Characterizing and Segmenting the Online Customer Market Using Neural Networks

  • Alfredo Vellido
  • Paulo J. G. Lisboa
  • Karon Meehan
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)


The proliferation of Business-to-Consumer (B2C) Internet companies that characterised the late ‘90s seems now under threat. A focus on customers’ needs and expectations seems more justified than ever and, with it, the quantitative analysis of customer behavioural data. Neural networks, as quantitative analytical tools, have been proposed as a leading methodology for data mining. This chapter provides guidelines for the application of neural networks to the characterization and segmentation of the on-line customer market, in a way that we consider being best practice.


Neural Network Hide Node Market Segmentation Shopping Experience Bayesian Neural Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Balakrishnan PVS, Cooper MC, Jacob VS, Lewis PA (1996) Comparative performance of the FSCL neural net and K-means algorithm for market segmentation. European Journal of Operational Research, 93: 346–357CrossRefGoogle Scholar
  2. 2.
    Bellman S, Lohse GL, Johnson EJ (1999) Predictors of online buying behaviour, Communications of the ACM, 42 (12): 32–38CrossRefGoogle Scholar
  3. 3.
    Bishop CM, Svensén M, Williams, CKI (1997) Magnification factors for the GTM algorithm. In Proceedings IEE Fifth International Conference on Artificial Neural Networks, Cambridge, pp 64–69Google Scholar
  4. 4.
    Bishop CM, Svensén M, Williams, CKI (1998) Developments of the Generative Topographic Mapping. Neurocomputing 21 (1–3): 203–224CrossRefGoogle Scholar
  5. 5.
    Changchien SW, Lu, T-C (2001) Mining association rules procedure to support on-line recommendation by customers and product fragmentation. Expert Systems with Applications 20: 325–335CrossRefGoogle Scholar
  6. 6.
    Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B, 39 (1): 138Google Scholar
  7. 7.
    Gordon A (1999) Classification. Chapman and Hall/CRC Press, LondonGoogle Scholar
  8. 8.
    Green PE, Krieger, AM (1995) Alternative approaches to cluster-based market segmentation. Journal of the Market Reseach Society, 37 (3): 221–239Google Scholar
  9. 9.
    Ha SH, Park SC (1998) Application of data mining tools to hotel data mart on the Intranet for database marketing. Expert Systems With Applications 15: 131CrossRefGoogle Scholar
  10. 10.
    Hand DJ (1997) Construction and Assessment of Classification Rules. John Wiley & Sons, ChichesterGoogle Scholar
  11. 11.
    Hinton GE, Williams CKI, Revow MD (1992) Adaptive elastic models for hand-printed character recognition. In Moody JE, Hanson SJ, Lippmann RP (eds) Advances in Neural Information Processing Systems vol 4, Morgan Kauffmann, pp 512–519Google Scholar
  12. 12.
    Hoffman DL, Novak TP, Peralta MA (1999) Information privacy in the marketspace: implications for the commercial uses of anonymity on the Web. The Information Society, 15 (2): 129–139CrossRefGoogle Scholar
  13. 13.
    Jarvenpaa SL, Todd PA (1996/1997) Consumer reactions to electronic shopping on the WWW. International Journal of Electronic Commerce 1(2): 59–88Google Scholar
  14. 14.
    Kehoe C, Pitkow J, Rogers JD (1998) 9th GVU’s WWW User Survey URL: htt.://www. vu suive s/surve -1998–04/Google Scholar
  15. 15.
    Kiang MY, Raghu TS, Shang KH-M (2000) Marketing on the Internet-who can benefit from an online marketing approach. Decision Support Systems 27: 383–393CrossRefGoogle Scholar
  16. 16.
    Lisboa PJG, Vellido A, Wong H (2000) Bias reduction in skewed binary classification with Bayesian neural networks. Neural Networks 13: 407–410CrossRefGoogle Scholar
  17. 17.
    Mackay DJC (1992a) A practical Bayesian framework for back-propagation networks. Neural Computation 4 (3): 448–472CrossRefGoogle Scholar
  18. 18.
    Mackay DJC (1992b) The evidence framework applied to classification networks. Neural Computation 4 (5): 698–714CrossRefGoogle Scholar
  19. 19.
    Mackay DJC (1995) Probable networks and plausible predictions a review of practical Bayesian methods for supervised neural networks. Network: Computation in Neural Systems 6: 469–505CrossRefGoogle Scholar
  20. 20.
    Mena J (1999) Data Mining Your Website. Butterworth-Heinemann/Digital Press, Woburn, MAGoogle Scholar
  21. 21.
    Milligan GW, Cooper MC (1985) An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50: 159–179CrossRefGoogle Scholar
  22. 22.
    Murtagh F (1995) Interpreting the Kohonen self-organizing feature map using contiguity-constrained clustering. Pattern Recognition Letters 16 (4): 399–408CrossRefGoogle Scholar
  23. 23.
    Penny WD, Roberts SJ (1999) Bayesian neural networks for classification: how useful is the evidence framework? Neural Networks 12: 877–892CrossRefGoogle Scholar
  24. 24.
    Refenes A, Burgess AN, Bentz Y (1997) Neural networks in financial engineering: A study in methodology. IEEE Transactions on Neural Networks 8 (6): 1222–1267CrossRefGoogle Scholar
  25. 25.
    Ripley B (1996) Pattern Recognition and Neural Networks. Cambridge University Press, CambridgeGoogle Scholar
  26. 26.
    Scharl A, Brandtweiner R (1998) A conceptual research framework for analyzing the evolution of electronic markets. Electronic Markets Newsletter 8 (2): 16Google Scholar
  27. 27.
    Svensén M (1998) GTM: The Generative Topographic Mapping. PhD thesis. Aston University, UKGoogle Scholar
  28. 28.
    Tarassenko L (1998) A Guide to Neural Computing Applications. Arnold, LondonGoogle Scholar
  29. 29.
    Tibshirani R, Walther G, Hastie T (2000) Estimating the number of clusters in a data set via the Gap statistic. Technical Report, Stanford University, CaliforniaGoogle Scholar
  30. 30.
    Vellido A, Lisboa PJG, Meehan K (2000a) Quantitative characterization and prediction of on-line purchasing behaviour: a latent variable approach. International Journal of Electronic Commerce 4 (4): 83–104Google Scholar
  31. 31.
    Vellido A, Lisboa PJG, Meehan K (2000b) The Generative Topographic Mapping as a principled model for data visualization and market segmentation: an electronic commerce case study. International Journal of Computers, Systems and Signals 1(2): 119–138Google Scholar
  32. 32.
    Vellido A, Lisboa PJG, Vaughan J (1999) Neural networks in business: a survey of applications (1992–1998). Expert Systems with Applications 17 (1): 51–70CrossRefGoogle Scholar
  33. 33.
    Vesanto J (1999) SOM-based data visualization. Intelligent Data Analysis 3 (2): 111–126CrossRefGoogle Scholar
  34. 34.
    Vesanto J, Alhoniemi E (2000) Clustering of the Self-Organizing Map. IEEE Transactions on Neural Networks 11 (3) 586–600CrossRefGoogle Scholar
  35. 35.
    Wallin EO (1999) Consumer personalization technologies for e-commerce on the Internet: a taxonomy. In Roger, J-Y, Standford-Smith B, Kidd, PT (eds) Proceedings of the European Multimedia, Microprocessor Systems and Electronic Commerce (EMMSEC’99). IOS Press, AmsterdamGoogle Scholar
  36. 36.
    Wedel M, Kamakura WA (1998) Market Segmentation. Conceptual and Methodological Foundations. Kluwer, MassachusettsGoogle Scholar
  37. 37.
    Wong BK, Bodnovich TA, Selvi Y (1997) Neural network applications in business: A review and analysis of the literature (1988–95). Decision Support Systems 19: 301–320CrossRefGoogle Scholar
  38. 38.
    Zhang G, Patuwo BE, Hu, MY (1998) Forecasting with artificial neural networks: The state of the art. International Journal of Forecasting 14 (1): 35–62CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Alfredo Vellido
    • 1
  • Paulo J. G. Lisboa
    • 1
  • Karon Meehan
    • 2
  1. 1.School of Computing and Mathematical SciencesLiverpool John Moores UniversityLiverpoolUK
  2. 2.Business SchoolLiverpool John Moores UniversityLiverpoolUK

Personalised recommendations