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Segmenting the e-Commerce Market Using the Generative Topographic Mapping

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MICAI 2000: Advances in Artificial Intelligence (MICAI 2000)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 1793))

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Abstract

The neural network-based Generative Topographic Mapping (GTM) (Bishop et al. 1998a, 1998b) is a statistically sound alternative to the well-known Self Organizing Map (Kohonen 1982, 1995). In this paper we propose the GTM as a principled model for cluster-based market segmentation and data visualization. It has the capability to define, using Bayes’ theorem, a posterior probability of cluster/segment membership for each individual in the data sample. This, in turn, enables the GTM to be used to perform segmentation to different levels of detail or granularity, encompassing aggregate segmentation and one-to-one micro-segmentation. The definition of that posterior probability also makes the GTM a tool for fuzzy clustering/segmentation. The capabilities of the model are illustrated by a segmentation case study using real-world data of Internet users opinions on business-to-consumer electronic commerce.

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References

  • Allenby, G.M., Lenk, P.J.: Modelling household purchase behaviour with logistic normal regression. Journal of the American Statistical Association 89, 1218–1231 (1994)

    Article  Google Scholar 

  • Arabie, P., Hubert, L.: Cluster analysis in market research. In: Bagozzi, R.P. (ed.) Advanced methods in marketing research, pp. 160–189. Blackwell & Company, Oxford (1994)

    Google Scholar 

  • Bishop, C.M., SvensĂ©n, M., Williams, C.K.I.: GTM: the Generative Topographic Mapping. Neural Computation 10(1), 215–234 (1998)

    Article  Google Scholar 

  • Bishop, C.M., SvensĂ©n, M., Williams, C.K.I.: Developments of the Generative Topographic Mapping. Neurocomputing 21(1-3), 203–224 (1998)

    Article  MATH  Google Scholar 

  • Chen, M.S., Han, J., Yu, P.S.: Data mining: An overview from a database perspective. IEEE Transactions on Knowledge and Data Engineering 8(6), 866–884 (1996)

    Article  Google Scholar 

  • Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, B 39(1), 1–38 (1977)

    MATH  MathSciNet  Google Scholar 

  • Firat, A.F., Shultz II, C.J.: From Segmentation to fragmentation: Markets and marketing strategy in the postmodern era. European Journal of Marketing 31(3-4), 183–207 (1997)

    Article  Google Scholar 

  • Gordon, M.E., De Lima-Turner, K.: Consumer attitudes towards Internet advertising: A social contract perspective. International Marketing Review 14(5), 362–375 (1997)

    Article  Google Scholar 

  • Green, P.E., Krieger, A.M.: Alternative approaches to cluster-based market segmentation. Journal of the Market Reseach Society 37(3), 221–239 (1995)

    Google Scholar 

  • Hinton, G.E., Williams, C.K.I., Revow, M.D.: Adaptive elastic models for handprinted character recognition. In: Moody, J.E., Hanson, S.J., Lippmann, R.P. (eds.) Advances in Neural Information Processing Systems, vol. 4, pp. 512–519. Morgan Kauffmann, San Francisco (1992)

    Google Scholar 

  • Kara, A., Kaynak, E.: Markets of a single customer: exploiting conceptual developments in market segmentation. European Journal of Marketing 31(11-12), 873–895 (1997)

    Article  Google Scholar 

  • Kehoe, C., Pitkow, J., Rogers, J.D.: 9th GVU’s WWW user survey (1998), http://www.gvu.gatech.edu/user_surveys/survey-1998-04/

  • Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43(1), 59–69 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  • Kohonen, T.: Self-Organizing Maps. Springer, Berlin (1995)

    Google Scholar 

  • Lewis, O.M., Ware, J.A., Jenkins, D.: A novel neural network technique for the valuation of residential property. Neural Computing & Applications 5(4), 224–229 (1997)

    Article  Google Scholar 

  • MacKay, D.J.C.: A practical Bayesian framework for back-propagation networks. Neural Computation 4(3), 448–472 (1992)

    Article  Google Scholar 

  • MacKay, D.J.C.: Probable networks and plausible predictions - a review of practical Bayesian methods for supervised neural networks network-computation in neural systems. Network: Computation in Neural Systems 6, 469–505 (1995)

    Article  MATH  Google Scholar 

  • McDonald, W.J.: Internet customer segments: an international perspective. In: Droge, C., Calantone, R. (eds.) Enhancing knowledge development in marketing, pp. 338–344. American Marketing Association, Chicago (1996)

    Google Scholar 

  • McLachlan, G.J., Basford, K.E.: Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York (1988)

    MATH  Google Scholar 

  • Ripley, B.: Pattern recognition and neural networks. Cambridge University Press, Cambridge (1996)

    MATH  Google Scholar 

  • Schaffer, C.M., Green, P.E.: Cluster-based market segmentation: some further comparisons of alternative approaches. Journal of the Market Research Society 40(2), 155–163 (1998)

    Google Scholar 

  • Scharl, A., Brandtweiner, R.: A conceptual research framework for analyzing the evolution of electronic markets. Electronic Markets Newsletter 8(2), 1–6 (1998)

    Google Scholar 

  • Serrano-Cinca, C.: Self-organizing neural networks for financial diagnosis. Decision Support Systems 17, 227–238 (1996)

    Article  Google Scholar 

  • Slater, D., Mulvenna, M., BĂĽchner, A., Moussy, L.: Mining marketing intelligence from Internet retailing data: user requirements & business process description. In: Proceedings of the European Multimedia, Microprocessor Systems and Electronic Commerce (EMMSEC 1999) Annual Conference, Stockholm, Sweden (1999)

    Google Scholar 

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

    Google Scholar 

  • Vellido, A., Lisboa, P.J.G., Meehan, K.: Characterizing and segmenting the business-toconsumer e-commerce market using neural networks. In: Lisboa, P.J.G., Vellido, A., Edisbury, B. (eds.) Neural Networks Mean Business, World Scientific, Singapore (2000) (to appear)

    Google Scholar 

  • Wallin, E.O.: Consumer personalization technologies for e-commerce on the Internet: a taxonomy. In: Proceedings of the European Multimedia, Microprocessor Systems and Electronic Commerce (EMMSEC 1999) Annual Conference, Stockholm, Sweden (1999)

    Google Scholar 

  • Wedel, M., Kamakura, W.A.: Market Segmentation. Conceptual and Methodological Foundations. International Series in Quantitative Marketing. Kluwer Academic Publishers, Massachusetts (1998)

    Google Scholar 

  • Wind, Y.: Issues and advances in segmentation research. Journal of Marketing Research 15, 317–337 (1978)

    Article  Google Scholar 

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Vellido, A., Lisboa, P.J.G., Meehan, K. (2000). Segmenting the e-Commerce Market Using the Generative Topographic Mapping. In: CairĂł, O., Sucar, L.E., Cantu, F.J. (eds) MICAI 2000: Advances in Artificial Intelligence. MICAI 2000. Lecture Notes in Computer Science(), vol 1793. Springer, Berlin, Heidelberg. https://doi.org/10.1007/10720076_43

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  • DOI: https://doi.org/10.1007/10720076_43

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-67354-5

  • Online ISBN: 978-3-540-45562-2

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