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Optimal Internet Media Selection


  • 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)


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.

Suggested Citation

  • Peter J. Danaher & Janghyuk Lee & Laoucine Kerbache, 2010. "Optimal Internet Media Selection," Marketing Science, INFORMS, vol. 29(2), pages 336-347, 03-04.
  • Handle: RePEc:inm:ormksc:v:29:y:2010:i:2:p:336-347

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    References listed on IDEAS

    1. Rust, Roland T., 1985. "Selecting network television advertising schedules," Journal of Business Research, Elsevier, vol. 13(6), pages 483-494, December.
    2. Young-Hoon Park & Peter S. Fader, 2004. "Modeling Browsing Behavior at Multiple Websites," Marketing Science, INFORMS, vol. 23(3), pages 280-303, May.
    3. Danaher, Peter J. & Hardie, Bruce G.S., 2005. "Bacon With Your Eggs? Applications of a New Bivariate Beta-Binomial Distribution," The American Statistician, American Statistical Association, vol. 59, pages 282-286, November.
    4. Peter J. Danaher, 2007. "Modeling Page Views Across Multiple Websites with an Application to Internet Reach and Frequency Prediction," Marketing Science, INFORMS, vol. 26(3), pages 422-437, 05-06.
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    Cited by:

    1. Rik Pieters & Michel Wedel, 2012. "Ad Gist: Ad Communication in a Single Eye Fixation," Marketing Science, INFORMS, vol. 31(1), pages 59-73, January.
    2. Parham Fami Tafreshi & Mohammad Hasan Aghdaie & Majid Behzadian & Mahdieh Ghani Abadi, 2016. "Developing a Group Decision Support System for Advertising Media Evaluation: A Case in the Middle East," Group Decision and Negotiation, Springer, vol. 25(5), pages 1021-1048, September.
    3. Liberali, G. & Urban, G.L. & Dellaert, B.G.C. & Tucker, C. & Bart, Y. & Stremersch, S., 2016. "A New Method of Measuring Online Media Advertising Effectiveness: Prospective Meta-Analysis in Marketing," ERIM Report Series Research in Management ERS-2016-007-MKT, Erasmus Research Institute of Management (ERIM), ERIM is the joint research institute of the Rotterdam School of Management, Erasmus University and the Erasmus School of Economics (ESE) at Erasmus University Rotterdam.


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