Customer Segmentation Using a Hybrid Approach of K-Means Clustering and RFM: An Online Retail Case Study
DOI:
https://doi.org/10.53848/jlsco.v12i2.299053Keywords:
Customer segmentation, Machine learning, K-Means clustering, RFM, Online RetailAbstract
Traditional customer segmentation methods are inadequate for understanding complex consumer behaviors, as online shopping requires extensive data analysis. This research aims to formulate a hybrid customer segmentation methodology by amalgamating RFM scoring with K-Means clustering, employing a dataset from a genuine online retail transaction consisting of 406,573 records. The research methodology encompasses data cleansing and preprocessing, the computation of the Recency, Frequency, and Monetary (RFM) variables, and data normalization. We use RFM scoring to put customers into five groups and K-Means clustering with the Elbow method to find the best number of clusters to put customers into four groups. The findings demonstrate that RFM produces distinct and intelligible groups relevant to practical situations. K-Means, on the other hand, wants to find deeper structural differences between customers. This is especially helpful for finding customer groups that are worth a lot. Using both methods together makes customer segmentation more accurate and complete. Online stores can use this information to plan their marketing, keep customers, and increase the value of their business.
References
Awaliyah, D. A., Prasetiyo, B., Muzayanah, R., and Lestari, A. D. (2024). Optimizing customer segmentation in online retail transactions through the implementation of the K-means clustering algorithm. Scientific Journal of Informatics, 11(2), 539-548. https://doi.org/10.15294/sji.v11i2.6137
Blattberg, R. C., Kim, B.-D., and Neslin, S. A. (2008). Database marketing: Analyzing and managing customers. Springer.
Cooil, B., Aksoy, L., and Keiningham, T. L. (2008). Approaches to Customer Segmentation. Journal of Relationship Marketing, 6(3–4), 9–39. https://doi.org/10.1300/J366v06n03_02
Das, S., and Biswas, B. (2025). Machine learning segment customers based on their reviews in e-commerce portals: A fresh inquiry. The Lumbini Journal of Business and Economics, 12(2), 120–130. https://doi.org/10.3126/ljbe.v12i2.77422
Hennig, C. (2007). Cluster-wise assessment of cluster stability. Computational Statistics and Data Analysis, 52(1), 258–271. https://doi.org/10.1016/j.csda.2006.11.025
Kajornkasirat, S., Gunglin, P., Puangsuwan, K., and Kaewsuwan, N. (2024). Customer segmentation using association rule mining on retail transaction data. IAES International Journal of Artificial Intelligence, 14(3), 1919–1929. http://doi.org/10.11591/ijai.v14.i3.pp1919-1929
Mahboob Basha, S. K., Akhila, N., Deepak, A., Sindhu, J., and Rohith, K. (2025). Real time customer segmentation using hybrid models for smarter e commerce. International Journal of Engineering Research and Science and Technology, 21(3(1)), 1459–1469. https://doi.org/10.62643/ijerst.v21.n3(1).pp1459-1469
Wedel, M., and Kannan, P. K. (2016). Marketing analytics for data-rich environments. Journal of Marketing, 80(6), 97–121. https://doi.org/10.1509/jm.15.0413
Wedel, M., and Kamakura, W. A. (2000). Market segmentation: Conceptual and methodological foundations (2nd ed.). Kluwer Academic Publishers.
Wong, C.-G., Tong, G.-K., and Haw, S.-C. (2024). Exploring Customer Segmentation in E-Commerce using RFM Analysis with Clustering Techniques. Journal of Telecommunications and the Digital Economy, 12(3), 97-125. https://doi.org/10.18080/jtde.v12n3.978
