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Measuring long-run marketing effects and their implications for long-run marketing decisions

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Abstract

This paper discusses the role of agents’ beliefs and their implications for the economic modeling of their behavior, in particular, their behavior over time. The paper also discusses the corresponding planning problems facing both firms and consumers in their current decision making. After a general discussion of the consumer and firm problem, we discuss recent examples of some of the emerging empirical literature on dynamic choice behavior in marketing.

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Notes

  1. These three features exist for forward-looking dynamic structural models of market interactions (e.g., forward-looking structural dynamic models of competition) as well. Given the availability of excellent review papers on the solution and the estimation of dynamic structural models, we do not review such technical issues in this paper. Interested readers can refer to Amman and Rust (1995) and Rust (1994) for detailed information on this topic. For more very recent developments such as two-stage methods of estimation, please refer to Pakes et al. (2003) and Bajari et al. (2007a).

  2. The recent case surrounding the browser war between Microsoft and Netscape (United States v. Microsoft, 87 F. Supp. 2d 30 and Bresnahan 2001) highlights the importance both to academics and to practitioners of understanding the dynamics of a standards war.

  3. Other Bayesian approaches have been proposed that add priors on the distribution of the demand shocks (e.g., Musalem et al. 2008a, b; Jiang et al. 2007). These are additional parametric assumptions that are not typically included in the GMM approaches. It is unclear how the additional demand moments proposed by Albuquerque and Bronnenberg (2008) would be incorporated into a likelihood-based framework. However, their findings do raise a concern about the extent to which identification in the Bayesian approaches arises from the data versus from the additional prior information on demand shocks.

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Authors and Affiliations

  1. The Fuqua School of Business, Duke University, Durham, NC, 27708, USA

    Carl F. Mela

  2. CentER and Department of Economics and Business, Tilburg University, Warandelaan 2, 5037, AB, Tilburg, The Netherlands

    Bart J. Bronnenberg

  3. Graduate School of Business, University of Chicago, 5807 South Woodlawn Avenue, Chicago, IL, 60637, USA

    Jean Pierre Dubé & Guenter Hitsch

  4. William E. Simon Graduate School of Business, University of Rochester, Rochester, NY, 14627, USA

    Paulo Albuquerque

  5. Leonard N. Stern School of Business, New York University, 40 West 4th Street, Room 913, New York, NY, 10012, USA

    Tulin Erdem

  6. Columbia Business School, Columbia University, 3022 Broadway, New York, NY, 10027, USA

    Brett Gordon

  7. Anderson School of Management, University of California Los Angeles, 110 Westwood Plaza, Suite B417, Los Angeles, CA, 90095, USA

    Dominique Hanssens

  8. Department of Economics, Stanford University, 579 Serra Mall, Stanford, CA, 94305-6072, USA

    Han Hong

  9. Department of Marketing, Tepper School of Business, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, USA

    Baohong Sun

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  1. Bart J. Bronnenberg
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  2. Jean Pierre Dubé
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  3. Carl F. Mela
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  4. Paulo Albuquerque
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  5. Tulin Erdem
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  7. Dominique Hanssens
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  8. Guenter Hitsch
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  9. Han Hong
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  10. Baohong Sun
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Correspondence to Carl F. Mela.

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Bronnenberg, B.J., Dubé, J.P., Mela, C.F. et al. Measuring long-run marketing effects and their implications for long-run marketing decisions. Mark Lett 19, 367–382 (2008). https://doi.org/10.1007/s11002-008-9055-0

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