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Application, Predictive Test, and Strategy Implications for a Dynamic Model of Consumer Response

Author

Listed:
  • John R. Hauser

    (Alfred P. Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • Kenneth J. Wisniewski

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

Abstract

This paper describes and evaluates the application of a dynamic stochastic model of consumer response. The model describes, then forecasts, how consumers respond to a new transportation service and to the marketing strategies used during its introduction. The model is estimated on survey data during the first 11 weeks of service. Forecasts over the next 19 weeks are then compared to actual ridership as measured by dispatch records. The model is simple. At any point in time, consumers are described by a set of “behavioral states”, indicating (1) whether they are aware of the new service (DART) and (2) what mode of transportation was used for their last trip. Behavior is described by movement among behavioral states. E.G., If a car user tries DART, he makes a transition from ‘car used for last trip' to ‘DART used for last trip'. The transition probabilities and the rate of transition are dependent on marketing strategies (direct mail, publicity), word of mouth, consumer perceptions, availability of a mode, and budget allocation to transportation. The advantages and disadvantages of the model and the measurements are discussed with respect to predictive ability and managerial utility.

Suggested Citation

  • John R. Hauser & Kenneth J. Wisniewski, 1982. "Application, Predictive Test, and Strategy Implications for a Dynamic Model of Consumer Response," Marketing Science, INFORMS, vol. 1(2), pages 143-179.
    • Handle: RePEc:inm:ormksc:v:1:y:1982:i:2:p:143-179
    DOI: 10.1287/mksc.1.2.143
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    Cited by:

    1. Ferreira, Kevin D. & Lee, Chi-Guhn, 2014. "An integrated two-stage diffusion of innovation model with market segmented learning," Technological Forecasting and Social Change, Elsevier, vol. 88(C), pages 189-201.
    2. Ralf van der Lans & Gerrit van Bruggen & Jehoshua Eliashberg & Berend Wierenga, 2010. "A Viral Branching Model for Predicting the Spread of Electronic Word of Mouth," Marketing Science, INFORMS, vol. 29(2), pages 348-365, 03-04.
    3. 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.
    4. Urban, Glen L. & Weinberg, Bruce D. & Hauser, John R., 1994. "Premarket forecasting of really new products," Working papers 3689-94., Massachusetts Institute of Technology (MIT), Sloan School of Management.
    5. 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.

    More about this item

    Keywords

    consumer model; diffusion of innovations;

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