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Optimal Bidding in Multi-Item Multislot Sponsored Search Auctions

Author

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  • Vibhanshu Abhishek

    (H. John Heinz III College of Public Policy and Management, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

  • Kartik Hosanagar

    (The Wharton School, University of Pennsylvania, Philadelphia, Pennsylvania 19104)

Abstract

We study optimal bidding strategies for advertisers in sponsored search auctions. In general, these auctions are run as variants of second-price auctions but have been shown to be incentive incompatible. Thus, advertisers have to be strategic about bidding. Uncertainty in the decision-making environment, budget constraints, and the presence of a large portfolio of keywords makes the bid optimization problem nontrivial. We present an analytical model to compute the optimal bids for keywords in an advertiser's portfolio. To validate our approach, we estimate the parameters of the model using data from an advertiser's sponsored search campaign and use the bids proposed by the model in a field experiment. The results of the field implementation show that the proposed bidding technique is very effective in practice. We extend our model to account for interactions between keywords, in the form of positive spillovers from generic keywords into branded keywords. The spillovers are estimated using a dynamic linear model framework and are used to jointly optimize the bids of the keywords using an approximate dynamic programming approach. Accounting for the interaction between keywords leads to an additional improvement in the campaign performance.

Suggested Citation

  • Vibhanshu Abhishek & Kartik Hosanagar, 2013. "Optimal Bidding in Multi-Item Multislot Sponsored Search Auctions," Operations Research, INFORMS, vol. 61(4), pages 855-873, August.
    • Handle: RePEc:inm:oropre:v:61:y:2013:i:4:p:855-873
    DOI: 10.1287/opre.2013.1187
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    References listed on IDEAS

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    Cited by:

    1. Shijie Lu & Yi Zhu & Anthony Dukes, 2015. "Position Auctions with Budget Constraints: Implications for Advertisers and Publishers," Marketing Science, INFORMS, vol. 34(6), pages 897-905, November.
    2. Ashish Agarwal & Kartik Hosanagar & Michael D. Smith, 2015. "Do Organic Results Help or Hurt Sponsored Search Performance?," Information Systems Research, INFORMS, vol. 26(4), pages 695-713, December.
    3. Sameer Mehta & Milind Dawande & Ganesh Janakiraman & Vijay Mookerjee, 2020. "Sustaining a Good Impression: Mechanisms for Selling Partitioned Impressions at Ad Exchanges," Information Systems Research, INFORMS, vol. 31(1), pages 126-147, March.
    4. Tunuguntla, Vaishnavi & Basu, Preetam & Rakshit, Krishanu & Ghosh, Debabrata, 2019. "Sponsored search advertising and dynamic pricing for perishable products under inventory-linked customer willingness to pay," European Journal of Operational Research, Elsevier, vol. 276(1), pages 119-132.
    5. Ranjit M. Christopher & Sungho Park & Sang Pil Han & Min-Kyu Kim, 2022. "Bypassing Performance Optimizers of Real Time Bidding Systems in Display Ad Valuation," Information Systems Research, INFORMS, vol. 33(2), pages 399-412, June.
    6. Chen Jin & Luyi Yang & Kartik Hosanagar, 2023. "To Brush or Not to Brush: Product Rankings, Consumer Search, and Fake Orders," Information Systems Research, INFORMS, vol. 34(2), pages 532-552, June.
    7. Santiago Balseiro & Christian Kroer & Rachitesh Kumar, 2021. "Contextual Standard Auctions with Budgets: Revenue Equivalence and Efficiency Guarantees," Papers 2102.10476, arXiv.org, revised Oct 2022.
    8. 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.
    9. Tunuguntla, Vaishnavi & Rakshit, Krishanu & Basu, Preetam, 2023. "Bidding for an optimal portfolio of keywords in sponsored search advertising: From generic to branded keywords," European Journal of Operational Research, Elsevier, vol. 307(3), pages 1424-1440.
    10. Vibhanshu Abhishek & Kartik Hosanagar & Peter S. Fader, 2015. "Aggregation Bias in Sponsored Search Data: The Curse and the Cure," Marketing Science, INFORMS, vol. 34(1), pages 59-77, January.
    11. Gérard P. Cachon, 2020. "A Research Framework for Business Models: What Is Common Among Fast Fashion, E-Tailing, and Ride Sharing?," Management Science, INFORMS, vol. 66(3), pages 1172-1192, March.
    12. Lijia Ma & Xingchen Xu & Yong Tan, 2024. "Crafting Knowledge: Exploring the Creative Mechanisms of Chat-Based Search Engines," Papers 2402.19421, arXiv.org.
    13. Zhaohua Chen & Chang Wang & Qian Wang & Yuqi Pan & Zhuming Shi & Zheng Cai & Yukun Ren & Zhihua Zhu & Xiaotie Deng, 2022. "Dynamic Budget Throttling in Repeated Second-Price Auctions," Papers 2207.04690, arXiv.org, revised Dec 2023.
    14. Chen Jin & Luyi Yang & Kartik Hosanagar, 2019. "To Brush or Not to Brush: Product Rankings, Customer Search, and Fake Orders," Working Papers 19-02, NET Institute.

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