IDEAS home Printed from https://ideas.repec.org/a/inm/ormksc/v43y2024i2p407-418.html
   My bibliography  Save this article

What Cookie-Based Advertising Effectiveness Fails to Measure

Author

Listed:
  • Min Tian

    (The Ohio State University, Columbus, Ohio 43210)

  • Paul R. Hoban

    (Amazon.com, Inc., Seattle, Washington 98109)

  • Neeraj Arora

    (University of Wisconsin–Madison, Madison, Wisconsin 53706)

Abstract

Retargeted advertising is a popular form of digital advertising that algorithmically delivers ads to users who previously visited an advertiser’s website. We empirically investigate the challenge of estimating individual-level advertising response when the data stem from a field experiment randomized at the cookie level. Such experiments are common in the industry. We investigate how cookie-level randomization manifests itself at the individual level and the role it plays in measuring retargeted advertising effectiveness. We show that cookie-based analyses to assess retargeted advertising effectiveness are flawed. Cookie randomization breaks down because of the presence of individuals who have both treatment and control cookies—these individuals are more engaged and spend more. Because of the cookie propagation effect we uncover, individual assignment to the treatment and control groups is not random. Individual-level analyses, in conjunction with first-party data, paint a more complete picture of the impact of retargeted advertising. We detect an increase in offline sales that cookie-based analyses fail to capture. Although many marketers are apprehensive about the cookie-less world of the future, we show that cookie-based analyses can be misleading. Individual data, obtained with consent while protecting identity, are better and overcome many of these problems with cookie-based analyses.

Suggested Citation

  • Min Tian & Paul R. Hoban & Neeraj Arora, 2024. "What Cookie-Based Advertising Effectiveness Fails to Measure," Marketing Science, INFORMS, vol. 43(2), pages 407-418, March.
  • Handle: RePEc:inm:ormksc:v:43:y:2024:i:2:p:407-418
    DOI: 10.1287/mksc.2023.1453
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mksc.2023.1453
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mksc.2023.1453?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Navdeep S. Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    2. Avi Goldfarb & Catherine Tucker, 2011. "Rejoinder--Implications of "Online Display Advertising: Targeting and Obtrusiveness"," Marketing Science, INFORMS, vol. 30(3), pages 413-415, 05-06.
    3. Garrett A. Johnson & Scott K. Shriver & Shaoyin Du, 2020. "Consumer Privacy Choice in Online Advertising: Who Opts Out and at What Cost to Industry?," Marketing Science, INFORMS, vol. 39(1), pages 33-51, January.
    4. Oliver Rutz & Randolph Bucklin, 2012. "Does banner advertising affect browsing for brands? clickstream choice model says yes, for some," Quantitative Marketing and Economics (QME), Springer, vol. 10(2), pages 231-257, June.
    5. Navdeep S. Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    6. Tesary Lin & Sanjog Misra, 2022. "Frontiers: The Identity Fragmentation Bias," Marketing Science, INFORMS, vol. 41(3), pages 433-440, May.
    7. Michael Braun & Wendy W. Moe, 2013. "Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories," Marketing Science, INFORMS, vol. 32(5), pages 753-767, September.
    8. Unnati Narang & Venkatesh Shankar, 2019. "Mobile App Introduction and Online and Offline Purchases and Product Returns," Marketing Science, INFORMS, vol. 38(5), pages 756-772, September.
    9. Avi Goldfarb & Catherine Tucker, 2011. "Online Display Advertising: Targeting and Obtrusiveness," Marketing Science, INFORMS, vol. 30(3), pages 389-404, 05-06.
    10. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    11. Imbens,Guido W. & Rubin,Donald B., 2015. "Causal Inference for Statistics, Social, and Biomedical Sciences," Cambridge Books, Cambridge University Press, number 9780521885881, September.
    12. Navdeep Sahni, 2015. "Erratum to: Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 249-250, September.
    13. Navdeep Sahni, 2015. "Effect of temporal spacing between advertising exposures: Evidence from online field experiments," Quantitative Marketing and Economics (QME), Springer, vol. 13(3), pages 203-247, September.
    14. Alexander Bleier & Maik Eisenbeiss, 2015. "Personalized Online Advertising Effectiveness: The Interplay of What, When, and Where," Marketing Science, INFORMS, vol. 34(5), pages 669-688, September.
    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. Ryan Dew & Nicolas Padilla & Anya Shchetkina, 2024. "Your MMM is Broken: Identification of Nonlinear and Time-varying Effects in Marketing Mix Models," Papers 2408.07678, arXiv.org.

    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. Christina Uhl & Nadia Abou Nabout & Klaus Miller, 2020. "How Much Ad Viewability is Enough? The Effect of Display Ad Viewability on Advertising Effectiveness," Papers 2008.12132, arXiv.org.
    2. Hana Choi & Carl F. Mela & Santiago R. Balseiro & Adam Leary, 2020. "Online Display Advertising Markets: A Literature Review and Future Directions," Information Systems Research, INFORMS, vol. 31(2), pages 556-575, June.
    3. 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.
    4. Johannes Hermle & Giorgio Martini, 2022. "Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split," Papers 2207.00206, arXiv.org.
    5. Kurt P. Munz & Minah H. Jung & Adam L. Alter, 2020. "Name Similarity Encourages Generosity: A Field Experiment in Email Personalization," Marketing Science, INFORMS, vol. 39(6), pages 1071-1091, November.
    6. Brett R. Gordon & Florian Zettelmeyer & Neha Bhargava & Dan Chapsky, 2019. "A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook," Marketing Science, INFORMS, vol. 38(2), pages 193-225, March.
    7. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    8. Garrett A. Johnson & Randall A. Lewis & David H. Reiley, 2017. "When Less Is More: Data and Power in Advertising Experiments," Marketing Science, INFORMS, vol. 36(1), pages 43-53, January.
    9. Thomas W. Frick & Rodrigo Belo & Rahul Telang, 2023. "Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment," Management Science, INFORMS, vol. 69(3), pages 1665-1686, March.
    10. Omid Rafieian & Hema Yoganarasimhan, 2021. "Targeting and Privacy in Mobile Advertising," Marketing Science, INFORMS, vol. 40(2), pages 193-218, March.
    11. 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.
    12. Miller, Klaus M. & Skiera, Bernd, 2024. "Economic consequences of online tracking restrictions: Evidence from cookies," International Journal of Research in Marketing, Elsevier, vol. 41(2), pages 241-264.
    13. Försch, Steffen & de Haan, Evert, 2018. "Targeting online display ads: Choosing their frequency and spacing," International Journal of Research in Marketing, Elsevier, vol. 35(4), pages 661-672.
    14. Benjamin Heymann & Alexandre Gilotte & R'emi Chan-Renous, 2023. "Repeated Bidding with Dynamic Value," Papers 2308.01755, arXiv.org.
    15. Shun-Yang Lee & Julian Runge & Daniel Yoo & Yakov Bart & Anett Gyurak & J. W. Schneider, 2023. "COVID-19 Demand Shocks Revisited: Did Advertising Technology Help Mitigate Adverse Consequences for Small and Midsize Businesses?," Papers 2307.09035, arXiv.org, revised Jan 2024.
    16. Chadwick J. Miller & Daniel C. Brannon & Jim Salas & Martha Troncoza, 2021. "Advertising, incentives, and the upsell: how advertising differentially moderates customer- vs. retailer-directed price incentives’ impact on consumers’ preferences for premium products," Journal of the Academy of Marketing Science, Springer, vol. 49(6), pages 1043-1064, November.
    17. Navdeep S. Sahni & Dan Zou & Pradeep K. Chintagunta, 2017. "Do Targeted Discount Offers Serve as Advertising? Evidence from 70 Field Experiments," Management Science, INFORMS, vol. 63(8), pages 2688-2705, August.
    18. Chen He & Tobias J. Klein, 2023. "Advertising as a Reminder: Evidence from the Dutch State Lottery," Marketing Science, INFORMS, vol. 42(5), pages 892-909, September.
    19. Jan Krämer & Daniel Schnurr & Michael Wohlfarth, 2019. "Winners, Losers, and Facebook: The Role of Social Logins in the Online Advertising Ecosystem," Management Science, INFORMS, vol. 65(4), pages 1678-1699, April.
    20. Wesley R. Hartmann & Daniel Klapper, 2018. "Super Bowl Ads," Marketing Science, INFORMS, vol. 37(1), pages 78-96, January.

    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:inm:ormksc:v:43:y:2024:i:2:p:407-418. 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: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.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.