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The Evolution of Internal Market Structure

Author

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  • Oliver J. Rutz

    (Yale School of Management, Yale University, New Haven, Connecticut 06520)

  • Garrett P. Sonnier

    (Red McCombs School of Business, University of Texas at Austin, Austin, Texas 78712)

Abstract

We present a dynamic factor-analytic choice model to capture evolution of brand positions in latent attribute space. Our dynamic model allows researchers to investigate brand positioning in new categories or mature categories affected by structural change such as entry. We argue that even for mature categories not affected by structural change, the assumption of stable attributes may be untenable. We allow for evolution in attributes by modeling individual-level time-specific attributes as arising from dynamic means. The dynamic attribute means are modeled as a Bayesian dynamic linear model (DLM). The DLM is nested within a factor-analytic choice model. Our approach makes efficient use of the data by leveraging estimates from previous and future periods to estimate current period attributes. We demonstrate the robustness of our model with data that simulate a variety of dynamic scenarios, including stationary behavior. We show that misspecified attribute dynamics induce temporal heteroskedasticty and correlation between the preference weights and the error term. Applying the model to a panel data set on household purchases in the malt beverage category, we find considerable evidence for dynamics in the latent brand attributes. From a managerial perspective, we find advertising expenditures help explain variation in the dynamic attribute means.

Suggested Citation

  • Oliver J. Rutz & Garrett P. Sonnier, 2011. "The Evolution of Internal Market Structure," Marketing Science, INFORMS, vol. 30(2), pages 274-289, 03-04.
    • Handle: RePEc:inm:ormksc:v:30:y:2011:i:2:p:274-289
    DOI: 10.1287/mksc.1100.0620
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    2. Guhl, Daniel & Baumgartner, Bernhard & Kneib, Thomas & Steiner, Winfried J., 2018. "Estimating time-varying parameters in brand choice models: A semiparametric approach," International Journal of Research in Marketing, Elsevier, vol. 35(3), pages 394-414.
    3. Bernhard Baumgartner & Daniel Guhl & Thomas Kneib & Winfried J. Steiner, 2018. "Flexible estimation of time-varying effects for frequently purchased retail goods: a modeling approach based on household panel data," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 40(4), pages 837-873, October.
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    6. van Ewijk, Bernadette J. & Gijsbrechts, Els & Steenkamp, Jan-Benedict E.M., 2022. "What drives brands’ price response metrics? An empirical examination of the Chinese packaged goods industry," International Journal of Research in Marketing, Elsevier, vol. 39(1), pages 288-312.

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