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A Dynamic Model of Sponsored Search Advertising

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Abstract

Sponsored search advertising is ascendant---Jupiter Research reports expenditures rose 28% in 2007 to $8.9B and will continue to rise at a 15% CAGR, making it one of the major trends to affect the marketing landscape. Yet little, if any empirical research focuses upon search engine marketing strategy by integrating the behavior of various agents in sponsored search advertising (i.e., searchers, advertisers, and the search engine platform). The dynamic structural model we propose serves as a foundation to explore these and other sponsored search advertising phenomena. Fitting the model to a proprietary data set provided by an anonymous search engine, we conduct several policy simulations to illustrate the benefits of our approach. First, we explore how information asymmetries between search engines and advertisers can be exploited to enhance platform revenues. This has consequences for the pricing of market intelligence. Second, we assess the effect of allowing advertisers to bid not only on key words, but also by consumers searching histories and demographics thereby creating a more targeted model of advertising. Third, we explore several different auction pricing mechanisms and assess the role of each on engine and advertiser profits and revenues. Finally, we consider the role of consumer search tools such as sorting on consumer and advertiser behavior and engine revenues. One key finding is that the estimated advertiser value for a click on its sponsored link averages about 24 cents. Given the typical $22 retail price of the software products advertised on the considered search engine, this implies a conversion rate (sales per click) of about 1.1%, well within common estimates of 1-2% (gamedaily.com). Hence our approach appears to yield valid estimates of advertiser click valuations. Another finding is that customers appear to be segmented by their clicking frequency, with frequent clickers placing a greater emphasis on the position of the sponsored advertising link. Estimation of the policy simulations is in progress.

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Bibliographic Info

Paper provided by NET Institute in its series Working Papers with number 08-16.

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Length: 61 pages
Date of creation: Sep 2008
Date of revision: Sep 2008
Handle: RePEc:net:wpaper:0816

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Web page: http://www.NETinst.org/

Related research

Keywords: Sponsored Search Advertising; Two-sided Market; Dynamic Game; Structural Models; Empirical IO; Customization; Auctions;

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  1. Patrick Bajari & Han Hong, 2006. "Semiparametric Estimation of a Dynamic Game of Incomplete Information," NBER Technical Working Papers 0320, National Bureau of Economic Research, Inc.
  2. Mireia Jofre-Bonet & Martin Pesendorfer, 2001. "Estimation of a Dynamic Auction Game," NBER Working Papers 8626, National Bureau of Economic Research, Inc.
  3. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2004. "Estimating Dynamic Models of Imperfect Competition," NBER Working Papers 10450, National Bureau of Economic Research, Inc.
  4. Eric T. Bradlow & David C. Schmittlein, 2000. "The Little Engines That Could: Modeling the Performance of World Wide Web Search Engines," Marketing Science, INFORMS, vol. 19(1), pages 43-62, June.
  5. Diehl, Kristin & Kornish, Laura J & Lynch, John G, Jr, 2003. " Smart Agents: When Lower Search Costs for Quality Information Increase Price Sensitivity," Journal of Consumer Research, University of Chicago Press, vol. 30(1), pages 56-71, June.
  6. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2007. "Internet Advertising and the Generalized Second-Price Auction: Selling Billions of Dollars Worth of Keywords," American Economic Review, American Economic Association, vol. 97(1), pages 242-259, March.
  7. Hotz, V.J. & Miller, R.A., 1991. "Conditional Choice Probabilities and the Estimation of Dynamic Models," GSIA Working Papers 1992-12, Carnegie Mellon University, Tepper School of Business.
  8. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, 03.
  9. Kenneth Train, 2003. "Discrete Choice Methods with Simulation," Online economics textbooks, SUNY-Oswego, Department of Economics, number emetr2, Spring.
  10. Anindya Ghose & Sha Yang, 2007. "An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets," Working Papers 07-35, NET Institute, revised Sep 2007.
  11. repec:rje:randje:v:37:y:2006:3:p:645-667 is not listed on IDEAS
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Cited by:
  1. Argenton, C. & Prüfer, J., 2011. "Search Engine Competition with Network Externalities," Discussion Paper 2011-024, Tilburg University, Tilburg Law and Economic Center.
  2. Tom Blake & Chris Nosko & Steven Tadelis, 2014. "Consumer Heterogeneity and Paid Search Effectiveness: A Large Scale Field Experiment," NBER Working Papers 20171, National Bureau of Economic Research, Inc.

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  1. Online Marketing and Advertising

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