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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)

Abstract

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.

Keywords

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.

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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

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