Customer Segmentation Based on RFM Value Using K-Means Algorithm

Customer segmentation is a marketing strategy in improving customer relationships. Customer loyal behavior towards a product or service will greatly benefit the company because customers will continue to look for the product they want. Many companies do not have a segmentation system to know the type of customers and measure customer value, even though the potential of data can be used for company profits. The purpose of this study is to find out the type of customer and measure the value of the customer so that the business owner can determine which customer gives the greatest benefit and which customer does not provide benefits. Identification of customer criteria in cluster formation based on RFM (Recency, Frequency and Monetary) values called clustering. This grouping method uses the K-Means Clustering algorithm. The results of the Elbow Method are seen from the SSE (Sum Squard Error) of several cluster numbers. The value of the SSE difference is 2.7630 and the average Silhouette Index is 0.7210 in cluster 3. This can provide customer grouping results based on accurate RFM values in customer segmentation.

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