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An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets

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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 (non-sponsored) web search results is gaining ground as the largest source of revenues for search engines. Using a unique panel dataset of several hundred keywords collected from a large nationwide retailer that advertises on Google, we empirically model the relationship between different metrics such as click-through rates, conversion rates, bid prices and keyword ranks. Our paper proposes a novel framework and data to better understand what drives these differences. We use a Hierarchical Bayesian modeling framework and estimate the model using Markov Chain Monte Carlo (MCMC) methods. We empirically estimate the impact of keyword attributes on consumer search and purchase behavior as well as on firms’ decision-making behavior on bid prices and ranks. We find that the presence of retailer-specific information in the keyword increases click-through rates, and the presence of brand-specific information in the keyword increases conversion rates. Our analysis provides some evidence that advertisers are not bidding optimally with respect to maximizing the profits. We also demonstrate that as suggested by anecdotal evidence, search engines like Google factor in both the auction bid price as well as prior click-through rates before allotting a final rank to an advertisement. Finally, we conduct a detailed analysis with product level variables to explore the extent of cross-selling opportunities across different categories from a given keyword advertisement. We find that there exists significant potential for cross-selling through search keyword advertisements. Latency (the time it takes for consumer to place a purchase order after clicking on the advertisement) and the presence of a brand name in the keyword are associated with consumer spending on product categories that are different from the one they were originally searching for on the Internet.

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Bibliographic Info

Paper provided by NET Institute in its series Working Papers with number 07-35.

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Length: 39 pages
Date of creation: Sep 2007
Date of revision: Sep 2007
Handle: RePEc:net:wpaper:0735

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

Related research

Keywords: Online advertising; Search engines; Hierarchical Bayesian modeling; Paid search; Clickthrough rates; Conversion rates; Keyword ranking; Bid price; Electronic commerce; Cross-Selling; Internet economics.;

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References

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  1. 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.
  2. Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
  3. 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(03), pages 252-267, September.
  4. 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.
  5. Grossman, Gene M & Shapiro, Carl, 1984. "Informative Advertising with Differentiated Products," Review of Economic Studies, Wiley Blackwell, vol. 51(1), pages 63-81, January.
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Citations

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Cited by:
  1. Pollock, Rufus, 2008. "Is Google the next Microsoft? Competition, Welfare and Regulation in Internet Search," MPRA Paper 8885, University Library of Munich, Germany.
  2. 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.
  3. 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.
  4. 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.
  5. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer, vol. 44(2), pages 115-129, March.
  6. 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.
  7. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.
  8. Monic Sun & Feng Zhu, 2011. "Ad Revenue and Content Commercialization: Evidence from Blogs," Working Papers 11-32, NET Institute.
  9. Andreas M. Hefti, 2011. "Attention competition," ECON - Working Papers 028, Department of Economics - University of Zurich.
  10. Berman, Ron & Katona, Zsolt, 2010. "The Role of Search Engine Optimization in Search Rankings," MPRA Paper 20129, University Library of Munich, Germany.
  11. Hans Haans & Néomie 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.
  12. Avi Goldfarb & Catherine Tucker, 2007. "Search Engine Advertising: Pricing Ads to Context," Working Papers 07-23, NET Institute, revised Sep 2007.
  13. 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.

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