Author Info

• Song Yao

()

(Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

• Carl F. Mela

()

(Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Registered author(s):

Abstract

Sponsored search advertising is ascendant--Forrester Research reports expenditures rose 28% in 2007 to $8.1 billion and will continue to rise at a 26% compound annual growth rate [VanBoskirk, S. 2007. U.S. interactive marketing forecast, 2007 to 2012. Forrester Research (October 10)], approaching half the level of television advertising and making sponsored search one of the major advertising trends to affect the marketing landscape. Yet little empirical research exists to explore how the interaction of various agents (searchers, advertisers, and the search engine) in keyword markets affects consumer welfare and firm profits. The dynamic structural model we propose serves as a foundation to explore these outcomes. We fit this model to a proprietary data set provided by an anonymous search engine. These data include consumer search and clicking behavior, advertiser bidding behavior, and search engine information such as keyword pricing and website design. With respect to advertisers, we find evidence of dynamic bidding behavior. Advertiser value for clicks on their links averages about 26 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 1.2%, well within common estimates of 1%-2% [Narcisse, E. 2007. Magid: Casual free to pay conversion rate too low. GameDaily.com (September 20)]. With respect to consumers, we find that frequent clickers place a greater emphasis on the position of the sponsored advertising link. We further find that about 10% of consumers do 90% of the clicks. We then conduct several policy simulations to illustrate the effects of changes in search engine policy. First, we find the search engine obtains revenue gains of 1% by sharing individual-level information with advertisers and enabling them to vary their bids by consumer segment. This also improves advertiser revenue by 6% and consumer welfare by 1.6%. Second, we find that a switch from a first- to second-price auction results in truth telling (advertiser bids rise to advertiser valuations). However, the second-price auction has little impact on search engine profits. Third, consumer search tools lead to a platform revenue increase of 2.9% and an increase of consumer welfare by 3.8%. However, these tools, by reducing advertising exposures, lower advertiser profits by 2.1%.

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File URL: http://dx.doi.org/10.1287/mksc.1100.0626

Bibliographic Info

Article provided by INFORMS in its journal Marketing Science.

Volume (Year): 30 (2011)
Issue (Month): 3 (05-06)
Pages: 447-468

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 Handle: RePEc:inm:ormksc:v:30:y:2011:i:3:p:447-468 Contact details of provider: Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USAPhone: +1-443-757-3500Fax: 443-757-3515Web page: http://www.informs.org/Email: More information through EDIRC

References

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1. V. Joseph Hotz & Robert A. Miller, 1993. "Conditional Choice Probabilities and the Estimation of Dynamic Models," Review of Economic Studies, Oxford University Press, vol. 60(3), pages 497-529.
2. Mireia Jofre-Bonet & Martin Pesendorfer, 2001. "Estimation of a Dynamic Auction Game," NBER Working Papers 8626, National Bureau of Economic Research, Inc.
3. Yongmin Chen & Chuan He, 2006. "Paid Placement: Advertising and Search on the Internet," Working Papers 06-02, NET Institute, revised Sep 2006.
4. Avi Goldfarb & Catherine Tucker, 2011. "Search Engine Advertising: Channel Substitution When Pricing Ads to Context," Management Science, INFORMS, vol. 57(3), pages 458-470, March.
5. Elaine Zanutto & Eric Bradlow, 2006. "Data pruning in consumer choice models," Quantitative Marketing and Economics, Springer, vol. 4(3), pages 267-287, September.
6. Benjamin Edelman & Michael Ostrovsky & Michael Schwarz, 2005. "Internet Advertising and the Generalized Second Price Auction: Selling Billions of Dollars Worth of Keywords," NBER Working Papers 11765, National Bureau of Economic Research, Inc.
7. Rochet, Jean-Charles & Tirole, Jean, 2005. "Two-Sided Markets : A Progress Report," IDEI Working Papers 275, Institut d'Économie Industrielle (IDEI), Toulouse.
8. Juan Escobar & Ulrich Doraszelski, 2008. "A Theory of Regular Markov Perfect Equilibria\\in Dynamic Stochastic Games: Genericity, Stability, and Purification," 2008 Meeting Papers 453, Society for Economic Dynamics.
9. Zsolt Katona & Miklos Sarvary, 2010. "The Race for Sponsored Links: Bidding Patterns for Search Advertising," Marketing Science, INFORMS, vol. 29(2), pages 199-215, 03-04.
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. Dan Horsky & Leonard S. Simon, 1983. "Advertising and the Diffusion of New Products," Marketing Science, INFORMS, vol. 2(1), pages 1-17.
12. Bresnahan, Timothy F & Reiss, Peter C, 1991. "Entry and Competition in Concentrated Markets," Journal of Political Economy, University of Chicago Press, vol. 99(5), pages 977-1009, October.
13. Juan Feng, 2008. "—Optimal Mechanism for Selling a Set of Commonly Ranked Objects," Marketing Science, INFORMS, vol. 27(3), pages 501-512, 05-06.
14. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, 06.
15. Jun B. Kim & Paulo Albuquerque & Bart J. Bronnenberg, 2010. "Online Demand Under Limited Consumer Search," Marketing Science, INFORMS, vol. 29(6), pages 1001-1023, 11-12.
16. Ali Hortaçsu & Chad Syverson, 2004. "Product Differentiation, Search Costs, and Competition in the Mutual Fund Industry: A Case Study of S&P 500 Index Funds," The Quarterly Journal of Economics, Oxford University Press, vol. 119(2), pages 403-456.
17. Martin Pesendorfer & Philipp Schmidt-Dengler, 2008. "Asymptotic Least Squares Estimators for Dynamic Games -super-1," Review of Economic Studies, Oxford University Press, vol. 75(3), pages 901-928.
18. Patrick Bajari & C. Lanier Benkard & Jonathan Levin, 2007. "Estimating Dynamic Models of Imperfect Competition," Econometrica, Econometric Society, vol. 75(5), pages 1331-1370, 09.
19. 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.
20. Daniel McFadden, 1977. "Modelling the Choice of Residential Location," Cowles Foundation Discussion Papers 477, Cowles Foundation for Research in Economics, Yale University.
21. Anindya Ghose & Sha Yang, 2009. "An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets," Management Science, INFORMS, vol. 55(10), pages 1605-1622, October.
22. Ulrich Doraszelski & Mark Satterthwaite, 2010. "Computable Markov-perfect industry dynamics," RAND Journal of Economics, RAND Corporation, vol. 41(2), pages 215-243.
23. Jean-Pierre H. Dubé & Günter J. Hitsch & Pradeep K. Chintagunta, 2010. "Tipping and Concentration in Markets with Indirect Network Effects," Marketing Science, INFORMS, vol. 29(2), pages 216-249, 03-04.
24. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, 03.
25. Jean-Pierre Dubé & K. Sudhir & Andrew Ching & Gregory Crawford & Michaela Draganska & Jeremy Fox & Wesley Hartmann & Günter Hitsch & V. Viard & Miguel Villas-Boas & Naufel Vilcassim, 2005. "Recent Advances in Structural Econometric Modeling: Dynamics, Product Positioning and Entry," Marketing Letters, Springer, vol. 16(3), pages 209-224, December.
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