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November 23, 2025
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Abstract

This study aims to segment customers of the cat grooming service at Mocha Petshop using the K-Means Clustering algorithm and the RFM (Recency, Frequency, Monetary) model. Using the Elbow method and Davies Bouldin Index (DBI) evaluation, three optimal clusters were identified with a DBI value of 0.507. The results of the study show that there are three segments identified: Best Customers with high loyalty, Ordinary Customers with moderate loyalty, and Lost Customers with low loyalty. Based on this segmentation, a marketing strategy was designed using the STP (Segmentation, Targeting, Positioning) approach. For Best Customers, the strategy focuses on exclusive services and reward points, while for Ordinary and Lost Customers, the strategy includes education, discounts for first visits after inactivity, and discounts for subsequent services. Increasing transaction frequency is key to improving loyalty and moving customers to the Best Customer segment, which ultimately increases retention or transaction frequency at Mocha Petshop.

Keywords

Customer Segmentation K-Means Clustering RFM Model Elbow Method Davies Bouldin Index

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Arnanda, M. A., Rakib, M., & Aris, V. (2025). Customer Segmentation Using the K-Means Clustering Algorithm for Marketing Strategy Design. Journal Applied of Economics, Mathematics, Computer Science, and Data Science in Business, 1(1), 21–31. Retrieved from https://onmind.bisdig.feb.unm.ac.id/index.php/ecommerce/article/view/495
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