IDEAS home Printed from https://ideas.repec.org/p/ces/ceswps/_6684.html
   My bibliography  Save this paper

A Large-Scale Field Experiment to Evaluate the Effectiveness of Paid Search Advertising

Author

Listed:
  • Lorenzo Coviello
  • Uri Gneezy
  • Lorenz Götte

Abstract

Companies spend billions of dollars online for paid links to branded search terms. Measuring the effectiveness of this marketing spending is hard. Blake, Nosko and Tadelis (2015) ran an experiment with eBay, showing that when the company suspended paid search, most of the traffic still ended up on its website. Can findings from one of the largest companies in the world be generalized? We conducted a similar experiment with Edmunds.com, arguably a more representative company, and found starkly different results. More than half of the paid traffic is lost when we shut off paid-links search. These results suggest money spent on search-engine marketing may be more effective than previously documented.

Suggested Citation

  • Lorenzo Coviello & Uri Gneezy & Lorenz Götte, 2017. "A Large-Scale Field Experiment to Evaluate the Effectiveness of Paid Search Advertising," CESifo Working Paper Series 6684, CESifo.
  • Handle: RePEc:ces:ceswps:_6684
    as

    Download full text from publisher

    File URL: https://www.cesifo.org/DocDL/cesifo1_wp6684.pdf
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Motta, Massimo & Penta, Antonio, 2022. "Market Effects of Sponsored Search Auctions," TSE Working Papers 22-1370, Toulouse School of Economics (TSE).
    2. 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.
    3. Chalil, Tengku Munawar & Dahana, Wirawan Dony & Baumann, Chris, 2020. "How do search ads induce and accelerate conversion? The moderating role of transaction experience and organizational type," Journal of Business Research, Elsevier, vol. 116(C), pages 324-336.
    4. Sviták, Jan & Tichem, Jan & Haasbeek, Stefan, 2021. "Price effects of search advertising restrictions," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    5. Weijia Dai & Hyunjin Kim & Michael Luca, 2023. "Frontiers: Which Firms Gain from Digital Advertising? Evidence from a Field Experiment," Marketing Science, INFORMS, vol. 42(3), pages 429-439, May.

    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. Mariia I. Okuneva & Dmitriy B. Potapov, 2015. "The Effectiveness of Individual Targeting Through Smartphone Application in Retail: Evidence from Field Experiment," HSE Working papers WP BRP 47/MAN/2015, National Research University Higher School of Economics.
    2. Natalie Cox & Benjamin Handel & Jonathan Kolstad & Neale Mahoney, 2015. "Messaging and the Mandate: The Impact of Consumer Experience on Health Insurance Enrollment through Exchanges," American Economic Review, American Economic Association, vol. 105(5), pages 105-109, May.
    3. Randall Lewis & David Reiley, 2014. "Online ads and offline sales: measuring the effect of retail advertising via a controlled experiment on Yahoo!," Quantitative Marketing and Economics (QME), Springer, vol. 12(3), pages 235-266, September.
    4. Randall Lewis & Dan Nguyen, 2015. "Display advertising’s competitive spillovers to consumer search," Quantitative Marketing and Economics (QME), Springer, vol. 13(2), pages 93-115, June.
    5. Jacob LaRiviere & Mikolaj Czajkowski & Nick Hanley & Katherine Simpson, 2016. "What is the Causal Impact of Knowledge on Preferences in Stated Preference Studies?," Working Papers 2016-12, Faculty of Economic Sciences, University of Warsaw.
    6. Sviták, Jan & Tichem, Jan & Haasbeek, Stefan, 2021. "Price effects of search advertising restrictions," International Journal of Industrial Organization, Elsevier, vol. 77(C).
    7. George Z. Gui, 2024. "Combining Observational and Experimental Data to Improve Efficiency Using Imperfect Instruments," Marketing Science, INFORMS, vol. 43(2), pages 378-391, March.
    8. Wei Zhou & Zidong Wang, 2020. "Competing for Search Traffic in Query Markets: Entry Strategy, Platform Design, and Entrepreneurship," Working Papers 20-12, NET Institute.
    9. Kirthi Kalyanam & John McAteer & Jonathan Marek & James Hodges & Lifeng Lin, 2018. "Cross channel effects of search engine advertising on brick & mortar retail sales: Meta analysis of large scale field experiments on Google.com," Quantitative Marketing and Economics (QME), Springer, vol. 16(1), pages 1-42, March.
    10. Eiji Yamamura, 2015. "Is university sports an advertisement in the higher education market? An analysis of the Hakone long-distance relay road race in Japan," ISER Discussion Paper 0922, Institute of Social and Economic Research, Osaka University.
    11. Gui Liberali & Alina Ferecatu, 2022. "Morphing for Consumer Dynamics: Bandits Meet Hidden Markov Models," Marketing Science, INFORMS, vol. 41(4), pages 769-794, July.
    12. Uddin, Main & Wang, Liang Choon & Smyth, Russell, 2021. "Do government-initiated energy comparison sites encourage consumer search and lower prices? Evidence from an online randomized controlled experiment in Australia," Journal of Economic Behavior & Organization, Elsevier, vol. 188(C), pages 167-182.
    13. Raluca M. Ursu, 2018. "The Power of Rankings: Quantifying the Effect of Rankings on Online Consumer Search and Purchase Decisions," Marketing Science, INFORMS, vol. 37(4), pages 530-552, August.
    14. Francesco Decarolis & Gabriele Rovigatti, 2021. "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," American Economic Review, American Economic Association, vol. 111(10), pages 3299-3327, October.
    15. Navdeep S. Sahni & Charles Zhang, 2024. "Are consumers averse to sponsored messages? The role of search advertising in information discovery," Quantitative Marketing and Economics (QME), Springer, vol. 22(1), pages 63-114, March.
    16. Xiang Hui & Meng Liu, 2022. "Quality Certificates Alleviate Consumer Aversion to Sponsored Search Advertising," CESifo Working Paper Series 9886, CESifo.
    17. Bart J. Bronnenberg & Jean-Pierre Dubé & Chad Syverson, 2022. "Marketing Investment and Intangible Brand Capital," Journal of Economic Perspectives, American Economic Association, vol. 36(3), pages 53-74, Summer.
    18. George Gui & Harikesh Nair & Fengshi Niu, 2021. "Auction Throttling and Causal Inference of Online Advertising Effects," Papers 2112.15155, arXiv.org, revised Feb 2022.
    19. Motta, Massimo & Penta, Antonio, 2022. "Market Effects of Sponsored Search Auctions," TSE Working Papers 22-1370, Toulouse School of Economics (TSE).
    20. Susan Athey & Kristen Grabarz & Michael Luca & Nils Wernerfelt, 2023. "Digital public health interventions at scale: The impact of social media advertising on beliefs and outcomes related to COVID vaccines," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 120(5), pages 2208110120-, January.

    More about this item

    Keywords

    field experiment; online advertising;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • C93 - Mathematical and Quantitative Methods - - Design of Experiments - - - Field Experiments
    • D03 - Microeconomics - - General - - - Behavioral Microeconomics: Underlying Principles

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:ces:ceswps:_6684. 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: Klaus Wohlrabe (email available below). General contact details of provider: https://edirc.repec.org/data/cesifde.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.