RFM-Based Customer Segmentation Using K-Means Clustering for Marketing Strategy Optimization at Queen Audio
DOI:
https://doi.org/10.30871/jaic.v10i2.12302Keywords:
Customer Segmentation, RFM Model, K-Means Clustering, Marketing Strategy Optimization, Retail AnalyticsAbstract
Queen Audio, a musical instrument retailer, has recently faced declining sales performance leading to excess inventory. To address this issue, this study aims to optimize marketing strategies through customer segmentation based on transaction behavior. The research applies the Recency, Frequency, and Monetary (RFM) model combined with the K-Means clustering algorithm to classify customers according to purchasing patterns. The optimal number of clusters was determined using the Within-Cluster Sum of Squares (WSS) and Silhouette Score evaluation metrics. The dataset consists of 3,200 transaction records from 2,637 customers collected between July and September 2025. The results indicate that two clusters provide the optimal segmentation structure with a Silhouette Score of 0.513, indicating reasonably well-defined clusters. The analysis reveals a distinct high-value customer segment characterized by higher transaction frequency and monetary value compared to the moderate-value segment. These findings provide practical insights for implementing differentiated marketing strategies, including targeted promotions, customer retention programs, and personalized offers to improve sales performance and inventory management. This study contributes to data-driven marketing decision-making by demonstrating the effectiveness of integrating RFM analysis with K-Means clustering in the retail musical instrument industry.
Downloads
References
[1] Rizki, B., N. G. Ginasta, M. A. Tamrin, and A. Rahman, “Customer Loyalty Segmentation on Point of Sale System Using Recency-Frequency-Monetary (RFM) and K-Means,” Jurnal Online Informatika, pp. 130–136, 2020. doi: 10.15575/join.v5i2.511.
[2] Mahendra, M. R., E. Darmanto, and S. Muzid, “Penerapan Metode RFM Analysis dan K-Means Clustering untuk Manajemen Pelanggan pada Zhe Homewear,” Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI), vol. 8, no. 4, pp. 2128–2135, 2025. doi: 10.32672/jnkti.v8i4.9452.
[3] Widhyastuti, L. P. W., I. N. Sukajaya, and K. Y. E. Aryanto, “Customer Profiling Berdasarkan Model RFM dengan Metode K-Means pada Institusi Pendidikan untuk Menunjang Strategi Bisnis di Masa Pandemi Covid-19,” JTIM: Jurnal Teknologi Informasi dan Multimedia, vol. 4, no. 2, pp. 94–108, 2022. doi: 10.35746/jtim.v4i2.232.
[4] Chalechema, S., M. Saini, I. Perla, and A. V. Shivanand, “Customer Segmentation Using K-Means Algorithm and RFM Model,” in Proc. 2023 International Conference on Computational Intelligence and Smart Systems (ICCIS), 2023, pp. 393–398. doi: 10.1109/icccis60361.2023.10425556.
[5] Ikotun, A. M., et al., “K-means clustering algorithms: A comprehensive review,” International Journal of Information Technology & Decision Making, 2023.
[6] Mukhtar, H., I. D. Pramaditya, W. S. Weisdiyanto, S. H. Putra, D. Trimuawasih, and A. A. Rilda, “Algoritma K-Means untuk Pengelompokan Perilaku Customer,” Journal of Software Engineering and Information Systems, vol. 4, no. 2, pp. 96–101, 2021. doi: 10.37859/seis.v4i2.7615.
[7] Grigorova, I., A. Efremov, and A. Karamfilov, “An Automated Machine Learning Framework for Interpretable Customer Segmentation in Financial Services,” International Journal of Financial Studies, vol. 13, no. 4, p. 243, 2025. doi: 10.3390/ijfs13040243.
[8] Triyoga, K. W., P. Widyo Laksono, and R. W. Damayanti, “Optimization of Stock Price Prediction Using Long Short-Term Memory (LSTM) Algorithm and Cross-Industry Standard Process Approach for Data Mining (CRISP-DM),” International Journal of Electronics and Communications Systems, vol. 5, no. 1, pp. 19–30, 2025. doi: 10.24042/ijecs.v5i1.26727.
[9] Sholeh, M., and K. Aeni, “Perbandingan Evaluasi Metode Davies-Bouldin, Elbow dan Silhouette pada Model Clustering dengan Menggunakan Algoritma K-Means,” STRING (Satuan Tulisan Riset dan Inovasi Teknologi), vol. 8, no. 1, pp. 56–65, 2023. doi: 10.30998/string.v8i1.16388.
[10] Abdillah, M. H., “Implementation of Forgy Initialization and K-Means++ Algorithms in the K-Means Clustering Method for Sales Data Analysis of Dazzle Store,” Telematika: Jurnal Telematika dan Teknologi Informasi, vol. 22, no. 2, 2025. doi: 10.31315/telematika.v22i2.14468.
[10] Trapanese, L., et al., “Comparison of K-Means and Hierarchical Clustering Methods in Animal Movement Data,” Animals, vol. 15, no. 22, p. 3246, 2025. doi: 10.3390/ani15223246.
[11] Rousseeuw, P. J., “Silhouettes: A Graphical Aid to the Interpretation and Validation of Cluster Analysis,” Journal of Computational and Applied Mathematics, vol. 20, pp. 53–65, 1987. doi: 10.1016/0377-0427(87)90125-7.
[12] Marisa, F., A. R. Wardhani, W. Purnomowati, A. V. Vitianingsih, A. L. Maukar, and E. W. Puspitarini, “Potential Customer Analysis Using K-Means with Elbow Method,” Jurnal Informatika dan Komputer (JIKO), vol. 7, no. 2, pp. 307–312, 2023. doi: 10.26798/jiko.v7i2.911.
[13] Siagian, N. A., A. Rikki, and P. B. N. Simangunsong, “Clustering Menggunakan Metode K-Medoids dengan Pendekatan Manhattan Distance,” KAKIFIKOM (Kumpulan Artikel Karya Ilmiah Fakultas Ilmu Komputer), vol. 6, no. 2, pp. 169–175, 2024. doi: 10.54367/kakifikom.v6i2.4608.
[14] Perdhana, R. B., & Heikal, J. (2024). Enhancing customer segmentation in online transportation services: a comprehensive approach using K-means clustering and RFM model. Indonesian Interdisciplinary Journal of Sharia Economics (IIJSE), 7(2), 2849-2865.
[15] Rajendran, N. (2025). Enhancing Customer Segmentation and Behaviour Analysis with RFM Clustering: A Machine Learning Approach (Doctoral dissertation, Dublin, National College of Ireland).
[16] Kaewpradit, T. (2025). Optimizing Retail Strategy: A Data-Driven Approach to Customer Segmentation Using RFM Analysis and K-Means Clustering. Available at SSRN 5238097.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Bahar Al Hamid, Trianggoro Wiradinata
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License (Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) ) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
