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Adjusting Choice Models to Better Predict Market Behavior

Author

Listed:
  • Greg Allenby
  • Geraldine Fennell
  • Joel Huber
  • Thomas Eagle
  • Tim Gilbride
  • Dan Horsky
  • Jaehwan Kim
  • Peter Lenk
  • Rich Johnson
  • Elie Ofek
  • Bryan Orme
  • Thomas Otter
  • Joan Walker

Abstract

The emergence of Bayesian methodology has facilitated respondent-level conjoint models, and deriving utilities from choice experiments has become very popular among those modeling product line decisions or new product introductions. This review begins with a paradox of why experimental choices should mirror market behavior despite clear differences in content, structure and motivation. It then addresses ways to design the choice tasks so that they are more likely to reflect market choices. Finally, it examines ways to model the results of the choice experiments to better mirror both underlying decision processes and potential market choices. Copyright Springer Science + Business Media, Inc. 2005

Suggested Citation

  • Greg Allenby & Geraldine Fennell & Joel Huber & Thomas Eagle & Tim Gilbride & Dan Horsky & Jaehwan Kim & Peter Lenk & Rich Johnson & Elie Ofek & Bryan Orme & Thomas Otter & Joan Walker, 2005. "Adjusting Choice Models to Better Predict Market Behavior," Marketing Letters, Springer, vol. 16(3), pages 197-208, December.
    • Handle: RePEc:kap:mktlet:v:16:y:2005:i:3:p:197-208
    DOI: 10.1007/s11002-005-5885-1
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    References listed on IDEAS

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    1. Toubia, Olivier & Hauser, John & Simester, Duncan, 2003. "Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis," Working papers 4285-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    2. Dan Horsky & Paul Nelson, 1992. "New Brand Positioning and Pricing in an Oligopolistic Market," Marketing Science, INFORMS, vol. 11(2), pages 133-153.
    3. Elie Ofek & V. Srinivasan, 2002. "How Much Does the Market Value an Improvement in a Product Attribute?," Marketing Science, INFORMS, vol. 21(4), pages 398-411, June.
    4. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, January.
    5. Peter J. Lenk & Ambar G. Rao, 1990. "New Models from Old: Forecasting Product Adoption by Hierarchical Bayes Procedures," Marketing Science, INFORMS, vol. 9(1), pages 42-53.
    6. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    7. Huber, Joel & Ariely, Dan & Fischer, Gregory, 2002. "Expressing Preferences in a Principal-Agent Task: A Comparison of Choice, Rating, and Matching," Organizational Behavior and Human Decision Processes, Elsevier, vol. 87(1), pages 66-90, January.
    8. Greg M. Allenby & Thomas S. Shively & Sha Yang & Mark J. Garratt, 2004. "A Choice Model for Packaged Goods: Dealing with Discrete Quantities and Quantity Discounts," Marketing Science, INFORMS, vol. 23(1), pages 95-108, June.
    9. David R. Bell & James M. Lattin, 2000. "Looking for Loss Aversion in Scanner Panel Data: The Confounding Effect of Price Response Heterogeneity," Marketing Science, INFORMS, vol. 19(2), pages 185-200, May.
    10. Jaehwan Kim & Greg M. Allenby & Peter E. Rossi, 2002. "Modeling Consumer Demand for Variety," Marketing Science, INFORMS, vol. 21(3), pages 229-250, December.
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