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Dynamic Analysis of Consumer Response to Marketing Strategies


  • John R. Hauser

    (Massachusetts Institute of Technology)

  • Kenneth J. Wisniewski

    (University of Chicago)


This paper develops a methodology for modeling consumer response that integrates previous research in stochastic brand selection, diffusion of innovation, test market analysis, and new product design. The methodology makes it practical to extend brand selection models to include diffusion phenomena such as awareness, trial, and information flow. Purchase timing and brand selection are interdependent and both phenomena depend jointly on managerial controls such as advertising, coupons, price-off promotion, product positioning, and consumer characteristics. Within this general structure, we provide practical estimation procedures (a least squares approximation to the maximum likelihood estimates) to determine the parameters which link managerial controls to consumer response. Closed form solutions are derived for cumulative awareness, cumulative trial, penetration, expected sales, and purchases due to promotion---all as a function of time. We also provide simplified expressions for equilibrium (t -> \infty ) market share. Tradeoffs among complexity of the diffusion process, number of managerial variables, nonstationarity, complexity of purchase timing, consumer segmentation, and sample size are made explicit so that the marketing scientist can customize his analyses to the managerial problems that he faces. The effects of sample size, data interval frequency, and collinearity in the explanatory variables are investigated with simulations based on a five-state consumer response process which depends on 8--10 marketing variables. The paper closes with a brief description of the application and predictive test of a consumer response model based on the methodology.

Suggested Citation

  • John R. Hauser & Kenneth J. Wisniewski, 1982. "Dynamic Analysis of Consumer Response to Marketing Strategies," Management Science, INFORMS, vol. 28(5), pages 455-486, May.
  • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:5:p:455-486

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    Cited by:

    1. Gary L. Lilien, 2004. "Special Section Introduction by the ISMS Practice Prize Competition Chairman," Marketing Science, INFORMS, vol. 23(2), pages 180-191.
    2. John Hauser & Gerard J. Tellis & Abbie Griffin, 2006. "Research on Innovation: A Review and Agenda for," Marketing Science, INFORMS, vol. 25(6), pages 687-717, 11-12.
    3. repec:eee:ijrema:v:30:y:2013:i:2:p:101-113 is not listed on IDEAS
    4. Jan Kaluski, 2000. "An Analytical Method To Calculate The Ergodic And Difference Matrices Of The Discounted Markov Decision Processes," Computing in Economics and Finance 2000 235, Society for Computational Economics.
    5. Islam, Towhidul, 2014. "Household level innovation diffusion model of photo-voltaic (PV) solar cells from stated preference data," Energy Policy, Elsevier, vol. 65(C), pages 340-350.
    6. John H. Roberts & Charles J. Nelson & Pamela D. Morrison, 2005. "A Prelaunch Diffusion Model for Evaluating Market Defense Strategies," Marketing Science, INFORMS, vol. 24(1), pages 150-164, August.
    7. Min Ding & Jehoshua Eliashberg, 2008. "A Dynamic Competitive Forecasting Model Incorporating Dyadic Decision Making," Management Science, INFORMS, vol. 54(4), pages 820-834, April.
    8. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    9. Jehoshua Eliashberg & Jedid-Jah Jonker & Mohanbir S. Sawhney & Berend Wierenga, 2000. "MOVIEMOD: An Implementable Decision-Support System for Prerelease Market Evaluation of Motion Pictures," Marketing Science, INFORMS, vol. 19(3), pages 226-243, January.
    10. Kim, Sang-Hoon & Srinivasan, V. Seenu, 2006. "A Conjoint-Hazard Model of the Timing of Buyers' Upgrading to Improved Versions of High Technology Products," Research Papers 1720r1, Stanford University, Graduate School of Business.

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    marketing; consumer behavior; Markov analysis;


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