[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v55y2009i10p1605-1622.html
   My bibliography  Save this article

An Empirical Analysis of Search Engine Advertising: Sponsored Search in Electronic Markets

Author

Listed:
  • Anindya Ghose

    (Leonard N. Stern School of Business, New York University, New York, New York 10012)

  • Sha Yang

    (Leonard N. Stern School of Business, New York University, New York, New York 10012; and School of Economics and Management, Southwest Jiaotong University, 610031 Chenfgdu, China)

Abstract
The phenomenon of sponsored search advertising--where advertisers pay a fee to Internet search engines to be displayed alongside organic (nonsponsored) Web search results--is gaining ground as the largest source of revenues for search engines. Using a unique six-month panel data set of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different sponsored search metrics such as click-through rates, conversion rates, cost per click, and ranking of advertisements. Our paper proposes a novel framework to better understand the factors that drive differences in these metrics. We use a hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo methods. Using a simultaneous equations model, we quantify the relationship between various keyword characteristics, position of the advertisement, and the landing page quality score on consumer search and purchase behavior as well as on advertiser's cost per click and the search engine's ranking decision. Specifically, we find that the monetary value of a click is not uniform across all positions because conversion rates are highest at the top and decrease with rank as one goes down the search engine results page. Though search engines take into account the current period's bid as well as prior click-through rates before deciding the final rank of an advertisement in the current period, the current bid has a larger effect than prior click-through rates. We also find that an increase in landing page quality scores is associated with an increase in conversion rates and a decrease in advertiser's cost per click. Furthermore, our analysis shows that keywords that have more prominent positions on the search engine results page, and thus experience higher click-through or conversion rates, are not necessarily the most profitable ones--profits are often higher at the middle positions than at the top or the bottom ones. Besides providing managerial insights into search engine advertising, these results shed light on some key assumptions made in the theoretical modeling literature in sponsored search.

Suggested Citation

  • 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.
    • Handle: RePEc:inm:ormnsc:v:55:y:2009:i:10:p:1605-1622
    DOI: 10.1287/mnsc.1090.1054
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.1090.1054
    Download Restriction: no
    File URL: https://libkey.io/10.1287/mnsc.1090.1054?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Yongmin Chen & Chuan He, 2011. "Paid Placement: Advertising and Search on the Internet," Economic Journal, Royal Economic Society, vol. 121(556), pages 309-328, November.
    2. Kenneth C. Wilbur, 2008. "A Two-Sided, Empirical Model of Television Advertising and Viewing Markets," Marketing Science, INFORMS, vol. 27(3), pages 356-378, 05-06.
    3. 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.
    4. Juan Feng & Hemant K. Bhargava & David M. Pennock, 2007. "Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms," INFORMS Journal on Computing, INFORMS, vol. 19(1), pages 137-148, February.
    5. Ambarish Chandra, 2009. "Targeted Advertising: The Role Of Subscriber Characteristics In Media Markets," Journal of Industrial Economics, Wiley Blackwell, vol. 57(1), pages 58-84, March.
    6. Patrali Chatterjee & Donna L. Hoffman & Thomas P. Novak, 2003. "Modeling the Clickstream: Implications for Web-Based Advertising Efforts," Marketing Science, INFORMS, vol. 22(4), pages 520-541, May.
    7. Danaher, Peter J. & Mullarkey, Guy W., 2003. "Factors Affecting Online Advertising Recall: A Study of Students," Journal of Advertising Research, Cambridge University Press, vol. 43(3), pages 252-267, September.
    8. Avi Goldfarb & Catherine Tucker, 2007. "Search Engine Advertising: Pricing Ads to Context," Working Papers 07-23, NET Institute, revised Sep 2007.
    9. Tilman B?rgers & Ingemar Cox & Martin Pesendorfer & Vaclav Petricek, 2013. "Equilibrium Bids in Sponsored Search Auctions: Theory and Evidence," American Economic Journal: Microeconomics, American Economic Association, vol. 5(4), pages 163-187, November.
    10. 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.
    11. Michael R. Baye & John Morgan, 2001. "Information Gatekeepers on the Internet and the Competitiveness of Homogeneous Product Markets," American Economic Review, American Economic Association, vol. 91(3), pages 454-474, June.
    12. Hausman, Jerry A, 1975. "An Instrumental Variable Approach to Full Information Estimators for Linear and Certain Nonlinear Econometric Models," Econometrica, Econometric Society, vol. 43(4), pages 727-738, July.
    13. Michael R. Baye & J. Rupert J. Gatti & Paul Kattuman & John Morgan, 2009. "Clicks, Discontinuities, and Firm Demand Online," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 18(4), pages 935-975, December.
    14. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    15. Nikolay Archak & Anindya Ghose & Panagiotis G. Ipeirotis, 2007. "Deriving the Pricing Power of Product Features by Mining Consumer Reviews," Working Papers 07-36, NET Institute.
    16. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
    17. Lahiri, Kajal & Schmidt, Peter, 1978. "On the Estimation of Triangular Structural Systems," Econometrica, Econometric Society, vol. 46(5), pages 1217-1221, September.
    18. Brynjolfsson, Erik & Dick, Astrid Andrea & Smith, Michael D., 2004. "Search and Product Differentiation at an Internet Shopbot," Working papers 4441-03, Massachusetts Institute of Technology (MIT), Sloan School of Management.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    2. 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.
    3. Kenneth C. Wilbur & Yi Zhu, 2009. "Click Fraud," Marketing Science, INFORMS, vol. 28(2), pages 293-308, 03-04.
    4. Yi Zhu & Kenneth C. Wilbur, 2008. "Strategic Bidding in Hybrid CPC/CPM Auctions," Working Papers 08-25, NET Institute, revised Oct 2008.
    5. 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.
    6. Anindya Ghose & Panagiotis G. Ipeirotis & Beibei Li, 2014. "Examining the Impact of Ranking on Consumer Behavior and Search Engine Revenue," Management Science, INFORMS, vol. 60(7), pages 1632-1654, July.
    7. Mark Armstrong & Jidong Zhou, 2011. "Paying for Prominence," Economic Journal, Royal Economic Society, vol. 121(556), pages 368-395, November.
    8. Burguet, Roberto & Caminal, Ramon & Ellman, Matthew, 2015. "In Google we trust?," International Journal of Industrial Organization, Elsevier, vol. 39(C), pages 44-55.
    9. Avi Goldfarb & Catherine Tucker, 2007. "Search Engine Advertising: Pricing Ads to Context," Working Papers 07-23, NET Institute, revised Sep 2007.
    10. Ashish Agarwal & Kartik Hosanagar & Michael D. Smith, 2015. "Do Organic Results Help or Hurt Sponsored Search Performance?," Information Systems Research, INFORMS, vol. 26(4), pages 695-713, December.
    11. Yi Zhu & Kenneth C. Wilbur, 2011. "Hybrid Advertising Auctions," Marketing Science, INFORMS, vol. 30(2), pages 249-273, 03-04.
    12. White, Alexander, 2013. "Search engines: Left side quality versus right side profits," International Journal of Industrial Organization, Elsevier, vol. 31(6), pages 690-701.
    13. Thomas A. Weber & Zhiqiang (Eric) Zheng, 2007. "A Model of Search Intermediaries and Paid Referrals," Information Systems Research, INFORMS, vol. 18(4), pages 414-436, December.
    14. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.
    15. Dirk Bergemann & Alessandro Bonatti, 2010. "Targeting in Advertising Markets: Implications for Offline vs. Online Media," Cowles Foundation Discussion Papers 1758, Cowles Foundation for Research in Economics, Yale University.
    16. Jonathan Levin, 2011. "The Economics of Internet Markets," Discussion Papers 10-018, Stanford Institute for Economic Policy Research.
    17. 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.
    18. Patrick Hummel, 2018. "Hybrid mechanisms for Vickrey–Clarke–Groves and generalized second-price bids," International Journal of Game Theory, Springer;Game Theory Society, vol. 47(1), pages 331-350, March.
    19. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2016. "Search Engine Optimization: What Drives Organic Traffic to Retail Sites?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 25(1), pages 6-31, March.
    20. Susan Athey & Glenn Ellison, 2011. "Position Auctions with Consumer Search," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 126(3), pages 1213-1270.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:55:y:2009:i:10:p:1605-1622. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.