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Customer portfolio analysis using the SOM

Published Online:pp 396-412https://doi.org/10.1504/IJBIS.2011.042397

In order to compete for profitable customers, companies are looking to add value using customer relationship management (CRM). One subset of CRM is customer segmentation, which is the process of dividing customers into groups based upon common features or needs. Segmentation methods can be used for customer portfolio analysis (CPA), the process of analysing the profitability of customers. The purpose of this paper is to illustrate how the self-organising map (SOM) can be used for CPA. We segment, identify and analyse a case organisation’s profitable and unprofitable customers in order to gain knowledge for the organisation to develop its marketing strategies. The results are validated through cross and face validation. The SOM is able to segment the data in an innovative and reliable way and to provide new insights for decision makers.

Keywords

customer relationship management, CRM, customer portfolio analysis, CPA, data-driven market segmentation, self-organising map, SOM

References

  • 1. Back, B. , Oosterom, G. , Sere, K. , Van Wezel, M. , Doukidis, G. Galliers, R.D. Jelassi, T. Kremer, H. Land, F.F. (1995). ‘Intelligent information systems within business: bankruptcy predictions using neural networks’. The 3rd European Conference on Information Systems (ECIS’95), June 1–3, 99-111 Google Scholar
  • 2. Back, B. , Sere, K. , Vanharanta, H. (1998). ‘Managing complexity in large data bases using self-organizing maps’. Accounting Management and Information Technologies. 8, 4, 191-210 Google Scholar
  • 3. Berry, M.J.A. , Linoff, G.S. (2004). Data Mining Techniques: For marketing, Sales, and Customer Relationship Management. 2nd ed., Indianapolis, Indiana:Wiley Publishing Inc. Google Scholar
  • 4. Berson, A. , Smith, S. , Thearling, K. (2000). Building Data Mining Applications for CRM. USA:McGraw-Hill Companies Inc. Google Scholar
  • 5. Bigus, J.P. (1996). Data mining with Neural Networks: Solving Business Problems from Application Development to Decision Support. New York, NY:The McGraw-Hill Companies Inc. Google Scholar
  • 6. Bishop, C.M. (1995). Neural Networks for Pattern Recognition. Avon:Oxford University Press Google Scholar
  • 7. Buttle, F. (2004). Customer Relationship Management Concepts and Tools. Oxford:Butterworth-Heinemann Google Scholar
  • 8. Chalmeta, R. (2006). ‘Methodology for customer relationship management’. The Journal of Systems and Software. 79, 7, 1015-1024 Google Scholar
  • 9. Chan, C.C.H. (2008). ‘Intelligent value-based customer segmentation method for campaign management: a case study of automobile retailer’. Expert Systems with Applications. 34, 4, 2754-2762 Google Scholar
  • 10. Crone, S.F. , Lessmann, S. , Stahlbock, R. (2006). ‘The impact of preprocessing on data mining: an evaluation of classifier sensitivity in direct marketing’. European Journal of Operational Research. 173, 3, 781-800 Google Scholar
  • 11. Datta, Y. (1996). ‘Market segmentation: an integrated framework’. Long Range Planning. 29, 6, 797-811 Google Scholar
  • 12. Deboeck, G.J. , Deboeck, G.J. Kohonen, T. (1998). ‘Software tools for self-organizing maps’. Visual Explorations in Finance Using Self-Organizing Maps. Berlin:Springer-Verlag , 179-194 Google Scholar
  • 13. Deboeck, G.J. , Kohonen, T. (1998). Visual Explorations in Finance with Self-Organizing Maps. Berlin:Springer-Verlag Google Scholar
  • 14. Desmet, P. (2001). ‘Buying behavior study with basket analysis: pre-clustering with a Kohonen map’. European Journal of Economic and Social Systems. 15, 2, 17-30 Google Scholar
  • 15. Dibb, S. (2001). ‘New millennium, new segments: moving towards the segment of one?’. Journal of Strategic Marketing. 9, 3, 193-213 Google Scholar
  • 16. Eklund, T. , Back, B. , Vanharanta, H. , Visa, A. (2003). ‘Using the self-organizing map as a visualization tool in financial benchmarking’. Information Visualization. 2, 3, 171-181 Google Scholar
  • 17. Famili, A. , Shen, W. , Weber, R. , Simoudis, E. (1997). ‘Data preprocessing and intelligent data analysis’. Intelligent Data Analysis. 1, 1, 3-23 Google Scholar
  • 18. Frank, R.E. , Massy, W.F. , Wind, Y. (1972). Market Segmentation. Englewood Cliffs, New Jersey:Prentice-hall Inc. Google Scholar
  • 19. Hand, D.J. , Mannila, H. , Smyth, P. (2001). Principles of Data Mining. Boston. USA:MIT Press Google Scholar
  • 20. Haykin, S. (1999). Neural Networks – A Comprehensive Foundation. Upper Saddle River, NJ:Prentice Hall International, Inc. Google Scholar
  • 21. Heinrich, B. (2005). ‘Transforming strategic goals of CRM into process goals and activities’. Business Process Management Journal. 11, 6, 709-723 Google Scholar
  • 22. Holmbom, A.H. (2007). ‘Identifying customer segments using the self-organizing map’. Turku, Finland:Abo Akademi University , Unpublished Master’s Thesis Google Scholar
  • 23. Holmbom, A.H. , Eklund, T. , Back, B. (2008). ‘Customer portfolio analysis using the SOM’. in Proceedings of 19th Australasian Conference on Information Systems (ACIS 2008). December 3–5, 412-422 Google Scholar
  • 24. Hosseini, S.M.S. , Malekia, A. , Gholamiana, 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 Google Scholar
  • 25. Hu, M.Y. , Shanker, M. , Zhang, G.P. , Hung, M.S. (2008). ‘Modeling consumer situational choice of long distance communication with neural networks’. Decision Support Systems. 44, 4, 899-908 Google Scholar
  • 26. Kaski, S. , Kohonen, T. , Apostolos, P.N.R. Abu-Mostafa, Y. Moody, J. Weigend, A. (1996). ‘Exploratory data analysis by the self-organizing map: structures of welfare and poverty in the world’. Neural Networks in Financial Engineering. Singapore:World Scientific , 498-507 Google Scholar
  • 27. Kaski, S. , Kangas, J. , Kohonen, T. (1998). ‘Bibliography of self-organizing map (SOM) papers 1981–1997’. Neural Computing Surveys. 1, 102-350 Google Scholar
  • 28. Kim, S. , Jung, T. , Suh, E. , Hwang, H. (2006). ‘Customer segmentation and strategy development based on customer lifetime value: a case study’. Expert Systems with Applications. 31, 1, 101-107 Google Scholar
  • 29. Kiviluoto, K. (1998). ‘Predicting bankruptcies with the self-organizing map’. Neurocomputing. 21, 1–3, 191-201 Google Scholar
  • 30. Kohonen, T. , Deboeck, G.J. Kohonen, T. (1998). ‘The SOM methodology’. Visual Explorations in Finance: with Self-Organizing Maps. Berlin:Springer Verlag , 159-167 Google Scholar
  • 31. Kohonen, T. (2001). Self-Organizing Maps. Berlin:Springer-Verlag Google Scholar
  • 32. Länsiluoto, A. (2007). ‘Suitability of self-organising maps for analysing a macro-environment – an empirical field survey’. International Journal of Business Information Systems. 2, 2, 149-161 AbstractGoogle Scholar
  • 33. Larose, D.T. (2005). Discovering Knowledge in Data. An Introduction to Data Mining. Hoboken, NJ:John Wiley & Sons Inc. Google Scholar
  • 34. Lee, S.C. , Suh, Y.H. , Kim, J.K. , Lee, K.J. (2004). ‘A cross-national market segmentation of online game industry using SOM’. Expert Systems with Applications. 27, 4, 559-570 Google Scholar
  • 35. Lee, S.C. , Xiang, J.Y. , Jing, L.B. (2005). ‘Who are the target customers in Chinese online game market? Segmentation with a two-step approach’. in Proceedings of the 2005 IEEE International Conference on e-Business Engineering. IEEE, 736-743 Google Scholar
  • 36. Lingras, P. , Hogo, M. , Snorek, M. , West, C. (2005). ‘Temporal analysis of clusters of supermarket customers: conventional versus interval set approach’. Information Sciences. 172, 1–2, 215-240 Google Scholar
  • 37. Martín-del-Brío, B. , Serrano-Cinca, C. (1993). ‘Self-organizing neural networks for the analysis and representation of data: some financial cases’. Neural Computing and Applications. 1, 2, 193-206 Google Scholar
  • 38. McCarty, J.A. , Hastak, M. (2007). ‘Segmentation approaches in data-mining: a comparison of RFM, CHAID, and logistic regression’. Journal of Business Research. 60, 6, 656-662 Google Scholar
  • 39. Mo, J. , Kiang, M.Y. , Zou, P. , Li, Y. (2010). ‘A two-stage clustering approach for multi-region segmentation’. Expert Systems with Applications. 37, 10, 7120-7131 Google Scholar
  • 40. Ngai, E.W.T. , Xiub, L. , Chaua, D.C.K. (2009). ‘Application of data mining techniques in customer relationship management: a literature review and classification’. Expert Systems with Applications. 36, 2, 2592-2602 Google Scholar
  • 41. Oja, M. , Kaski, S. , Kohonen, T. (2003). ‘Bibliography of self-organizing map (SOM) papers: 1998–2001 addendum’. Neural Computing Surveys. 3, 1-156 Google Scholar
  • 42. Paas, L. , Kuijlen, T. (2001). ‘Towards a general definition of customer relationship management’. Journal of Database Marketing. 9, 1, 51-60 Google Scholar
  • 43. Parvatiyar, A. , Sheth, J.N. (2001). ‘Customer relationship management: emerging practice, process and discipline’. Journal of Economic and Social Research. 3, 2, 1-34 Google Scholar
  • 44. Pyle, D. (1999). Data Preparation for Data Mining. San Diego, CA:Academic Press Google Scholar
  • 45. Romdhane, L.B. , Fadhel, N. , Ayeb, B. (2010). ‘An efficient approach for building customer profiles from business data’. Expert Systems with Applications. 37, 2, 1573-1585 Google Scholar
  • 46. Rushmeier, H. , Lawrence, R. , Almasi, G. (1997). ‘Case study: visualizing customer segmentations produced by self organizing maps’. 463-466, Eighth IEEE Visualization 1997 (VIS’97) Google Scholar
  • 47. Rygielski, C. , Wang, J. , Yen, D.C. (2002). ‘Data mining techniques for customer relationship management’. Technology in Society. 24, 4, 483-502 Google Scholar
  • 48. Shaw, M.J. , Subramaniam, C. , Tan, G.W. , Welge, M.E. (2001). ‘Knowledge management and data mining for marketing’. Decision Support Systems. 31, 1, 127-137 Google Scholar
  • 49. Smith, K. , Gupta, J. (2002). Neural Networks in Business. Hershey, PA:IDEA Group Publishing Google Scholar
  • 50. Terho, H. , Halinen, A. (2007). ‘Customer portfolio analysis practices in different exchange contexts’. Journal of Business Research. 60, 7, 720-730 Google Scholar
  • 51. Tsai, C. , Chiu, C. (2004). ‘A purchase-based market segmentation methodology’. Expert Systems with Applications. 27, 2, 265-276 Google Scholar
  • 52. Turnbull, P.W. (1990). ‘A review of portfolio planning models for industrial marketing and purchasing management’. European Journal of Marketing. 24, 3, 7-22 Google Scholar
  • 53. Ultsch, A. , Gielen, S. Kappen, B. (1993). ‘Self organized feature planes for monitoring and knowledge acquisition of a chemical process’. The International Conference on Artificial Neural Networks (ICANN93), London:Springer-Verlag , 864-867 Google Scholar
  • 54. Vellido, A. , Lisboa, P.J.G. , Meehan, K. (1999a). ‘Segmentation of the on-line shopping market using neural networks’. Expert Systems with Applications. 17, 4, 303-314 Google Scholar
  • 55. Vellido, A. , Lisboa, P.J.G. , Vaughan, J. (1999b). ‘Neural networks in business: a survey of applications (1992–1998)’. Expert Systems with Applications. 17, 1, 51-70 Google Scholar
  • 56. Vesanto, J. , Alhoniemi, E. (2000). ‘Clustering of the self-organizing map’. IEEE Transactions on Neural Networks. 11, 3, 586-600 Google Scholar
  • 57. Wang, Y. , Chiang, D. , Hsu, M. , Lin, C. , Lin, I. (2009). ‘A recommender system to avoid customer churn: a case study’. Expert Systems with Applications. 36, 4, 8071-8075 Google Scholar
  • 58. Wedel, M. , Kamakura, W. (1999). ‘Market segmentation conceptual and methodological foundations’. Massachusetts, USA:Kluwer Academic Publishers Google Scholar
  • 59. Yao, Z. , Holmbom, A.H. , Eklund, T. , Back, B. (2010). ‘Combining unsupervised and supervised data mining techniques for conducting customer portfolio analysis’. Advances in Data Mining. Applications and Theoretical Aspects. Heidelberg:Springer , 292-307 Google Scholar