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Market response models and marketing practice

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  • Dominique M. Hanssens
  • Peter S. H. Leeflang
  • Dick R. Wittink

Abstract

Market response models are intended to help scholars and managers understand how consumers individually and collectively respond to marketing activities, and how competitors interact. Appropriately estimated effects constitute a basis for improved decision making in marketing. We review the demand and supply of market response models and we highlight areas of future growth. We discuss two characteristics that favour model use in practice, viz. the supply of standardized models and the availability of empirical generalizations. Marketing as a discipline and market response models as a technology may often not receive top management attention. In order to have enhanced relevance for senior management, we argue that marketing models should be cross‐functional, include short‐ and long‐term effects, and be considerate of capital markets. We also identify emerging opportunities for marketing model applications in areas such as public policy and litigation. Copyright © 2005 John Wiley & Sons, Ltd.

Suggested Citation

  • Dominique M. Hanssens & Peter S. H. Leeflang & Dick R. Wittink, 2005. "Market response models and marketing practice," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 21(4‐5), pages 423-434, July.
    • Handle: RePEc:wly:apsmbi:v:21:y:2005:i:4-5:p:423-434
    DOI: 10.1002/asmb.584
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    Cited by:

    1. Somayeh Moazeni & Boris Defourny & Monika J. Wilczak, 2020. "Sequential Learning in Designing Marketing Campaigns for Market Entry," Management Science, INFORMS, vol. 66(9), pages 4226-4245, September.
    2. P D Berger & J Lee & B D Weinberg, 2006. "Optimal cooperative advertising integration strategy for organizations adding a direct online channel," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(8), pages 920-927, August.
    3. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2014. "Model Selection Using Database Characteristics: Developing a Classification Tree for Longitudinal Incidence Data," Marketing Science, INFORMS, vol. 33(2), pages 188-205, March.

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