IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v28y1982i5p455-486.html
   My bibliography  Save this article

Dynamic Analysis of Consumer Response to Marketing Strategies

Author

Listed:
  • John R. Hauser

    (Massachusetts Institute of Technology)

  • Kenneth J. Wisniewski

    (University of Chicago)

Abstract

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
    DOI: 10.1287/mnsc.28.5.455
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.28.5.455
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.28.5.455?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. Hauser, John R., 2014. "Consideration-set heuristics," Journal of Business Research, Elsevier, vol. 67(8), pages 1688-1699.
    3. 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.
    4. 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.
    5. Liberali, Guilherme & Urban, Glen L. & Hauser, John R., 2013. "Competitive information, trust, brand consideration and sales: Two field experiments," International Journal of Research in Marketing, Elsevier, vol. 30(2), pages 101-113.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. Antonello Maruotti & Jan Bulla & Tanya Mark, 2019. "Assessing the influence of marketing activities on customer behaviors: a dynamic clustering approach," METRON, Springer;Sapienza Università di Roma, vol. 77(1), pages 19-42, April.
    11. Min Ding & Jehoshua Eliashberg, 2008. "A Dynamic Competitive Forecasting Model Incorporating Dyadic Decision Making," Management Science, INFORMS, vol. 54(4), pages 820-834, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:28:y:1982:i:5:p:455-486. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.