Segmentasi Pelanggan Ritel Global dan Inggris Menggunakan RFM dan K-Means Clustering
DOI:
https://doi.org/10.61132/prosemnasproit.v2i2.84Keywords:
customer segmentation, RFM, K-Means, clustering, online retail, UK marketAbstract
Transaction logs in online retail provide opportunities for data-driven customer segmentation. This study segments customers at two scopes global (all countries) and United Kingdom (UK) using Recency, Frequency, and Monetary (RFM) features derived from the Online Retail transaction dataset. After cleaning cancellations and invalid records, RFM variables are computed per customer and normalized. K-Means clustering is applied separately for global and UK data, while the number of clusters is selected via the elbow criterion and validated using internal indices. The best configuration for both scopes yields five clusters, with moderate separation quality based on the silhouette score. Cluster profiling indicates distinct groups ranging from low-frequency low-spending customers to highly frequent high-spending customers. The comparison between global and UK segmentation shows similar structural patterns, yet different proportions across segments, supporting targeted retention and value-driven marketing actions.
References
Alves Gomes, D., & Meisen, T. (2023). A review on customer segmentation methods for personalized customer targeting in e-commerce use cases. Information Systems and e-Business Management, 21, 527–570. https://doi.org/10.1007/s10257-023-00640-4
Anitha, P., & Patil, M. M. (2022). RFM model for customer purchase behavior using K-Means algorithm. Journal of King Saud University – Computer and Information Sciences, 34(5), 1785–1792. https://doi.org/10.1016/j.jksuci.2019.12.011
Christy, A. J., Umamakeswari, A., Priyatharsini, L., & Neyaa, A. (2021). RFM ranking—An effective approach to customer segmentation. Journal of King Saud University – Computer and Information Sciences, 33(10), 1251–1257. https://doi.org/10.1016/j.jksuci.2018.09.004
Gustriansyah, R., Suhandi, N., & Antony, F. (2020). Clustering optimization in RFM analysis based on k-means. Indonesian Journal of Electrical Engineering and Computer Science, 18(1), 470–477. https://doi.org/10.11591/ijeecs.v18.i1.pp470-477
Handoko, A. F., & Wibowo, A. (2020). Three-layer data clustering model for multi-view customer segmentation using K-Means. International Journal of Recent Technology and Engineering, 8(6), 1840–1845. https://doi.org/10.35940/ijrte.F7962.038620
John, J. M., Shobayo, O., & Ogunleye, B. (2023). An exploration of clustering algorithms for customer segmentation in the UK retail market. Analytics, 2(4), 809–823. https://doi.org/10.3390/analytics2040042
Kanchanapoom, K., & Chongwatpol, J. (2023). Integrated customer lifetime value and customer migration model to improve customer segmentation and marketing strategies. Journal of Marketing Analytics, 11, 172–185. https://doi.org/10.1057/s41270-022-00158-7
Pai, P.-Y., Lin, S.-W., & Lu, W.-M. (2025). Integration of association rule mining and RFM analysis with machine learning for e-commerce customer value segmentation: A sustainable retail perspective. Quality & Quantity. Advance online publication. https://doi.org/10.1007/s11135-025-02259-8
Rungruang, C., Riyapan, P., Intarasit, A., Chuarkham, K., & Muangprathub, J. (2024). RFM model customer segmentation based on hierarchical approach using formal concept analysis. Expert Systems with Applications, 241, 122605. https://doi.org/10.1016/j.eswa.2023.121449
Safari, F., Safari, N., & Montazer, G. A. (2016). Customer lifetime value determination based on RFM model. Marketing Intelligence & Planning, 34(4), 446–461. https://doi.org/10.1108/MIP-03-2015-0060
Shutaywi, M., & Kachouie, N. N. (2021). Silhouette analysis for performance evaluation in machine learning with applications. Entropy, 23(6), 759. https://doi.org/10.3390/e23060759
Sivaguru, R., & Punniyamoorthy, M. (2020). Modified dynamic fuzzy c-means clustering algorithm—Application in dynamic customer segmentation. Applied Intelligence, 50, 1922–1942. https://doi.org/10.1007/s10489-019-01626-x
Wong, C.-G., Tong, T.-C., & Haw, D.-S. (2024). Exploring customer segmentation in e-commerce using RFM analysis with hierarchical clustering and K-Means clustering. Journal of Telecommunications and the Digital Economy, 12(3), 97–125. https://doi.org/10.18080/jtde.v12n3.978
Wu, F.-X., Shi, B.-S., Lin, C.-J., Tsai, L.-C., Li, R.-H., Yang, C.-Z., & Xu, J. (2022). Research on segmenting e-commerce customer through an improved K-medoids and RFM model. Computational Intelligence and Neuroscience, 2022, Article 9930613. https://doi.org/10.1155/2022/9930613
Talaat, F. M., Aljadani, A., Alharthi, B., Farsi, M. A., Badawy, M., & Elhosseini, M. (2023). A Mathematical Model for Customer Segmentation Leveraging Deep Learning, Explainable AI, and RFM Analysis in Targeted Marketing. Mathematics, 11(18), 3930. https://doi.org/10.3390/math11183930
Zhou, F., Wei, Q., & Xu, M. (2021). Customer segmentation by web content mining. Journal of Retailing and Consumer Services, 61, 102588. https://doi.org/10.1016/j.jretconser.2021.102588
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Prosiding Seminar Nasional Ilmu Teknik
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
