Customer Segmentation Using the K-Means Algorithm for Customer Intelligence and Marketing Strategy in an Online Retail Company (Case Study: Online Retail Dataset II)
Keywords:
Customer Segmentation, RFM analysis, K-Means clustering, Customer Intelligence, Online retailAbstract
Customer intelligence is crucial for online retail companies to design targeted marketing strategies and increase customer
retention. This study aims to segment customers based on purchasing behavior using RFM analysis and K-Means
clustering. The Online Retail II dataset containing more than 525,000 transactions from December 2010 to December
2011 was used. After data cleaning, outlier handling, and RFM feature engineering, the Elbow Method and Silhouette
Score determined the optimal number of clusters (K=3). The results produced three distinct customer segments: Lost/At
Risk (25.17%), Regular Customers (74.32%), and High-Value Loyal (0.51%). Actionable business recommendations were
formulated for each segment. This segmentation provides deeper customer intelligence and supports more effective
marketing strategies for online retail businesses.
References
Achmad, S. L., Fauzi, A., Rahmat, R., & Indra, J. (2024). SEGMENTASI PELANGGAN MENGGUNAKAN K-MEANS CLUSTERING DI TOKO RETAIL. Jurnal Teknik Informasi Dan Komputer (Tekinkom), 7(2), 736. https://doi.org/10.37600/tekinkom.v7i2.1226
Fadhilla, D. R., Ramadhan, R., Vedliyan, V., & Oktavian, D. (n.d.). Segmentasi Pelanggan Berbasis RFM dengan Algoritma K-Means Pada Data Transaksi Online Retail (Vol. 2, Number 3).
Fauzan, R. M., & Alfian, G. (2024). Segmentasi Pelanggan E-Commerce Menggunakan Fitur Recency, Frequency, Monetary (RFM) dan Algoritma Klasterisasi K-Means. In Jurnal Informatika Sunan Kalijaga) (Vol. 9, Number 3).
Febrina, Y. K., Saputra, R., & G, K. F. (2025). Segmentasi Pelanggan Toko Hanifah Berdasarkan Analisis RFM dengan Metode K-Means Clustering. Jurnal Pustaka AI (Pusat Akses Kajian Teknologi Artificial Intelligence), 5(2),
274–282. https://doi.org/10.55382/jurnalpustakaai.v5i2.1084
Hairani, H., Wahyudi, R., Gede Yogi Pratama, & M.Khaerul Ihsan. (2026). Application of RFM Model and K-Means Algorithm in Customer Loyalty Segmentation of Indonesian Regional Water Utility Company (PDAM). International Journal of Engineering and Computer Science Applications (IJECSA), 5(1), https://doi.org/10.30812/ijecsa.v5i1.6087 1–8.
Karimah, M., & Marwati, F. (2024). Sustainability of Quality Management by Implementing Data Mining to Predict Academic Achievement. Journal of Social Science and Business Studies, 2(3), 240–250. https://doi.org/10.61487/jssbs.v2i3.90
Martiansah, R., Monalisa, S., Muttakin, F., & Fronita, M. (2025). Customer Segmentation Analysis Through RFM-D Model and K-Means Algorithm. In Jurnal Sistem Cerdas.
Nurlathifa, A., & Sucahyati, D. (2025). Segmentasi Pelanggan
menggunakan Metode K-means Clustering Berdasarkan Model RFM pada Bisnis Food and Beverage (Studi Kasus: Coffee shop X). Jurnal Nasional Teknologi Dan Sistem Informasi, 11(3), 218–225. https://doi.org/10.25077/teknosi.v11i3.2025.218-225
Sitorus, E. R., & Nugraha, I. (2025). Analisis segmentasi pelanggan dengan model RFM (Recency, Frequency, Monetary) dan K-Means Clustering (Studi kasus: PT XYZ). Jurnal Teknik Industri Terintegrasi, 8(1), 266–278. https://doi.org/10.31004/jutin.v8i1.39447
Triantara, I. K. Y., Raharja, M. A., & Sarasvananda, I. B. G. (2025). Segmentasi Pelanggan Berbasis RFMT Menggunakan K-Means dan Hierarchical Clustering I Komang Yosua Triantara a1 , Made Agung Raharja a2, Ida Bagus Gede Sarasvananda a3. JNATIA, 3(4).
Yusak, A., Rumapea, N., Pratiwi, D., & Sari, S. (2024). Analisis Segmentasi Pelanggan Ritel Online Menggunakan K-Means Clustering Berdasarkan Model Recency, Frequency, Monetary (RFM). Jurnal Sains Dan Teknologi, 6(3), 292–299. https://doi.org/10.55338/saintek.v6i3.4607
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Copyright (c) 2026 Muhammad Naufal Skha Yusfa, Ahyat Musyawwa, Aldy Bifal Pratama (Author)
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