IDEAS home Printed from https://ideas.repec.org/a/wly/emetrp/v83y2015ip155-174.html
   My bibliography  Save this article

Consumer Heterogeneity and Paid Search Effectiveness: A Large‐Scale Field Experiment

Author

Listed:
  • Thomas Blake
  • Chris Nosko
  • Steven Tadelis

Abstract

Internet advertising has been the fastest growing advertising channel in recent years, with paid search ads comprising the bulk of this revenue. We present results from a series of large‐scale field experiments done at eBay that were designed to measure the causal effectiveness of paid search ads. Because search clicks and purchase intent are correlated, we show that returns from paid search are a fraction of non‐experimental estimates. As an extreme case, we show that brand keyword ads have no measurable short‐term benefits. For non‐brand keywords, we find that new and infrequent users are positively influenced by ads but that more frequent users whose purchasing behavior is not influenced by ads account for most of the advertising expenses, resulting in average returns that are negative.

Suggested Citation

  • Thomas Blake & Chris Nosko & Steven Tadelis, 2015. "Consumer Heterogeneity and Paid Search Effectiveness: A Large‐Scale Field Experiment," Econometrica, Econometric Society, vol. 83, pages 155-174, January.
  • Handle: RePEc:wly:emetrp:v:83:y:2015:i::p:155-174
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    4. Eric T. Anderson & Duncan I. Simester, 2010. "Price Stickiness and Customer Antagonism," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 729-765.
    5. Varian, Hal R., 2007. "Position auctions," International Journal of Industrial Organization, Elsevier, vol. 25(6), pages 1163-1178, December.
    6. Randall Lewis & David Reiley, 2014. "Advertising Effectively Influences Older Users: How Field Experiments Can Improve Measurement and Targeting," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 147-159, March.
    7. Füsun Gönül & Meng Ze Shi, 1998. "Optimal Mailing of Catalogs: A New Methodology Using Estimable Structural Dynamic Programming Models," Management Science, INFORMS, vol. 44(9), pages 1249-1262, September.
    8. Song Yao & Carl F. Mela, 2008. "A Dynamic Model of Sponsored Search Advertising," Working Papers 08-16, NET Institute, revised Sep 2008.
    9. Song Yao & Carl F. Mela, 2011. "A Dynamic Model of Sponsored Search Advertising," Marketing Science, INFORMS, vol. 30(3), pages 447-468, 05-06.
    10. Ackerberg, Daniel A, 2001. "Empirically Distinguishing Informative and Prestige Effects of Advertising," RAND Journal of Economics, The RAND Corporation, vol. 32(2), pages 316-333, Summer.
    11. Tat Y. Chan & Chunhua Wu & Ying Xie, 2011. "Measuring the Lifetime Value of Customers Acquired from Google Search Advertising," Marketing Science, INFORMS, vol. 30(5), pages 837-850, September.
    12. Sha Yang & Anindya Ghose, 2010. "Analyzing the Relationship Between Organic and Sponsored Search Advertising: Positive, Negative, or Zero Interdependence?," Marketing Science, INFORMS, vol. 29(4), pages 602-623, 07-08.
    13. Bagwell, Kyle, 2007. "The Economic Analysis of Advertising," Handbook of Industrial Organization, in: Mark Armstrong & Robert Porter (ed.), Handbook of Industrial Organization, edition 1, volume 3, chapter 28, pages 1701-1844, Elsevier.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Joseph Golden & John Joseph Horton, 2021. "The Effects of Search Advertising on Competitors: An Experiment Before a Merger," Management Science, INFORMS, vol. 67(1), pages 342-362, January.
    2. Weijia Dai & Hyunjin Kim & Michael Luca, 2016. "Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Harvard Business School Working Papers 17-025, Harvard Business School, revised Jan 2023.
    3. Avi Goldfarb, 2014. "What is Different About Online Advertising?," Review of Industrial Organization, Springer;The Industrial Organization Society, vol. 44(2), pages 115-129, March.
    4. Haoyan Sun & Ming Fan & Yong Tan, 2020. "An Empirical Analysis of Seller Advertising Strategies in an Online Marketplace," Information Systems Research, INFORMS, vol. 31(1), pages 37-56, March.
    5. Bernd Skiera & Nadia Abou Nabout, 2013. "Practice Prize Paper ---PROSAD: A Bidding Decision Support System for Profit Optimizing Search Engine Advertising," Marketing Science, INFORMS, vol. 32(2), pages 213-220, March.
    6. Dirk Bergemann & Alessandro Bonatti, 2010. "Targeting in Advertising Markets: Implications for Offline vs. Online Media," Cowles Foundation Discussion Papers 1758, Cowles Foundation for Research in Economics, Yale University.
    7. Shengqi Ye & Goker Aydin & Shanshan Hu, 2015. "Sponsored Search Marketing: Dynamic Pricing and Advertising for an Online Retailer," Management Science, INFORMS, vol. 61(6), pages 1255-1274, June.
    8. Kinshuk Jerath & Liye Ma & Young-Hoon Park & Kannan Srinivasan, 2011. "A "Position Paradox" in Sponsored Search Auctions," Marketing Science, INFORMS, vol. 30(4), pages 612-627, July.
    9. Burguet, Roberto & Caminal, Ramon & Ellman, Matthew, 2015. "In Google we trust?," International Journal of Industrial Organization, Elsevier, vol. 39(C), pages 44-55.
    10. Andrey Simonov & Shawndra Hill, 2021. "Competitive Advertising on Brand Search: Traffic Stealing and Click Quality," Marketing Science, INFORMS, vol. 40(5), pages 923-945, September.
    11. Yu (Jeffrey) Hu & Jiwoong Shin & Zhulei Tang, 2016. "Incentive Problems in Performance-Based Online Advertising Pricing: Cost per Click vs. Cost per Action," Management Science, INFORMS, vol. 62(7), pages 2022-2038, July.
    12. Greg Taylor, 2013. "Search Quality and Revenue Cannibalization by Competing Search Engines," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 22(3), pages 445-467, September.
    13. Li, Sanxi & Sun, Hailin & Yu, Jun, 2023. "Competitive targeted online advertising," International Journal of Industrial Organization, Elsevier, vol. 87(C).
    14. Peitz, Martin & Reisinger, Markus, 2014. "The Economics of Internet Media," Working Papers 14-23, University of Mannheim, Department of Economics.
    15. Wilfred Amaldoss & Kinshuk Jerath & Amin Sayedi, 2016. "Keyword Management Costs and “Broad Match” in Sponsored Search Advertising," Marketing Science, INFORMS, vol. 35(2), pages 259-274, March.
    16. Tarantino, Emanuele, 2013. "A simple model of vertical search engines foreclosure," Telecommunications Policy, Elsevier, vol. 37(1), pages 1-12.
    17. Sridhar Narayanan & Kirthi Kalyanam, 2015. "Position Effects in Search Advertising and their Moderators: A Regression Discontinuity Approach," Marketing Science, INFORMS, vol. 34(3), pages 388-407, May.
    18. Yang, Yupin & Lu, Qiang (Steven) & Tang, Guanting & Pei, Jian, 2015. "The Impact of Market Competition on Search Advertising," Journal of Interactive Marketing, Elsevier, vol. 30(C), pages 46-55.
    19. Chunhua Wu, 2015. "Matching Value and Market Design in Online Advertising Networks: An Empirical Analysis," Marketing Science, INFORMS, vol. 34(6), pages 906-921, November.
    20. Abou Nabout, Nadia & Skiera, Bernd, 2012. "Return on Quality Improvements in Search Engine Marketing," Journal of Interactive Marketing, Elsevier, vol. 26(3), pages 141-154.

    More about this item

    JEL classification:

    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D22 - Microeconomics - - Production and Organizations - - - Firm Behavior: Empirical Analysis
    • L10 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - General
    • L20 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - General
    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce
    • M37 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - Marketing and Advertising - - - Advertising

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:wly:emetrp:v:83:y:2015:i::p:155-174. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Wiley Content Delivery (email available below). General contact details of provider: https://edirc.repec.org/data/essssea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.