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Price Promotions in Choice Models

Author

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  • John R. Howell

    (Smeal College of Business, Pennsylvania State University, University Park, Pennsylvania 16802)

  • Sanghak Lee

    (Tippie College of Business, University of Iowa, Iowa City, Iowa 52242)

  • Greg M. Allenby

    (Fisher College of Business, Ohio State University, Columbus, Ohio 43210)

Abstract

Promotions are used in marketing to increase sales and drive profits by temporarily decreasing the price per unit of a good. Some price promotions apply to all quantities (20% off), some have limits on the number of units that can be purchased at a reduced price, and others only offer the discount if the volume purchased is sufficiently high. We develop a model of price promotions in the context of a direct utility model where its effects are incorporated through the budget constraint. Price promotions complicate the estimation and analysis of direct utility models because they induce kinks and points of discontinuity in the budget set. We propose a Bayesian approach to addressing these irregularities and demonstrate the ability of the direct utility model to be used in counterfactual analyses of price promotions. We investigate the stability of utility function estimates for consumers under alternative price promotions, and find that the majority of the effect of a price promotion is through the budget set, not through changes in the utility function. We also investigate the economic value of customized price promotions where the customization includes the value and format of the offer.Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2015.0948 .

Suggested Citation

  • John R. Howell & Sanghak Lee & Greg M. Allenby, 2016. "Price Promotions in Choice Models," Marketing Science, INFORMS, vol. 35(2), pages 319-334, March.
    • Handle: RePEc:inm:ormksc:v:35:y:2016:i:2:p:319-334
    DOI: 10.1287/mksc.2015.0948
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    References listed on IDEAS

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    Cited by:

    1. Jean-Pierre H. Dubé, 2018. "Microeconometric Models of Consumer Demand," NBER Working Papers 25215, National Bureau of Economic Research, Inc.
    2. Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2023. "Optimal Price Targeting," Marketing Science, INFORMS, vol. 42(3), pages 476-499, May.
    3. Sanghak Lee & Sunghoon Kim & Sungho Park, 2022. "A sequential choice model for multiple discrete demand," Quantitative Marketing and Economics (QME), Springer, vol. 20(2), pages 141-178, June.
    4. Hung Tran & Tien Mai, 2023. "Network-based Representations and Dynamic Discrete Choice Models for Multiple Discrete Choice Analysis," Papers 2306.04606, arXiv.org.
    5. Kim, Youngju & Hardt, Nino & Kim, Jaehwan & Allenby, Greg M., 2022. "Conjunctive screening in models of multiple discreteness," International Journal of Research in Marketing, Elsevier, vol. 39(4), pages 1209-1234.

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