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A Dynamic Model of Sponsored Search Advertising

Author

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  • Song Yao

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

  • Carl F. Mela

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

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%.

Suggested Citation

  • Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
  • Handle: RePEc:inm:ormksc:v:30:y:2011:i:3:p:447-468
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    File URL: http://dx.doi.org/10.1287/mksc.1100.0626
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    Cited by:

    1. repec:eee:joinma:v:26:y:2012:i:3:p:141-154 is not listed on IDEAS
    2. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    3. Kinshuk Jerath & Liye Ma & Young-Hoon Park & Kannan Srinivasan, 2011. "A "Position Paradox" in Sponsored Search Auctions," Marketing Science, INFORMS, vol. 30(4), pages 612-627, July.
    4. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    5. Tarantino, Emanuele, 2013. "A simple model of vertical search engines foreclosure," Telecommunications Policy, Elsevier, vol. 37(1), pages 1-12.
    6. repec:eee:joinma:v:28:y:2014:i:4:p:285-301 is not listed on IDEAS
    7. repec:eee:jouret:v:90:y:2014:i:2:p:206-216 is not listed on IDEAS
    8. Baye, Michael R. & De los Santos, Babur & Wildenbeest, Matthijs R., 2016. "What’s in a name? Measuring prominence and its impact on organic traffic from search engines," Information Economics and Policy, Elsevier, vol. 34(C), pages 44-57.
    9. Alex Kim & Subramanian Balachander & Karthik Kannan, 2012. "On the optimal number of advertising slots in a generalized second-price auction," Marketing Letters, Springer, vol. 23(3), pages 851-868, September.
    10. repec:eee:joreco:v:19:y:2012:i:1:p:78-87 is not listed on IDEAS
    11. repec:eee:ijrema:v:29:y:2012:i:1:p:68-80 is not listed on IDEAS
    12. repec:oup:jcomle:v:8:y:2012:i:1:p:73-105. is not listed on IDEAS
    13. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    14. repec:eee:joinma:v:30:y:2015:i:c:p:46-55 is not listed on IDEAS
    15. Oliver J. Rutz & Michael Trusov & Randolph E. Bucklin, 2011. "Modeling Indirect Effects of Paid Search Advertising: Which Keywords Lead to More Future Visits?," Marketing Science, INFORMS, vol. 30(4), pages 646-665, July.
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    18. repec:eee:ijrema:v:34:y:2017:i:1:p:22-45 is not listed on IDEAS
    19. Weijia (Daisy) Dai & Michael Luca, 2016. "Effectiveness of Paid Search Advertising: Experimental Evidence," Harvard Business School Working Papers 17-025, Harvard Business School.
    20. Yu (Jeffrey) Hu & Jiwoong Shin & Zhulei Tang, 2016. "Incentive Problems in Performance-Based Online Advertising Pricing: Cost per Click vs. Cost per Action," Management Science, INFORMS, vol. 62(7), pages 2022-2038, July.
    21. Carl F. Mela, 2011. "Structural Workshop Paper --Data Selection and Procurement," Marketing Science, INFORMS, vol. 30(6), pages 965-976, November.
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