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

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  • 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)

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    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.

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    File URL: http://dx.doi.org/10.1287/mnsc.1090.1054
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    Bibliographic Info

    Article provided by INFORMS in its journal Management Science.

    Volume (Year): 55 (2009)
    Issue (Month): 10 (October)
    Pages: 1605-1622

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    Handle: RePEc:inm:ormnsc:v:55:y:2009:i:10:p:1605-1622

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    Related research

    Keywords: online advertising; search engines; hierarchical Bayesian modeling; paid search; click-through rates; conversion rates; keyword ranking; cost per click; electronic commerce; Internet monetization;

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    Cited by:
    1. Andreas M. Hefti, 2011. "Attention competition," ECON - Working Papers 028, Department of Economics - University of Zurich.
    2. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer, vol. 44(2), pages 115-129, March.
    3. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2012. "What's in a Name? Measuring Prominence, and its Impact on Organic Traffic from Search Engines," Working Papers 2012-09, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    4. Berman, Ron & Katona, Zsolt, 2010. "The Role of Search Engine Optimization in Search Rankings," MPRA Paper 20129, University Library of Munich, Germany.
    5. 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.
    6. Novarese, Marco & Wilson, Chris M., 2013. "Being in the Right Place: A Natural Field Experiment on List Position and Consumer Choice," MPRA Paper 48074, University Library of Munich, Germany.
    7. Michael R. Baye & Babur De los Santos & Matthijs R. Wildenbeest, 2013. "Search Engine Optimization: What Drives Organic Traffic to Retail Sites?," Working Papers 2013-02, Indiana University, Kelley School of Business, Department of Business Economics and Public Policy.
    8. Hans Haans & NĂ©omie Raassens & Roel Hout, 2013. "Search engine advertisements: The impact of advertising statements on click-through and conversion rates," Marketing Letters, Springer, vol. 24(2), pages 151-163, June.
    9. Martin Spann & Oliver Hinz & Vandana Ramachandran, 2013. "Business and Information Systems Engineering and Marketing," Business & Information Systems Engineering, Springer, vol. 5(3), pages 127-128, June.
    10. Monic Sun & Feng Zhu, 2011. "Ad Revenue and Content Commercialization: Evidence from Blogs," Working Papers 11-32, NET Institute.
    11. White, Alexander, 2013. "Search engines: Left side quality versus right side profits," International Journal of Industrial Organization, Elsevier, vol. 31(6), pages 690-701.

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