Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry

Authors

  • Yohanni Syahra Faculty of Computer Science and Information Technology, Universitas Muhammadiyah Sumatera Utara, Medan, Indonesia. 1Doctoral Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
  • Abdul Fadlil Department Electrical Engineering, Universitas Ahmad Dahlan, Yogyakarta, Indonesia
  • Herman Yuliansyah Department Informatics, Universitas Ahmad Dahlan, Yogyakarta, Indonesia

DOI:

10.33395/sinkron.v9i3.14907

Keywords:

Customer Segmentation, RFM Analysis, K-Means Clustering, Retail Industry, CRM

Abstract

In today’s highly competitive retail landscape, businesses face increasing challenges in retaining customer loyalty and achieving sustainable growth. A common issue, particularly among small and medium-sized enterprises (SMEs), is the absence of a structured method for identifying and categorizing customers based on their value and behavior. This study addresses the challenge by implementing a data-driven customer segmentation approach using Recency, Frequency, and Monetary (RFM) analysis combined with the K-Means clustering algorithm. The research utilized 2,353 transaction records from 369 unique customers collected over three years from a local retail business. After preprocessing and normalizing the RFM values using Min-Max scaling, the Elbow Method was applied to determine the optimal number of clusters, resulting in four distinct customer segments. Cluster 3, labeled “Loyal Customers,” consisted of customers with high purchase frequency and very high spending; Cluster 1 (“Potential Loyalists”) included those with moderate activity; Cluster 0 represented “At-Risk Customers,” and Cluster 2 comprised “One-Time Buyers.” This segmentation framework supports the development of targeted Customer Relationship Management (CRM) strategies, such as loyalty programs and re-engagement campaigns. However, the approach also has limitations, including potential data bias due to the use of static transaction records and the challenge of interpreting clusters without qualitative customer feedback. Despite these constraints, the study demonstrates the practical utility of combining RFM analysis with clustering techniques to extract actionable insights in environments with limited technical infrastructure.

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How to Cite

Syahra, Y., Fadlil, A., & Yuliansyah, H. (2025). Customer Segmentation Using RFM and K-Means Clustering to Support CRM in Retail Industry. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 9(3), 1120-1131. https://doi.org/10.33395/sinkron.v9i3.14907

Issue

Vol. 9 No. 3 (2025): Article Research July 2025

Section

Articles

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Copyright (c) 2025 Yohanni Syahra, Abdul Fadlil, Herman Yuliansyah

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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