Journal of Marketing Analytics

, Volume 3, Issue 3, pp 110–121 | Cite as

New approach to customer segmentation based on changes in customer value

Original Article


In today’s fast moving world of marketing from product-orientation to customer-orientation, the management of customer treatment can be seen as a key to achieve revenue growth and profitability. Knowledge of customer behavior can help marketing managers re-evaluate their strategies with the customers and plan to improve and expand their application of the most effective strategies. B2B or business customers are more complex, their buying process is more complicated and their sales value is greater. The business marketers usually prefer to cooperate with fewer but larger buyers than the final consumer marketer. As a business transaction requires more decision makings and more professional buying effort than the consumer market does, the efficient relationship with business customers is of paramount importance. Most customer segmentation approaches based on customer value fail to account for the factor of time and the trend of value changes in their analysis. In this article, we classify customers based on their value using the RFM model and K-means clustering method. Then, an assessment of changes over several periods of time is carried out. The originality of this research lies in its incorporation of time and trend of customer value changes in improving the accuracy of predictions based on the past behavior of customers. For this purpose, we used the POS customer transactions.


segmentation Customer lifetime value Temporal Data Mining RFM model 


  1. Bauer, C.L. (1988) A direct mail customer purchase model. Journal of Direct Marketing 2 (3): 16–24.CrossRefGoogle Scholar
  2. Berson, A., Smith, S. and Thearling, K. (1999) Building Data Mining Applications for CRM. 1st edn. New York: McGraw-Hill Professional.Google Scholar
  3. Bose, I. and Chen, X. (2009) Quantitative models for direct marketing: A review from systems perspective. European Journal of Operational Research 195 (1): 1–16.CrossRefGoogle Scholar
  4. Böttcher, M., Höppner, F. and Spiliopoulou, M. (2008) On exploiting the power of time in data mining. ACM SIGKDD Explorations Newsletter 10 (2): 3–11.CrossRefGoogle Scholar
  5. Böttcher, M., Spott, M., Nauck, D. and Kruse, R. (2009) Mining changing customer segments in dynamic markets. Expert Systems with Applications 36 (1): 155–164.CrossRefGoogle Scholar
  6. Bramer, M. (2007) Principles of Data Mining (Vol. 131). Berlin: Springer.Google Scholar
  7. Chang, H.-C. and Tsai, H.-P. (2011) Group RFM analysis as a novel framework to discover better customer consumption behavior. Expert Systems with Applications 38 (12): 14499–14513.CrossRefGoogle Scholar
  8. Chang, H.H. and Tsay, S.F. (2004) Integrating of SOM and K-Mean in data mining clustering: An empirical study of CRM and profitability evaluation. Journal of Information Management 11 (4): 161–203.Google Scholar
  9. Chen, M.-C., Chiu, A.-L. and Chang, H.-H. (2005) Mining changes in customer behavior in retail marketing. Expert Systems with Applications 28 (4): 773–781.CrossRefGoogle Scholar
  10. Cheng, C.-H. and Chen, Y.-S. (2009) Classifying the segmentation of customer value via RFM model and RS theory. Expert Systems with Applications 36 (3): 4176–4184.CrossRefGoogle Scholar
  11. Davenport, T.H., Harris, J.G. and Morison, R. (2010) Analytics at Work: Smarter Decisions, Better Results. Boston: Harvard Business Press.Google Scholar
  12. Han, J., Kamber, M. and Pei, J. (2011) Data mining: concepts and techniques: concepts and techniques. Berlin: Elsevier.Google Scholar
  13. Hosseini, S.M.S., Maleki, A. and 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.CrossRefGoogle Scholar
  14. Hu, Y.-H., Huang, T. C.-K. and Kao, Y.-H. (2013) Knowledge discovery of weighted RFM sequential patterns from customer sequence databases. Journal of Systems and Software 86 (3): 779–788.CrossRefGoogle Scholar
  15. Huang, T. C.-K. (2012) Mining the change of customer behavior in fuzzy time-interval sequential patterns. Applied Soft Computing 12 (3): 1068–1086.CrossRefGoogle Scholar
  16. Hughes, A. (1994) Strategic Database Marketing, Chicago, IL: Probus Publishing.Google Scholar
  17. Hwang, H., Jung, T. and Suh, E. (2004) An LTV model and customer segmentation based on customer value: A case study on the wireless telecommunication industry. Expert Systems with Applications 26 (2): 181–188.CrossRefGoogle Scholar
  18. Kim, S.-Y., Jung, T.-S., Suh, E.-H. and Hwang, H.-S. (2006) Customer segmentation and strategy development based on customer lifetime value: A case study. Expert Systems with Applications 31 (1): 101–107.CrossRefGoogle Scholar
  19. Kotler, P. (2001) Marketing Management. 10th edn. Canada: Pearson Education Canada.Google Scholar
  20. Ling, R. and Yen, D.C. (2001) Customer relationship management: An analysis framework and implementation strategies. Journal of Computer Information Systems 41 (3): 82–97.Google Scholar
  21. Liu, D.-R. and Shih, Y.-Y. (2005) Integrating AHP and data mining for product recommendation based on customer lifetime value. Information & Management 42 (3): 387–400.CrossRefGoogle Scholar
  22. Marcus, C. (1998) A practical yet meaningful approach to customer segmentation. Journal of Consumer Marketing 15 (5): 494–504.CrossRefGoogle Scholar
  23. Roddick, J.F. and Spiliopoulou, M. (2002) A survey of temporal knowledge discovery paradigms and methods. Knowledge and Data Engineering, IEEE Transactions on 14 (4): 750–767.CrossRefGoogle Scholar
  24. Song, H.S. and Kim, S.H. (2001) Mining the change of customer behavior in an internet shopping mall. Expert Systems with Applications 21 (3): 157–168.CrossRefGoogle Scholar
  25. Stone, B. (1995) Successful Direct Marketing Methods, NTC Business Books, pp. 37–57.Google Scholar
  26. Tsai, C.-Y. and Chiu, C.-C. (2004) A purchase-based market segmentation methodology. Expert Systems with Applications 27 (2): 265–276.CrossRefGoogle Scholar
  27. Turban, E., Sharda, R., Aronson, J.E. and King, D.N. (2008) Business Intelligence: A Managerial Approach, Upper Saddle River, NJ: Pearson Prentice Hall.Google Scholar
  28. Verhoef, P.C., Spring, P.N., Hoekstra, J.C. and Leeflang, P.S.H. (2003) The commercial use of segmentation and predictive modeling techniques for database marketing in the Netherlands. Decision Support Systems 34 (4): 471–481.CrossRefGoogle Scholar
  29. Wei, J.-T., Lin, S.-Y. and Wu, H.-H. (2010) A review of the application of RFM model. African Journal of Business Management 4 (19): 4199–4206.Google Scholar

Copyright information

© Palgrave Macmillan, a division of Macmillan Publishers Ltd 2015

Authors and Affiliations

  1. 1.K. N. Toosi University of TechnologyTehranIran

Personalised recommendations