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What Makes them Click: Empirical Analysis of Consumer Demand for Search Advertising

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  • Przemyslaw Jeziorski
  • Ilya Segal

Abstract

We study users' response to sponsored-search advertising using data from Microsoft's Live AdCenter distributed in the "Beyond Search" initiative. We estimate a structural model of utility maximizing users, which quantifies "user experience" based on their "revealed preferences," and predicts user responses to counterfactual ad placements. In the model, each user chooses clicks sequentially to maximize his expected utility under incomplete information about the relevance of ads. We estimate the substitutability of ads in users' utility function, the fixed effects of different ads and positions, user uncertainty about ads' relevance, and user heterogeneity. We find substantial substitutability of ads, which generates large negative externalities: 40% more clicks would occur in a hypothetical world in which each ad faces no competition. As for counterfactual ad placements, our simulations indicate that CTR-optimal matching increases CTR by 10.1% while user-optimal matching increases user welfare by 13.3%. Moreover, targeting ad placement to specific users could raise user welfare by 59%. Here, we find a significant suboptimality (up to 16% of total welfare) in case the search engine tries to implement a sophisticated matching policy using a misspecified model that does not account for externalities. Finally, user welfare could be raised by 14% if they had full information about the relevance of ads to them.

Suggested Citation

  • Przemyslaw Jeziorski & Ilya Segal, 2010. "What Makes them Click: Empirical Analysis of Consumer Demand for Search Advertising," Economics Working Paper Archive 569, The Johns Hopkins University,Department of Economics.
  • Handle: RePEc:jhu:papers:569
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    Cited by:

    1. Emanuele Tarantino, 2012. "A Note on Vertical Search Engines’ Foreclosure," Chapters, in: Joseph E. Harrington Jr & Yannis Katsoulacos (ed.), Recent Advances in the Analysis of Competition Policy and Regulation, chapter 8, Edward Elgar Publishing.
    2. Tarantino, Emanuele, 2013. "A simple model of vertical search engines foreclosure," Telecommunications Policy, Elsevier, vol. 37(1), pages 1-12.
    3. Jonathan Levin, 2011. "The Economics of Internet Markets," Discussion Papers 10-018, Stanford Institute for Economic Policy Research.
    4. Fernando Branco & Monic Sun & J. Miguel Villas-Boas, 2012. "Optimal Search for Product Information," Management Science, INFORMS, vol. 58(11), pages 2037-2056, November.
    5. Berman, Ron & Katona, Zsolt, 2010. "The Role of Search Engine Optimization in Search Rankings," MPRA Paper 20129, University Library of Munich, Germany.
    6. , & ,, 2013. "Choice by iterative search," Theoretical Economics, Econometric Society, vol. 8(3), September.
    7. Jia Liu & Olivier Toubia, 2018. "A Semantic Approach for Estimating Consumer Content Preferences from Online Search Queries," Marketing Science, INFORMS, vol. 37(6), pages 930-952, November.

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