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Category Pricing with State-Dependent Utility

Author

Listed:
  • Jean-Pierre Dubé

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

  • Günter J. Hitsch

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

  • Peter E. Rossi

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

  • Maria Ana Vitorino

    (Graduate School of Business, University of Chicago, Chicago, Illinois 60637)

Abstract

There is substantial literature documenting the presence of state-dependent utility with packaged goods data. Typically, a form of brand loyalty is detected whereby there is a higher probability of purchasing the same brand as has been purchased in the recent past. The economic significance of the measured loyalty remains an open question. We consider the category pricing problem and demonstrate that the presence of loyalty materially affects optimal pricing. The prices of higher quality products decline relative to those of lower quality when loyalty is introduced into the model. Given the well-known problems with the confounding of state dependence and consumer heterogeneity, loyalty must be measured in a model which allows for an unknown and possibly highly nonnormal distribution of heterogeneity. We implement a highly flexible model of heterogeneity using multivariate mixtures of normals in a hierarchical choice model. We use an Euler equations approach to the solution of the dynamic pricing problem which allows us to consider a very large number of consumer types.

Suggested Citation

  • Jean-Pierre Dubé & Günter J. Hitsch & Peter E. Rossi & Maria Ana Vitorino, 2008. "Category Pricing with State-Dependent Utility," Marketing Science, INFORMS, vol. 27(3), pages 417-429, 05-06.
    • Handle: RePEc:inm:ormksc:v:27:y:2008:i:3:p:417-429
    DOI: 10.1287/mksc.1070.0305
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    References listed on IDEAS

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    1. Peter M. Guadagni & John D. C. Little, 1983. "A Logit Model of Brand Choice Calibrated on Scanner Data," Marketing Science, INFORMS, vol. 2(3), pages 203-238.
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    5. P. B. Seetharaman, 2004. "Modeling Multiple Sources of State Dependence in Random Utility Models: A Distributed Lag Approach," Marketing Science, INFORMS, vol. 23(2), pages 263-271, April.
    6. Tülin Erdem, 1996. "A Dynamic Analysis of Market Structure Based on Panel Data," Marketing Science, INFORMS, vol. 15(4), pages 359-378.
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