Advertisement

KDD in Marketing with Genetic Fuzzy Systems

  • Jorge Casillas
  • Francisco J. Martínez-López

This publication is the fruit of a collaborative research between academics from the marketing and the artificial intelligence fields. It presents a brand new methodology to be applied in marketing (causal) modeling. Specifically, we apply it to a consumer behavior model used for the experimentation. The characteristics of the problem (with uncertain data and available knowledge from a marketing expert) and the multiobjective optimization we propose make genetic fuzzy systems a good tool for tackling it. In sum, by applying this methodology we obtain useful information patterns (fuzzy rules) which help to better understand the relations among the elements of the marketing system (causal model) being analyzed; in our case, a consumer model.

Keywords

Fuzzy Rule Pareto Front Membership Degree Risk Averseness Linguistic Term 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Beynon M, Curry B, Morgan P. 2001 Knowledge discovery in marketing. An approach through rough set theory. European Journal of Marketing 35(7/8): 915-935.CrossRefGoogle Scholar
  2. ınez FJ 2004 Fuzzy association rules for estimating consumer behaviour models and their application to explaining trust in Internet shopping. Fuzzy Economic Review IX(2): 3-26.Google Scholar
  3. Csikszentmihalyi M 1975 Play and intrinsic rewards. Journal of Humanistic Psychology 15(3): 41-63.Google Scholar
  4. Csikszentmihalyi M 1977 Beyond boredom and anxiety (Second edition). San Francisco: Jossey-Bass.Google Scholar
  5. Deb K, Pratap A, Agarwal S, Meyarevian T 2002 A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation 6 (2): 182-197.CrossRefGoogle Scholar
  6. Dubois D, Prade H, Sudkamp T 2005 On the representation, measurement, and discovery of fuzzy associations. IEEE Transactions on Fuzzy Systems 13(2): 250-262.CrossRefGoogle Scholar
  7. Fish KE, Johnson JD, Dorsey RE, Blodgett JG 2004 Using an artificial neural network trained with a genetic algorithm to model brand share. Journal of Business Research 57 (1): 79-85.CrossRefGoogle Scholar
  8. Gatignon H 2000 Commentary on Peter Leeflang and Dick Wittink’s “Building models form marketing decisions: past, present and future”. International Journal of Research in Marketing 17: 209-214.CrossRefGoogle Scholar
  9. Hoffman D, Novak T 1996 Marketing in hypermedia computer-mediated environments: conceptual foundations Journal of Marketing 60 (July): 50-68.Google Scholar
  10. Hurley S, Moutinho L, Stephens NM 1995 Solving marketing optimization problems using genetic algorithms. European Journal of Marketing 29 (4): 39-56.CrossRefGoogle Scholar
  11. Korzaan ML (2003) Going with the flow: predicting online purchase intentions. Journal of Computer Information Systems (Summer): 25-31.Google Scholar
  12. Lavrac N, Cestnik B, Gamberger D, Flach P 2004 Decision support through subgroup discovery: three case studies and the lessons learned. Machine Learning 57 (1-2): 115-143.MATHCrossRefGoogle Scholar
  13. Lee, B.C.Y. 2007 Consumer attitude toward virtual stores and its correlates: Journal of Retailing and Consumer Services 14(3): 182-191.Google Scholar
  14. Levy JB, Yoon E 1995 Modeling global market entry decision by fuzzy logic with an application to country risk assessment. European Journal of Operational Research 82: 53-78.MATHCrossRefGoogle Scholar
  15. Luna D, Peracchio LA, De Juan MD 2002 Cross-cultural and cognitive aspects of Web site navigation. Journal of the Academy of Marketing Science 30(4): 397-410.CrossRefGoogle Scholar
  16. Novak T, Hoffman D, Duhachek A 2003 The influence of goal-directed and experiential activities on online flow experiences. Journal of Consumer Psychology 13 (1/2): 3-16.Google Scholar
  17. Novak T, Hoffman D, Yung Y 2000 Measuring the customer experience in online environments: A structural modeling approach. Marketing Science 19 (1): 22-42.Google Scholar
  18. Rhim H, Cooper LG 2005 Assessing potential threats to incumbent brands: New product positioning under price competition in a multisegmented market. International Journal of Research in Marketing 22: 159-182.CrossRefGoogle Scholar
  19. Ruspini E 1969 A new approach to clustering, Information and Control 15: 22-32.MATHCrossRefGoogle Scholar
  20. Shim JP, Warkentin M, Courtney JF, Power, DJ, Sharda R, Carlsson C 2002 Past, present and future of decision support technology. Decision Support Systems 33: 111-126.CrossRefGoogle Scholar
  21. Steenkamp J, Baumgartner H 2000 On the use of structural equation models for marketing modeling. International Journal of Research in Marketing 17: 195-202.CrossRefGoogle Scholar
  22. Wedel M, Kamakura W. Böckenholt U 2000 Marketing data, models and decisions. International Journal of Research in Marketing 17: 203-208.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2008

Authors and Affiliations

  • Jorge Casillas
    • 1
  • Francisco J. Martínez-López
    • 2
  1. 1.Department of Computer Science and Artificial IntelligenceUniversity of GranadaSpain
  2. 2.Department of MarketingUniversity of GranadaSpain

Personalised recommendations