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An analysis of the profitability of fee-based compensation plans for search engine marketing

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  • Abou Nabout, Nadia
  • Skiera, Bernd
  • Stepanchuk, Tanja
  • Gerstmeier, Eva

Abstract

Many advertisers hire agencies to run their search engine marketing campaigns; increasingly, they use innovative performance-based compensation plans. In these plans, the advertiser pays the agency a fee for each conversion (i.e., acquired customer) but requires the agency to pay all of the search engine marketing costs. In this article, the authors address compensation decision problems for search engine marketing for the first time and conclude that such fee-based plans lower the advertiser's profit by as much as 26–30%. This article uses a simulation study and four empirical data sets to better understand what drives this profit loss. Two causes account for the loss: first, the agency spends less on advertising than is optimal for the advertiser. Second, the agency often earns more to manage the advertiser's campaign than its minimum requirement. This higher profit for the agency occurs because the advertiser pays the agency more in order to limit the agency's potential underspending on advertising. The authors show that this latter reason accounts for more than one-third of the advertiser's profit loss. This article also offers insights into how the advertiser's profit changes if the advertiser is uncertain about its profit per conversion or if the advertiser does not truthfully reveal its profit per conversion to the agency.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:ijrema:v:29:y:2012:i:1:p:68-80
    DOI: 10.1016/j.ijresmar.2011.07.002
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    Cited by:

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    3. Popa Adela Laura, 2015. "A Classic Framework Of Online Marketing Tools," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, vol. 1(1), pages 1269-1277, July.
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    6. 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.
    7. 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.
    8. Juan José López García & David Lizcano & Celia MQ Ramos & Nelson Matos, 2019. "Digital Marketing Actions That Achieve a Better Attraction and Loyalty of Users: An Analytical Study," Future Internet, MDPI, vol. 11(6), pages 1-16, June.
    9. José Ramón Saura & Pedro Palos-Sánchez & Luis Manuel Cerdá Suárez, 2017. "Understanding the Digital Marketing Environment with KPIs and Web Analytics," Future Internet, MDPI, vol. 9(4), pages 1-13, November.
    10. 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.
    11. Leeflang, Peter S.H. & Verhoef, Peter C. & Dahlström, Peter & Freundt, Tjark, 2014. "Challenges and solutions for marketing in a digital era," European Management Journal, Elsevier, vol. 32(1), pages 1-12.

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