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Optimal dynamic multi-keyword bidding policy of an advertiser in search-based advertising

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  • Savas Dayanik

    (Bilkent University)

  • Semih O. Sezer

    (Sabancı University)

Abstract

Sponsored search advertisement allows advertisers to target their messages to appropriate customer segments at low costs. While search engines are interested in auction mechanisms that boost their revenues, advertisers seek optimal bidding strategies to increase their net sale revenues for multiple keywords under strict daily budget constraints in an environment where keyword query arrivals, competitor bid amounts, and user purchases are random. We focus on the advertiser’s question and formulate her optimal intraday dynamic multi-keyword bidding problem as a continuous-time stochastic optimization problem. We solve the problem, characterize an optimal policy, and bring a numerical algorithm for implementation. We also illustrate our optimal bidding policy and its benefits over heuristic solutions on numerical examples.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:mathme:v:97:y:2023:i:1:d:10.1007_s00186-022-00803-y
    DOI: 10.1007/s00186-022-00803-y
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    References listed on IDEAS

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    1. Santiago R. Balseiro & Omar Besbes & Gabriel Y. Weintraub, 2015. "Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design," Management Science, INFORMS, vol. 61(4), pages 864-884, April.
    2. Boulatov, Alexei & Severinov, Sergei, 2021. "Optimal and efficient mechanisms with asymmetrically budget constrained buyers," Games and Economic Behavior, Elsevier, vol. 127(C), pages 155-178.
    3. Kotowski, Maciej H., 2020. "First-price auctions with budget constraints," Theoretical Economics, Econometric Society, vol. 15(1), January.
    4. Krishnamurthy Iyer & Ramesh Johari & Mukund Sundararajan, 2014. "Mean Field Equilibria of Dynamic Auctions with Learning," Management Science, INFORMS, vol. 60(12), pages 2949-2970, December.
    5. Vibhanshu Abhishek & Kartik Hosanagar, 2013. "Optimal Bidding in Multi-Item Multislot Sponsored Search Auctions," Operations Research, INFORMS, vol. 61(4), pages 855-873, August.
    6. 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.
    7. Patrick Jaillet & Xin Lu, 2014. "Online Stochastic Matching: New Algorithms with Better Bounds," Mathematics of Operations Research, INFORMS, vol. 39(3), pages 624-646, August.
    8. Bernd Skiera & Nadia Abou Nabout, 2013. "Practice Prize Paper ---PROSAD: A Bidding Decision Support System for Profit Optimizing Search Engine Advertising," Marketing Science, INFORMS, vol. 32(2), pages 213-220, March.
    9. Patrick Hummel, 2018. "Hybrid mechanisms for Vickrey–Clarke–Groves and generalized second-price bids," International Journal of Game Theory, Springer;Game Theory Society, vol. 47(1), pages 331-350, March.
    10. Bae, Jinsoo & Kagel, John H., 2019. "An experimental study of the generalized second price auction," International Journal of Industrial Organization, Elsevier, vol. 63(C), pages 44-68.
    11. Savas Dayanik & Mahmut Parlar, 2013. "Dynamic bidding strategies in search-based advertising," Annals of Operations Research, Springer, vol. 211(1), pages 103-136, December.
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    1. Li, Baoku & Nan, Yafeng & Yao, Ruoxi, 2024. "The inverted U-shaped relationship between information entropy of keyword combinations and sales of digital products: Evidence from China Tmall," Journal of Retailing and Consumer Services, Elsevier, vol. 79(C).

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