Provide a method for customer segmentation using the RFM model in conditions of uncertainty
Subject Areas :mohammadreza gholamian 1 , azime mozafari 2 *
1 -
2 -
Keywords: RFM model, uncertainty, segmentation, gray number, data mining,
Abstract :
The purpose of this study is to provide a method for customer segmentation of a private bank in Shiraz based on the RFM model in the face of uncertainty about customer data. In the proposed framework of this study, first, the values of RFM model indicators including exchange novelty (R), number of exchanges (F) and monetary value of exchange (M) were extracted from the customer database and preprocessed. Given the breadth of the data, it is not possible to determine the exact number to determine whether the customer is good or bad; Therefore, to eliminate this uncertainty, the gray number theory was used, which considers the customer's situation as a range. In this way, using a different method, the bank's customers were segmented, which according to the results, customers were divided into three main sections or clusters as good, normal and bad customers. After validating the clusters using Don and Davis Boldin indicators, customer characteristics in each sector were identified and at the end, suggestions were made to improve the customer relationship management system.
1. Plakoyiannaki, E. (2005). How do organisational members perceive CRM? Evidence from a UK service firm. Journal of Marketing Management, 21(3-4), 363-392.
2. Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36(2), 2592-2602.
3. Liu, B. (2009). Some research problems in uncertainty theory. Journal of Uncertain Systems, 3(1), 3-10.
4. Liu, B. (2010). Uncertainty Theory: A Branch of Mathematics for Modeling Human Uncertainty, Springer-Verlag, Berlin.
5. Liu, B. (2007). Uncertainty theory (pp. 205-234). Springer Berlin Heidelberg.
6. Kiang, M. Y., Hu, M. Y., & Fisher, D. M. (2006). An extended self-organizing map network for market segmentation—a telecommunication example.Decision Support Systems, 42(1), 36-47.
7. Hsieh, N. C. (2004). An integrated data mining and behavioral scoring model for analyzing bank customers. Expert systems with applications, 27(4), 623-633.
8. Liu, D. R., & Shih, Y. Y. (2005). Integrating AHP and data mining for product recommendation based on customer lifetime value. Information & Management,42(3), 387-400.
9. Sohrabi, B., & Khanlari, A. (2007). Customer lifetime value (CLV) measurement based on RFM model. Iranian Accounting & Auditing Review, 14(47), 7-20.
10. Hu, W., & Jing, Z. (2008). Study of segmentation for auto services companies based on RFM model, [online],
11.Wu, H. H., Chang, E. C., & Lo, C. F. (2009). Applying RFM model and K-means method in customer value analysis of an outfitter. In Global Perspective for Competitive Enterprise, Economy and Ecology (pp. 665-672). Springer London.
12. 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.
13. Namvar, M., Gholamian, M. R., & KhakAbi, S. (2010, January). A two phase clustering method for intelligent customer segmentation. In Intelligent Systems, Modelling and Simulation (ISMS), 2010 International Conference on (pp. 215-219). IEEE.
14. Hosseini, S. M. S., Maleki, A., & Gholamian, M. R. (2010). Cluster analysis using data mining approach to develop CRM methodology to assess the customer loyalty. Expert Systems with Applications, 37(7), 5259-5264.
15. Khajvand, M., & Tarokh, M. J. (2011). Estimating customer future value of different customer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.
16. Li, D. C., Dai, W. L., & Tseng, W. T. (2011). A two-stage clustering method to analyze customer characteristics to build discriminative customer management: A case of textile manufacturing business. Expert Systems with Applications, 38(6), 7186-7191.
17. Seyedhosseini, S. M., Gholamian, M. R., & Maleki, A. (2011). A Methodology Based on RFM Using Data Mining Approach to Assess the Customer Loyalty. International Journal of Industrial Engineering, 22(2), 171-179.
18.کفاش‌پور، آ.؛ توکلی، ا.، و علیزاده زوارم، ع. (1391). بخش‌بندی مشتریان بر اساس ارزش دوره عمر آن‌ها با استفاده از داده‌کاوی بر مبنای مدل آر. اف. ام. (RFM). پژوهش‌های مدیریت عمومی، 5 (15)، 63-84.
19. زین‌العابدینی، س. ف.؛ مهدوی، م.، و خان‌بابایی، م. (1391). بخش‌بندي و شناسايي مشتريان خدمات بانكداري الكترونيكي بر مبناي تكنيك‌هاي داده‌كاوي و مدل تحليل RFM. مطالعه موردي: موسسه اعتباري توسعه. دومين كنفرانس ملي مهندسي نرم افزار.
20. Alvandi, M., Fazli, S., & Abdoli, F. S. (2012). K-Mean clustering method for analysis customer lifetime value with LRFM relationship model in banking services. International Research Journal of Applied and Basic Sciences, 3(11), 2294-2302.
21. Rezaeinia, S. M., Keramati, A., & Albadvi, A. (2012). An integrated AHP–RFM method to banking customer segmentation. International Journal of Electronic Customer Relationship Management, 6(2), 153-168.
22. Wei, J. T., Lee, M. C., Chen, H. K., & Wu, H. H. (2013). Customer relationship management in the hairdressing industry: An application of data mining techniques. Expert Systems with Applications, 40(18), 7513-7518.
23. Wen, K. L., & Huang, Y. F. (2004). The development of grey statistic toolbox and its application in the clustering of student’s test score. Journal of Quantitative Management, 1(2), 219-238.
24. Lee, K. L., Lin, S. C., & Hsiao, S. H. (2007). Analyzing the performance indices of information service type of supply chain model. Soochow Journal of Economics and Business, 58(1), 61.
25. Wen, K. L. (2008). A Matlab toolbox for grey clustering and fuzzy comprehensive evaluation. Advances in Engineering Software, 39(2), 137-145.
26. Lin, C. H., Wu, C. H., & Huang, P. Z. (2009). Grey clustering analysis for incipient fault diagnosis in oil-immersed transformers. Expert Systems with Applications, 36(2), 1371-1379.
27. Wei, J. T., Lin, S. Y., & Wu, H. H. (2010). A review of the application of RFM model.
28. Buttle, F. (2004). Customer relationship management: Concepts and Tools. Elsevier Butterworth Heinemann.
29. رزمي، ج.، و قنبري، آ. (1388). ارائه مدلي نوين جهت محاسبه ارزش دوره عمر مشتري. مديريت فناوري و اطلاعات، 1 (1)، 35 -50.
30. Kim, Y. S., & Sohn, S. Y. (2004). Managing loan customers using misclassification patterns of credit scoring model. Expert Systems with Applications, 26(4), 567-573.
31. Wu, W. H., Lin, C. T., Peng, K. H., & Huang, C. C. (2012). Applying hierarchical grey relation clustering analysis to geographical information systems–A case study of the hospitals in Taipei City. Expert Systems with Applications, 39(8), 7247-7254.
32. Liu, L., Zhou, J. Z., An, X. L., Yang, L., & Liu, S. Q. (2007, November). Improvement of the grey clustering method and its application in water quality assessment. In Wavelet Analysis and Pattern Recognition, 2007. ICWAPR'07. International Conference on (Vol. 2, pp. 907-911). IEEE.
33. Ke, L., Xiaoliu, S., Zhongfu, T., & Wenyan, G. (2012). Grey clustering analysis method for overseas energy project investment risk decision. Systems Engineering Procedia, 3, 55-62.
34. Luo, D., & Liu, S. F. (2005). Grey incidence decision-making with incomplete information. Journal of applied sciences, 23(4), 408-412.
35. Nozari, H., Jafari-Eskandari, M., Kamfirozi, M. H., & Mozafari, A. (2014). Using Numerical Taxonomy and Combined Bulls-Eye–Shapley Weighting Method in Order to Ranking Websites of Iranian Universities by Three-Parameter Interval Gray Numbers. Arabian Journal for Science and Engineering, 39(4), 3299-3305.
36. Hu, B. Q., & Wang, S. (2006). A novelapproach in uncertain programming part I: new arithmetic and order relation for interval numbers. Journal of Industrial and Management Optimization, 2(4), 351.