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Moment-to-Moment Optimal Branding in TV Commercials: Preventing Avoidance by Pulsing

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Author Info

  • Thales S. Teixeira

    ()
    (Marketing Unit, Harvard Business School, Boston, Massachusetts 02163)

  • Michel Wedel

    ()
    (Smith School of Business, University of Maryland, College Park, Maryland 20742)

  • Rik Pieters

    ()
    (Department of Marketing, Tilburg University, 5000 LE Tilburg, The Netherlands)

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    Abstract

    We develop a conceptual framework about the impact that branding activity (the audiovisual representation of brands) and consumers' focused versus dispersed attention have on consumer moment-to-moment avoidance decisions during television advertising. We formalize this framework in a dynamic probit model and estimate it with Markov chain Monte Carlo methods. Data on avoidance through zapping, along with eye tracking on 31 commercials for nearly 2,000 participants, are used to calibrate the model. New, simple metrics of attention dispersion are shown to strongly predict avoidance. Independent of this, central on-screen brand positions, but not brand size, further promote commercial avoidance. Based on the model estimation, we optimize the branding activity that is under marketing control for ads in the sample to reduce commercial avoidance. This reveals that brand pulsing--while keeping total brand exposure constant--decreases commercial avoidance significantly. Both numerical simulations and a controlled experiment using regular and edited commercials, respectively, provide evidence of the benefits of brand pulsing to ward off commercial avoidance. Implications for advertising management and theory are addressed.

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    File URL: http://dx.doi.org/10.1287/mksc.1100.0567
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    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 29 (2010)
    Issue (Month): 5 (09-10)
    Pages: 783-804

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    Handle: RePEc:inm:ormksc:v:29:y:2010:i:5:p:783-804

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    Related research

    Keywords: commercial avoidance; branding; attention; dynamic probit model; optimization; pulsing;

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    Cited by:
    1. Campbell, Benjamin L. & Behe, Bridget K. & Khachatryan, Hayk & Hall, Charles R. & Dennis, Jennifer H. & Huddleston, Patricia T. & Fernandez, R. Thomas, 2013. "Incorporating Eye Tracking Technology and Conjoint Analysis to Better Understand the Green Industry Consumer," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150431, Agricultural and Applied Economics Association.
    2. Khachatryan, Hayk & Behe, Bridget K. & Campbell, Benjamin L. & Hall, Charles & Dennis, Jennifer H., 2013. "Does Eye Tracking Reveal More About the Effects of Buying Impulsiveness on the Green Industry Consumer Choice Behavior?," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 150333, Agricultural and Applied Economics Association.

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