Segmentasi Konsumen Berdasarkan Model Recency, Frequency, Monetary dengan Metode K-Means

Abstract

A good company is a company that is responsive to market changes and opportunities by utilizing existing data and information. Company data and information can come from internal or external sources. One of the internal data sources that can be utilized is customer data. This data will be used as the basis for determining customer segmentation. Segmentation is a process to determine customer characteristics with certain similarities, making it easier to extract information related to profitable customers. Customer business behavior can be seen from recency (last transaction period), frequency (number of transactions), and monetary (rupiah issued) or known as RFM analysis. The effective RFM analysis helps achieve the implementation of customer relationship management because this model is an important facility in measuring the profitability of customer value. To consider this RFM model, researchers use clustering which assumes that customers are in the same cluster, then consider customers with customers in the cluster. This clustering will display customer segmentation. This clustering method uses K-Means clustering. From the results of data processing, 3 clusters were formed from 25 customer data. Based on the clusters formed, it can be concluded that customer purchases have a different pattern. Clusters included in the segment of potential customers are cluster 1. Clusters are needed to get customers who previously had low R, high F, and high M values. While the strategy that needs to be improved is cluster 2.