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Practice Prize Paper ---PROSAD: A Bidding Decision Support System for Profit Optimizing Search Engine Advertising

Author

Listed:
  • Bernd Skiera

    (Faculty of Business and Economics, Department of Marketing, Goethe University Frankfurt, 60629 Frankfurt am Main, Germany)

  • Nadia Abou Nabout

    (Faculty of Business and Economics, Department of Marketing, Goethe University Frankfurt, 60629 Frankfurt am Main, Germany)

Abstract

This paper reports on a large-scale implementation of marketing science models to solve the bidding problem in search engine advertising. In cooperation with the online marketing agency SoQuero, we developed a fully automated bidding decision support system, PROSAD (PRofit Optimizing Search engine ADvertising; see http://www.prosad.de), and implemented it through the agency's bid management software. The PROSAD system maximizes an advertiser's profit per keyword without the need for human intervention. A closed-form solution for the optimized bid and a newly developed “costs-per-profit” heuristic enable advertisers to submit good bids even when there is significant noise in the data. A field experiment demonstrates that PROSAD can increase the return on investment by 21 percentage points and improve the yearly profit potential for SoQuero and its clients by €2.7 million.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormksc:v:32:y:2013:i:2:p:213-220
    DOI: 10.1287/mksc.1120.0735
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    References listed on IDEAS

    as
    1. Abou Nabout, Nadia & Skiera, Bernd & Stepanchuk, Tanja & Gerstmeier, Eva, 2012. "An analysis of the profitability of fee-based compensation plans for search engine marketing," International Journal of Research in Marketing, Elsevier, vol. 29(1), pages 68-80.
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    Citations

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    Cited by:

    1. Klapdor, Sebastian & Anderl, Eva M. & von Wangenheim, Florian & Schumann, Jan H., 2014. "Finding the Right Words: The Influence of Keyword Characteristics on Performance of Paid Search Campaigns," Journal of Interactive Marketing, Elsevier, vol. 28(4), pages 285-301.
    2. Abou Nabout, Nadia & Lilienthal, Markus & Skiera, Bernd, 2014. "Empirical Generalizations in Search Engine Advertising," Journal of Retailing, Elsevier, vol. 90(2), pages 206-216.
    3. Carsten D. Schultz, 2020. "The impact of ad positioning in search engine advertising: a multifaceted decision problem," Electronic Commerce Research, Springer, vol. 20(4), pages 945-968, December.
    4. Kannan, P.K. & Li, Hongshuang “Alice”, 2017. "Digital marketing: A framework, review and research agenda," International Journal of Research in Marketing, Elsevier, vol. 34(1), pages 22-45.
    5. Simone Guercini, 2022. "Scope of heuristics and digitalization: the case of marketing automation," Mind & Society: Cognitive Studies in Economics and Social Sciences, Springer;Fondazione Rosselli, vol. 21(2), pages 151-164, November.
    6. Lukas Jurgensmeier & Bernd Skiera, 2023. "Measuring Self-Preferencing on Digital Platforms," Papers 2303.14947, arXiv.org, revised Feb 2024.
    7. Anoek Castelein & Dennis Fok & Richard Paap, 2019. "Dynamics in clickthrough and conversion probabilities of paid search advertisements," Tinbergen Institute Discussion Papers 19-056/III, Tinbergen Institute.
    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. de Haan, Evert & Wiesel, Thorsten & Pauwels, Koen, 2016. "The effectiveness of different forms of online advertising for purchase conversion in a multiple-channel attribution framework," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 491-507.
    10. Nagpal, Mayank & Petersen, J. Andrew, 2021. "Keyword Selection Strategies in Search Engine Optimization: How Relevant is Relevance?," Journal of Retailing, Elsevier, vol. 97(4), pages 746-763.
    11. Duncan Simester & Artem Timoshenko & Spyros I. Zoumpoulis, 2020. "Efficiently Evaluating Targeting Policies: Improving on Champion vs. Challenger Experiments," Management Science, INFORMS, vol. 66(8), pages 3412-3424, August.
    12. Bayer, Emanuel & Srinivasan, Shuba & Riedl, Edward J. & Skiera, Bernd, 2020. "The impact of online display advertising and paid search advertising relative to offline advertising on firm performance and firm value," International Journal of Research in Marketing, Elsevier, vol. 37(4), pages 789-804.
    13. 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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