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Optimal Keyword Bids in Search-Based Advertising with Stochastic Advertisement Positions

Author

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  • Susan Cholette

    (San Francisco State University)

  • Özgür Özlük

    (San Francisco State University)

  • Mahmut Parlar

    (McMaster University)

Abstract

US expenditures on search-based advertising exceeded $12 billion in 2010. Advertisers bid for keywords, where bid price determines ad placement, affecting click-through and conversion rates. Advertisers must select keywords, allocating each a proportion of their fixed daily budget. In this paper, we construct a stochastic model for the selection and allocation process. We provide analytical results for the single-keyword problem and examine the multiple-keyword problem numerically. We investigate trade-offs between keywords given varying levels of risk and return. We show the implications of enforcing a probabilistic budget constraint. Our paper provides a critical analysis of the advertiser’s problem that may guide future research.

Suggested Citation

  • Susan Cholette & Özgür Özlük & Mahmut Parlar, 2012. "Optimal Keyword Bids in Search-Based Advertising with Stochastic Advertisement Positions," Journal of Optimization Theory and Applications, Springer, vol. 152(1), pages 225-244, January.
    • Handle: RePEc:spr:joptap:v:152:y:2012:i:1:d:10.1007_s10957-011-9886-3
    DOI: 10.1007/s10957-011-9886-3
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    References listed on IDEAS

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    Cited by:

    1. Savas Dayanik & Semih O. Sezer, 2023. "Optimal dynamic multi-keyword bidding policy of an advertiser in search-based advertising," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 97(1), pages 25-56, February.

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