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Optimal keyword bidding in search-based advertising with target exposure levels

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  • Selçuk, B.
  • Özlük, Ö.
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    Abstract

    Search-based advertising has become very popular since it provides advertisers the ability to attract potential customers with measurable returns. In this type of advertising, advertisers bid on keywords to have an impact on their ad’s placement, which in turn affects the response from potential customers. An advertiser must choose the right keywords and then bid correctly for each keyword in order to maximize the expected revenue or attain a certain level of exposure while keeping the daily costs in mind. In response to increasing need for analytical models that provide a guidance to advertisers, we construct and examine deterministic optimization models that minimize total expected advertising costs while satisfying a desired level of exposure. We investigate the relationship between our problem and the well-known continuous non-linear knapsack problem, and then solve the problem optimally by utilizing Karush–Kuhn–Tucker conditions. We present practical managerial insights based on the analysis of both a real-life data from a retailer and a hypothetical data.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0377221712007977
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    Bibliographic Info

    Article provided by Elsevier in its journal European Journal of Operational Research.

    Volume (Year): 226 (2013)
    Issue (Month): 1 ()
    Pages: 163-172

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    Handle: RePEc:eee:ejores:v:226:y:2013:i:1:p:163-172

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    Web page: http://www.elsevier.com/locate/eor

    Related research

    Keywords: Internet; Search advertising; Pricing; Non-linear optimization; Knapsack problem;

    References

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    1. Bretthauer, Kurt M. & Shetty, Bala, 2002. "The nonlinear knapsack problem - algorithms and applications," European Journal of Operational Research, Elsevier, vol. 138(3), pages 459-472, May.
    2. Kumar, Subodha & Sethi, Suresh P., 2009. "Dynamic pricing and advertising for web content providers," European Journal of Operational Research, Elsevier, vol. 197(3), pages 924-944, September.
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    5. Oliver J. Rutz & Michael Trusov & Randolph E. Bucklin, 2011. "Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?," Marketing Science, INFORMS, vol. 30(4), pages 646-665, July.
    6. John F. Stewart, 1979. "The Beta Distribution as a Model of Behavior in Consumer Goods Markets," Management Science, INFORMS, vol. 25(9), pages 813-821, September.
    7. Zhao, Lan & Nagurney, Anna, 2008. "A network equilibrium framework for Internet advertising: Models, qualitative analysis, and algorithms," European Journal of Operational Research, Elsevier, vol. 187(2), pages 456-472, June.
    8. Kumar, Subodha & Jacob, Varghese S. & Sriskandarajah, Chelliah, 2006. "Scheduling advertisements on a web page to maximize revenue," European Journal of Operational Research, Elsevier, vol. 173(3), pages 1067-1089, September.
    9. Zsolt Katona & Miklos Sarvary, 2010. "The Race for Sponsored Links: Bidding Patterns for Search Advertising," Marketing Science, INFORMS, vol. 29(2), pages 199-215, 03-04.
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