An Empirical Analysis of Search Engine Advertising: Sponsored Search and Cross-Selling in Electronic Markets
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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- 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.
- 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.
- Peter E. Rossi & Greg M. Allenby, 2003. "Bayesian Statistics and Marketing," Marketing Science, INFORMS, vol. 22(3), pages 304-328, July.
- 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.
- Gene M. Grossman & Carl Shapiro, 1984. "Informative Advertising with Differentiated Products," Review of Economic Studies, Oxford University Press, vol. 51(1), pages 63-81.
- Avi Goldfarb & Catherine Tucker, 2007. "Search Engine Advertising: Pricing Ads to Context," Working Papers 07-23, NET Institute, revised Sep 2007.
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