Document Type : Original Article


1 Assistant Professor, Vali-e-Asr University of Rafsanjan.

2 Associate Professor, Vali-e-Asr University of Rafsanjan.

3 M.A Student, Vali-e-Asr University of Rafsanjan.

4 M.A Student, University Of Science and Technology.


As competition among marketing companies and retailers intensifies, segmenting customers and recommending suitable products has become a critical strategy for maintaining a competitive edge. With the rapid growth of online shopping, customers often make purchasing decisions based on their needs and desires. Salespeople play a crucial role in influencing customers, making a product recommendation system essential. Such a system has various applications and can also encourage customers to purchase additional products. In this study, we present a method for recommending products to customers that utilizes the K-means clustering algorithm and the RFM (Recency, Frequency, Monetary) model to segment customers and make personalized product recommendations. To evaluate the performance of the proposed system, we conducted experiments using data collected from Digikala, an online shopping company. The results show that clustering based on the RFM features has better results for cluster number zero, which represents loyal customers. Therefore, to encourage these customers to purchase higher-priced goods, companies can offer special discounts to cluster number zero. Our approach provides a customer-centric solution for increasing sales and customer satisfaction.


Main Subjects

  1. Angela, H., Chen, L., Yun-Chia Liang, Wan-Ju Chang, Hsuan-Yuan Siauw, Vanny Minanda. (2022). RFM Model and K-Means Clustering Analysis of Transit Traveller Profiles: A Case Study. Journal of Advanced Transportation, vol., Article ID 1108105, 14 pages.
  2. Birukov, A., Blanzieri E., & Giorgini P. (2005). Implicit: An agent-based recommendation system for web search[C]. Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems. ACM, 618-624.
  3. Cheng, C. H., & Chen, Y. S. (2009). Classifying the segmentation of customer value via RFM model and RS theory. Expert systems with applications, 36(3), 4176-4184.
  4. Choi, S. H., Kang, S., & Jeon, Y. J. (2006). Personalized recommendation system based on product specification values [J]. Expert Systems with Applications, 31(3), 607-616.
  5. Davidson, I. (2002). Understanding K-means non-hierarchical clustering. Computer Science Department of State University of New York (SUNY), Albany.
  6. Dibb, S. (1998). Market segmentation: strategies for success. Marketing Intelligence & Planning, MCB UP Ltd, 16(7), 394–406.
  7. Ernawati, E., Baharin, S. S. K., & Kasmin, F. (2020). A review of data mining methods in RFM-based customer segmentation. Journal of Physics: Conference Series, (Malang, Indonesia, Volume 1869), 1-9.
  8. Fader, P. S., Hardie, B. G. S., & Lee, K. L. (2005). RFM and CLV: Using iso-value curves for customer base analysis. Journal of Marketing Research, American Marketing Association, 42(4), 415–430.
  9. 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), 470-475.
  10. Han, J., Kamber, M., Pei, J. (2012). Data Mining: Concept and Techniques (3rd ed.). USA: Elsevier.
  11. Hartini, S., Sigit Kurniawan, W. G., Setiawan, H., Novel, K. (2020). Cosmetics Customer Segmentation and Profile in Indonesia Using Clustering and Classification Algorithm. Journal of Physics: Conference Series, 1641, IOP Publishing.
  12. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, Elsevier, 31(8), 651–666.
  13. Joy Christy, A., 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.
  14. Kahan, R. (1998). Using database marketing techniques to enhance your one-to-one marketing initiatives. Journal of Consumer Marketing, MCB UP Ltd, 15(5), 491–493.
  15. Khatami FiroozAbadi, M., TaghaviFard, M., Sadjadi, Kh., & Bamdad Soufi, J. (2018). Optimization through simulation to solve the problem of multi-objective allocation of services to the bank's clustered customers. The Journal of Industrial Management Perspective, 30, 85-110. (In Persian)
  16. Kulkarni, M., & Gulavani, S. (2022). Role of Recommender System for Selling Products in Online Shopping. International Journal of Humanities and Social Science, “Revival Strategies and Business Policies for Sustainability and Development” on 23rd March 2022, 2582-8568, 660-666.
  17. MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
  18. Michaud, P. (1997). Clustering techniques. Future Generation Computer Systems. Elsevier, 13(2), 135–147.
  19. Motameni, A., Rezaei, M., & Ehghaghi, M. (2013). Designing a demand prediction model in the ceramic and tile industry. The Journal of Industrial Management Perspective, 9, 159-176. (In Persian)
  20. Nabizade, F., & Rouhani, S. (2020). Clustering and Prediction Model of Customer Lifetime Value (Case Studies: IRAN National Center for Numbering Goods and Services). The Journal of Industrial Management Perspective. (In Persian)
  21. Newell, F. (1997). The New Rules of Marketing: How to Use One-To-One Relationship Marketing to Be the Leader in Your Industry. McGraw-Hill, Inc., New York.
  22. Pazzani, M. J. (1999). A framework for collaborative, content-based and demographic filtering. Artificial Intelligence Review, 13(5-6), 393-408.
  23. Peker, S., Kocyigit, A., & Eren, P. E. (2017). LRFMP model for customer segmentation in the grocery retail industry: a case study. Marketing Intelligence & Planning.
  24. Radev, D. R., Fan, W., & Zhang, Z. (2001). Webinessence: A personalized web-based multi-document summarization and recommendation system [J]. Ann Arbor, 1001,
  25. Shirole, R., Salokhe, L., & Jadhav, S. (2021). Customer Segmentation using RFM Model and K-Means Clustering. International Journal of Scientific Research in Science and Technology, - 10.32628/IJSRST2183118, 591-597.
  26. Tavakoli, M., Molavi, M., Masoumi, V., Mobini, M., Etemad, S., & Rahmani, R. (2018). Customer Segmentation and Strategy Development Based on User Behavior Analysis, RFM Model and Data Mining Techniques: A Case Study. 2018 IEEE 15th International Conference on e-Business Engineering (ICEBE), 119-126.
  27. Thabit, T. H. (2018). The Impact of Customer Relationship Management on Customer Behavior-Case Study of Ooredoo for Telecommunications. La Revue des Sciences Commerciales, 17(1), 67-78.
  28. Von Reischach, F., Guinard, D., Michahelles, F., et al. (2009). A mobile product recommendation system interacting with tagged products [C]. Pervasive Computing and Communications, PerCom 2009. IEEE International Conference on. IEEE, 1-6.
  29. Wang, Y.-F., Chuang, Y.-L., Hsu, M.-H., & Keh, H.-C. (2004). A personalized recommender system for the cosmetic business. Expert Systems with Applications, 26, 427-434.
  30. Wedel, M., Kamakura, & W. A. (2012). Market Segmentation: Conceptual and Methodological Foundations. Springer Science & Business Media, 8, 210-215.
  31. Wind, Y. (1978). Issues and advances in segmentation research. Journal of Marketing Research, JSTOR, 15(3), 317–337.