CRM in e-Business: a Client’s Life Cycle Model Based on a Neural Network

  • Oscar Marbán
  • Ernestina Menasalvas
  • César Montes
  • John G. Rajakulendran
  • Javier Segovia
Part of the Studies in Fuzziness and Soft Computing book series (STUDFUZZ, volume 105)


The competitive environment in which organisations are moving together with the arrival of the web has made it necessary the application of intelligent methods both to gather and to analyse information. Information gathered in the web represents only the first step in the problem. Integrating that information with information supplied by external providers is a need if the users behaviour is to be studied. In this paper we present a new approach that will make it possible to build adaptive web sites. Firstly according to the user attributes and his/her behaviour the probability to acquire certain products is obtained, later the propensity through his/her life cycle to buy different products either of the same category or different is obtained with the help of a Neural Network. This will also allow us to conduct different online marketing campaigns of cross-selling and up-selling.


Customer Relationship Management Life Cycle Model Woman Professional Binary Input Data Mining Process 
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.
    John S. Breese, David Heckerman, and Carl Kadie. (1998) Empirical analysis of predictive algorithms for collaborative filtering. In Gregory F. Cooper and Serafin Moral, editors, Proceedings of the 14th Conference on Uncertainty in Artificial Intelligence (UAI-98), pages 43–52, San Francisco, July 24–26. Morgan Kaufmann.Google Scholar
  2. 2.
    Joseph A. Konstan, Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl. (1997) GroupLens: Applying collaborative filtering to Usenet news. Communications of the ACM, 40(3):77–87, March.Google Scholar
  3. 3.
    Upendra Shardanand and Patti Maes. (1995) Social information filtering: Algorithms for automating \word of mouth“. In Proceedings of ACM CHI’95 Conference on Human Factors in Computing Systems, volume 1 of Papers: Using the Information of Others, pages 210–217.Google Scholar
  4. 4.
    Daniel Billsus and Michael J. Pazzani. (1998) Learning collaborative information filters. In Proc. 15th International Conf. on Machine Learning, pages 46–54. Morgan Kaufmann, San Francisco, CA.Google Scholar
  5. 5.
    Slodoban Vucetic and Zoran Obradovic. (2000) A regression based approach for scaling-up personalized recommeder systems in e-commerce. In The Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Workshop on Web Mining for E-Commerce - Challenges and Opportunities), August.Google Scholar
  6. 6.
    Lise Getoor and Mehran Sahami. Using probabiistic relational models for collaborative filtering.Google Scholar
  7. 7.
    Thomas Hofmann and Jan Puzicha. (1999) Latent class models for collaborative filtering. In Dean Thomas, editor, Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI-99-Vo12), pages 688–693, S.F., July 31-August 6. Morgan Kaufmann Publishers.Google Scholar
  8. 8.
    Simon, S. Shaffer 2001. Data Warehousing and Business Intelligence for e-commerce. Morgan.Kaufman 2001.Google Scholar
  9. 9.
  10. 10.
    Ansari S, Kohavi R, Mason L, Zheng Z, (2000). “Integrating E-Commerce and Data Mining: Archetecture and Challenges.” WEBKDD 2000 Workshop: Web Mining for E-Commerce - Challenges and Opportunities. http://robotics.Stanford.EDU/-ronnyk/WEBKDD2000/index.html
  11. 11.
  12. 12.
    Microsoft, Great Plains eEnterprise (2001). “Fully Integrated Customer Relationship Management Solutions.” Microsoft Corporation,
  13. 13. cID-24.pdf
  14. 14.
    Fingar P, Kumar H, Sharma T, (2000). “Enterprise E-Commerce.” Meghan-Kiffer Press. ISBN: 0929652118Google Scholar
  15. 15.
    Berry M.J.A Linoff G. (2000). “Mastering Data Mining; The Art and Science of Customer Relationship Management.” Wiley Computer Publishing. ISBN 0–47133123–6Google Scholar
  16. 16.
    Kimball R, Merz R, (2000). “The Data Webhouse Toolkit; Building The Web–Enabled Data Warehouse.” Wiley Computer Publishing. ISBN 0–471–37680–9Google Scholar
  17. 17.
    DM Review “Business Intelligence: Enabling E-Business.” Volume 10, Number 10, October 2000, p36–38Google Scholar
  18. 18.
    Han J, Kamber M, (2001). “Data Mining Concepts and Techniques.” Morgan Kaufmann Publishers. ISBN 1–55860–489–8Google Scholar
  19. 19.
    Andersen J, Giversen A, Jensen A, Larsen R.S, Pedersen T.B, Skyt J, (2000). “Analysing Clickstreams Using Subsessions.” ACMGoogle Scholar
  20. 20.
    Theusinger C, Huber KP, (2000). “Analysing the footsteps of your customers- A case study by ASKinet and SAS Institute GmbH.” SAS Institute GmbH, Heidelberg, GermanyGoogle Scholar
  21. 21.
  22. 22.
    Rudd O.P, (2001). “Data Mining Cookbook: Modelling Data for Marketing, Risk, and Customer Relationship Management.” Wiley Computer Publishing. ISBN 0–47138564–6Google Scholar
  23. 23.
    Hertz J, Krogh A, Palmer R (1991). “Introduction to the theory of Neural Computation”, Santa Fe Institute, Addison-Wesley.Google Scholar
  24. 24.
    Avatars Virtual Technologies (2001). “Strategies in Internet”. White Paper.
  25. 25.
    Ron Kohavi, Carla Brodley, Brian Frasca, Llew Mason, and Zijian Zheng. KDD-Cup 2000 organizers’ report: Peeling the onion. SIGKDD Explorations, 2(2):86–98, 2000.

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Oscar Marbán
    • 1
  • Ernestina Menasalvas
    • 2
  • César Montes
    • 3
  • John G. Rajakulendran
    • 4
  • Javier Segovia
    • 2
  1. 1.Departamento de InformáticaUniversidad Carlos III de MadridLeganés, MadridSpain
  2. 2.DLSIS, Facultad de InformaticaUniversidad PolitécnicaMadridSpain
  3. 3.DIA, Facultad de InformaticaUniversidad PolitécnicaMadridSpain
  4. 4.University of NewcasttleUK

Personalised recommendations