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All Journal Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA)
M.Khaerul Ihsan
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Computer Science & IT

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Application of RFM Model and K-Means Algorithm in Customer Loyalty Segmentation of Indonesian Regional Water Utility Company (PDAM) Hairani, Hairani; Wahyudi, Rizki; Gede Yogi Pratama; M.Khaerul Ihsan
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 5 No. 1 (2026): March 2026 (In Press)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v5i1.6087

Abstract

Customer loyalty is an important factor in maintaining the sustainability of the Regional Drinking Water Company (PDAM) business, because it directly affects customer retention and company revenue. However, PDAM faces challenges in understanding the diverse patterns of customer loyalty. Therefore, this study aims to group customer loyalty based on using Recency, Frequency, Monetary (RFM) analysis and the K-Means clustering method. The methods used are RFM and K-Means which use 20,000 PDAM customer transaction data. Based on the results of the RFM analysis which was then clustered with K-Means, three customer loyalty clusters were obtained, namely High Value customers (Cluster 0) with 4900 data, Medium Value (Cluster 2) with 10,600 data, and Low Value (Cluster 1) with 4500 data. The results of the analysis show that the High Value cluster consists of customers who have recently made transactions, have a high purchase frequency, and spend large purchase values. On the other hand, the Low Value cluster includes customers with low recency, frequency, and monetary values, indicating a low level of loyalty. The implication of this study is that PDAM customer segmentation using RFM analysis and the K-Means algorithm produces three loyalty clusters, namely High Value, Medium Value, and Low Value, which allow the company to design more targeted service and marketing strategies according to the characteristics of each customer group.
Co-Authors Gede Yogi Pratama Hairani Hairani Rizki Wahyudi