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Market Share Constraints and the Loss Function in Choice-Based Conjoint Analysis

Author

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  • Timothy J. Gilbride

    () (Mendoza College of Business, University of Notre Dame, Notre Dame, Indiana 46556)

  • Peter J. Lenk

    () (Stephen M. Ross School of Business, University of Michigan, Ann Arbor, Michigan 48109)

  • Jeff D. Brazell

    () (The Modellers, LLC, Salt Lake City, Utah 84047)

Abstract

Choice-based conjoint analysis is a popular marketing research technique to learn about consumers' preferences and to make market share forecasts under various scenarios for product offerings. Managers expect these forecasts to be “realistic” in terms of being able to replicate market shares at some prespecified or “base-case” scenario. Frequently, there is a discrepancy between the recovered and base-case market share. This paper presents a Bayesian decision theoretic approach to incorporating base-case market shares into conjoint analysis via the loss function. Because defining the base-case scenario typically involves a variety of management decisions, we treat the market shares as constraints on what are acceptable answers, as opposed to informative prior information. Our approach seeks to minimize the adjustment of parameters by using additive factors from a normal distribution centered at 0, with a variance as small as possible, but such that the market share constraints are satisfied. We specify an appropriate loss function, and all estimates are formally derived via minimizing the posterior expected loss. We detail algorithms that provide posterior distributions of constrained and unconstrained parameters and quantities of interest. The methods are demonstrated using discrete choice models with simulated data and data from a commercial market research study. These studies indicate that the method recovers base-case market shares without systematically distorting the preference structure from the conjoint experiment.

Suggested Citation

  • Timothy J. Gilbride & Peter J. Lenk & Jeff D. Brazell, 2008. "Market Share Constraints and the Loss Function in Choice-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 27(6), pages 995-1011, 11-12.
  • Handle: RePEc:inm:ormksc:v:27:y:2008:i:6:p:995-1011
    DOI: 10.1287/mksc.1080.0369
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    File URL: http://dx.doi.org/10.1287/mksc.1080.0369
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    References listed on IDEAS

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    Cited by:

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    9. Merja Halme & Outi Somervuori, 2013. "Choice behavior of information services when prices are increased and decreased from reference level," Annals of Operations Research, Springer, vol. 211(1), pages 549-564, December.
    10. Mesa-Arango, Rodrigo & Ukkusuri, Satish V., 2014. "Attributes driving the selection of trucking services and the quantification of the shipper’s willingness to pay," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 71(C), pages 142-158.
    11. John R. Hauser & Felix Eggers & Matthew Selove, 2019. "The Strategic Implications of Scale in Choice-Based Conjoint Analysis," Marketing Science, INFORMS, vol. 38(6), pages 1059-1081, November.
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