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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' responses to sponsored-search advertising using consumer-level data from Microsoft Live. We document that users click ads in a nonsequential order and that the click through rates depend on the identity of competing ads. We estimate a dynamic model of utility-maximizing users that rationalizes these two facts and find that 51 percent more clicks would occur if ads faced no competition. We demonstrate that optimal matching of advertisements to positions raises welfare by 27 percent, and that individual-level targeting raises welfare by 69 percent. Revealing the quality of the advertiser prior to clicking on a sponsored link raises welfare by 1.6 percent. (JEL D12, L86, M37)

Suggested Citation

  • Przemyslaw Jeziorski & Ilya Segal, 2015. "What Makes Them Click: Empirical Analysis of Consumer Demand for Search Advertising," American Economic Journal: Microeconomics, American Economic Association, vol. 7(3), pages 24-53, August.
    • Handle: RePEc:aea:aejmic:v:7:y:2015:i:3:p:24-53
    Note: DOI: 10.1257/mic.20100119
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    References listed on IDEAS

    as
    1. McFadden, Daniel, 1989. "A Method of Simulated Moments for Estimation of Discrete Response Models without Numerical Integration," Econometrica, Econometric Society, vol. 57(5), pages 995-1026, September.
    2. 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.
    3. Han Hong & Matthew Shum, 2006. "Using price distributions to estimate search costs," RAND Journal of Economics, RAND Corporation, vol. 37(2), pages 257-275, June.
    4. Benjamin Edelman & Michael Schwarz, 2010. "Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions," American Economic Review, American Economic Association, vol. 100(2), pages 597-602, May.
    5. Zsolt Katona & Miklos Sarvary, 2010. "The Race for Sponsored Links: Bidding Patterns for Search Advertising," Marketing Science, INFORMS, vol. 29(2), pages 199-215, 03-04.
    6. Benjamin Edelman & Michael Schwarz, 2010. "Optimal Auction Design and Equilibrium Selection in Sponsored Search Auctions," Harvard Business School Working Papers 10-054, Harvard Business School.
    7. Ali Hortaçsu & Chad Syverson, 2004. "Product Differentiation, Search Costs, and Competition in the Mutual Fund Industry: A Case Study of S&P 500 Index Funds," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 119(2), pages 403-456.
    8. 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.
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    More about this item

    JEL classification:

    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • L86 - Industrial Organization - - Industry Studies: Services - - - Information and Internet Services; Computer Software
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

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    1. What Makes Them Click: Empirical Analysis of Consumer Demand for Search Advertising (AEJ:MI 2015) in ReplicationWiki

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