Business Client Segmentation in Banking Using Self-Organizing Maps

Open access


Segmentation in banking for the business client market is traditionally based on size measured in terms of income and the number of employees, and on statistical clustering methods (e.g. hierarchical clustering, k-means). The goal of the paper is to demonstrate that self-organizing maps (SOM) effectively extend the pool of possible criteria for segmentation of the business client market with more relevant criteria, including behavioral, demographic, personal, operational, situational, and cross-selling products. In order to attain the goal of the paper, the dataset on business clients of several banks in Croatia, which, besides size, incorporates a number of different criteria, is analyzed using the SOM-Ward clustering algorithm of Viscovery SOMine software. The SOM-Ward algorithm extracted three segments that differ with respect to the attributes of foreign trade operations (import/export), annual income, origin of capital, important bank selection criteria, views on the loan selection and the industry. The analyzed segments can be used by banks for deciding on the direction of further marketing activities.

1. Agarwal, J., Malhotra, N. K., Bolton, R. N. 2010. A cross-national and cross-cultural approach to global market segmentation: An application using consumers’ perceived service quality. Journal of International Marketing 18(3): 18-40.

2. Anderson, W.T., Cox, E.P., Fulcher, D.G. 1976. Bank Selection Decisions and Market Segmentation. Journal of Marketing 40(1): 40-45.

3. Assunçao, J. 2013. Eliminating entry barriers for the provision of banking services: Evidence from ‘banking correspondents’ in Brazil. Journal of Banking & Finance 37(8): 2806-2811.

4. Athanassopoulos, A. D. 2000. Customer satisfaction cues to support market segmentation and explain switching behavior. Journal of Business Research 47(3): 191-207.

5. Bloom, J.Z. 2004. Tourist market segmentation with linear and non-linear techniques. Tourism Management 25(6): 723-733.

6. Chan, K.Y., Kwong, C.K., Hu, B.Q. 2012. Market segmentation and ideal point identification for new product design using fuzzy data compression and fuzzy clustering methods. Applied Soft Computing 12(4): 1371-1378.

7. Chen, J., Bell, P. C. 2012. Implementing market segmentation using full-refund and no-refund customer returns policies in a dual-channel supply chain structure. International Journal of Production Economics 136(1): 56-66.

8. Deboeck, G., Schmitt, B. 1998. Differential Patterns in Consumer Purchase Preferences using Self-Organizing Maps: A Case Study of China, Visual Explorations in Finance: with selforganizing maps, edited by G. Deboeck, T. Kohonen, 141-156, London: Springer.

9. Denton, L., Chan, A.K.K. 1991. Bank Selection Criteria of Multiple Bank Users in Hong Kong, International Journal of Bank Marketing 9(5): 23-34.

10. Edris, T. A. 1997. Services considered important to business customers and determinants of bank selection in Kuwait: a segmentation analysis. International Journal of Bank Marketing 15(4): 126-133.

11. Ekinci, Y., Uray, N., Ulengin, F. (2014). A customer lifetime value model for the banking industry: a guide to marketing actions. European Journal of Marketing 48(3/4), Accepted for publication.

12. Garland, R. 2005. Segmenting retail banking clients. Journal of Financial Services Marketing 10: 179-191.

13. Han, J., Kamber, M. 2006. Data Mining: Concepts and Techniques. Tokyo: Morgan Kaufmann Publishers.

14. Hanafizadeh, P., Mirzazadeh, M. 2011. Visualizing market segmentation using self-organizing maps and Fuzzy Delphi method ADSL market of a telecommunication company. Expert Systems with Applications 38(1): 198-205.

15. Hung, C. Tsai, C.F. 2008. Market segmentation based on hierarchical self-organizing map for markets of multimedia on demand, Expert Systems with Applications 34(2): 780-787.

16. Jagric, T., Jagric, V. 2011. A Comparison of Growing Cell Structures Neural Networks and Linear Scoring Models in the Retail Credit Environment. Eastern European Economics 49(6): 74-96.

17. Kiang, M. Y. 2001. Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics & Data Analysis 38: 161-180.

18. 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.

19. Kim, J. N., Grunig, J. E. 2011. Problem solving and communicative action: A situational theory of problem solving. Journal of Communication 61(1): 120-149.

20. Kohonen, T. 1995. Self-organizing maps. Berlin: Springer-Verlag.

21. Kotler, P., Armstrong, G., Saunders, J. & Wong, V. 2001. Principles of Marketing, Prentice Hall, Harlow.

22. Kumar, P. R., Ravi, V. 2007. Bankruptcy prediction in banks and firms via statistical and intelligent techniques - A review. European Journal of Operational Research 180: 1-28.

23. Kuo, R.J., Ho, L.M., Hu, C.M. 2002. Integration of self-organizing feature map and K-means algorithm for market segmentation. Computers & Operations Research 29(11): 1475-1493.

24. Laroche, M., Rosenblatt, J.A., Manning, T. 1986. Services Used and Factors Considered Important in Selecting a Bank: An Investigation across Diverse Demographic Segments, International Journal of Bank Marketing 4(1): 35 - 55.

25. Lee, K., Booth, D., Pervaiz, A. 2005. A comparison of supervised and unsupervised neural networks in predicting bankruptcy of Korean firms. Expert Systems with Applications, 2: 1-16.

26. Machauer, A., Morgner, S. 2001. Segmentation of bank clients by expected benefits and attitudes, International Journal of Bank Marketing, 19(1): 6-18.

27. Mäenpää, K. 2006. Clustering the consumers on the basis of their perceptions of the Internet banking services. Internet Research, 16(3): 304-322.

28. Mangiameli, P., Chen, S. K., West, D. 1996. A comparison of SOM neural network and hierarchical clustering methods. European Journal of Operational Research 93(2): 402-417.

29. Nasri, W., Charfeddine, L. 2012. Factors affecting the adoption of Internet banking in Tunisia: An integration theory of acceptance model and theory of planned behavior. The Journal of High Technology Management Research 23(1):1-14.

30. Patsiotis, A. G., Hughes, T., Webber, D. J. 2012. Adopters and non-adopters of internet banking: a segmentation study. International Journal of Bank Marketing 30(1): 20-42.

31. Shocker, A. D., Ben-Akiva, M., Boccara, B., Nedungadi, P. 1991. Consideration set influences on consumer decision-making and choice: Issues, models, and suggestions. Marketing letters 2(3): 181-197.

32. Sponer, M. 2012. Segmentation of corporate clients in a bank, Masaryk University, Brno, Czeck Republik.

33. Turnbull, P. W., Gibbs, M. L. 1987. Marketing bank services to corporate customers: the importance of relationships. International Journal of Bank Marketing 5(1): 19-26.

34. Vellido, A., Lisboa, P.J.G., Meehan, K. 1999. Segmentation of the on-line shopping market using neural networks. Expert Systems with Applications, 17(4): 303-314.

35. Venkatesh, R. 2011. New Parameters in Market Segmentation- Ethnic Marketing is the Key. Advances In Management 4(11): 15-19.

36. Watson, H.J., Wixom, B.H. 2007. The Current State of Business Intelligence, Computer, 40(9): 96-99.

37. Wedel, M., Kamakura, W. 2003. Market Segmentation: Conceptual and Methodological Foundations, Kluwer Academic Publishers, Norwell.

South East European Journal of Economics and Business

The Journal of University of Sarajevo

Journal Information

CiteScore 2017: 0.65

SCImago Journal Rank (SJR) 2017: 0.240
Source Normalized Impact per Paper (SNIP) 2017: 0.609


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 338 338 56
PDF Downloads 108 108 26