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Optimal Internet Media Selection

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Author Info

  • Peter J. Danaher

    ()
    (Melbourne Business School, Carlton, Victoria 3053, Australia)

  • Janghyuk Lee

    ()
    (Korea University Business School, An-am, Seong-buk, Seoul, South Korea)

  • Laoucine Kerbache

    ()
    (Department of OMIT and the Research Center GREGHEC, HEC School of Management, 78351 Paris, France)

Abstract

In this study we develop a method that optimally selects online media vehicles and determines the number of advertising impressions that should be purchased and then served from each chosen website. As a starting point, we apply Danaher's [Danaher, P. J. 2007. Modeling page views across multiple websites with an application to Internet reach and frequency prediction. (3) 422–437] multivariate negative binomial distribution (MNBD) for predicting online media exposure distributions. The MNBD is used as a component in the broader task of media selection. Rather than simply adapting previous selection methods used in traditional media, we show that the Internet poses some unique challenges. Specifically, online banner ads and other forms of online advertising are sold by methods that differ substantially from the way other media advertising is sold. We use a nonlinear optimization algorithm to solve the optimization problem and derive the optimum online media schedule. Data from an online audience measurement firm and an advertising agency are used to illustrate the speed and accuracy of our method, which is substantially quicker than using complete enumeration.

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File URL: http://dx.doi.org/10.1287/mksc.1090.0507
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Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 29 (2010)
Issue (Month): 2 (03-04)
Pages: 336-347

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Handle: RePEc:inm:ormksc:v:29:y:2010:i:2:p:336-347

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Keywords: advertising; Internet marketing; media; optimization; probability models;

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