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E-Loyalty Simulation Based on Hidden Markov Model

  • Juanjuan Chen
  • Chengliang Wang
  • Xiangjun Peng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 216)

Abstract

With the rapid development of E-retailing business, customer loyalty management becomes more important to E-retailers. However, E-loyalty is not observable from the perspective of merchants, there should have data mining before recognizing and grouping consumers. Moreover, the evolution process of customer loyalty shows dynamic, stochastic and non-after-effect characteristics, which can be called as a Markov process. The paper explores how Hidden Markov model can be applied on E-loyalty researches. Combining with K-mean clustering method, this paper builds the HMM-based E-loyalty simulation model, including transition matrix of customer loyalty and transaction behavior. Detailed experimental results are given in the last part.

Keywords

Hidden Markov model Customer E-loyalty K-means clustering Electronic Commerce 

Notes

Acknowledgments

This paper is sponsored by the Bilingual Course Foundation of Chongqing Normal University.

References

  1. 1.
    Laudon KC, Traver CG (2010) E-commerce business technology society, vol 20, pp 27–34Google Scholar
  2. 2.
    Griffin J, Loyalty C (2005) How to earn it, how to keep it, vol 18. Jossey-Bass Publishing, p 22Google Scholar
  3. 3.
    Luarn P, Lin HH (2003) A customer loyalty model for e-service context. J Electron Commer Res 12:156–167Google Scholar
  4. 4.
    Gefen D (2002) Customer loyalty in e-commerce. J Assoc Inf Syst 3:27–51Google Scholar
  5. 5.
    Reichheld FF et al (2000) E-loyalty: you secret weapon on the web. Harvard Bus Rev 21:390–397Google Scholar
  6. 6.
    Chaudhuri A, Holbrook MB (2001) The chain of effects from brand trust and brand affect to brand performance: the role of brand loyalty. J Mark 65(2):81–93CrossRefGoogle Scholar
  7. 7.
    Rabiner LR (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proc IEEE 77(2):181–187CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London 2013

Authors and Affiliations

  1. 1.College Of Computer and Information ScienceChongqing Normal UniversityChongqingChina

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