFM Model, K-Means Clustering
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