IDEAS home Printed from https://ideas.repec.org/a/inm/ormnsc/v69y2023i3p1665-1686.html
   My bibliography  Save this article

Incentive Misalignments in Programmatic Advertising: Evidence from a Randomized Field Experiment

Author

Listed:
  • Thomas W. Frick

    (Marketing Analyst, EPOS Group A/S, 2750 Ballerup, Denmark)

  • Rodrigo Belo

    (Nova School of Business and Economics, Universidade Nova de Lisboa, 2775-405 Carcavelos, Portugal; Rotterdam School of Management, Erasmus University, 3062 PA Rotterdam, Netherlands)

  • Rahul Telang

    (The Heinz College, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213)

Abstract

In programmatic advertising, firms outsource the bidding for ad impressions to ad platforms. Although firms are interested in targeting consumers that respond positively to advertising, ad platforms are usually rewarded for targeting consumers with high overall purchase probability. We develop a theoretical model that shows if consumers with high baseline purchase probability respond more positively to advertising, then firms and ad platforms agree on which consumers to target. If, conversely, consumers with low baseline purchase probability are the ones for which ads work best, then ad platforms target consumers that firms do not want to target—the incentives are misaligned. We conduct a large-scale randomized field experiment, targeting 208,538 individual consumers, in a display retargeting campaign. Our unique data set allows us to both causally identify advertising effectiveness and estimate the degree of incentive misalignments between the firm and ad platform. In accordance with the contracted incentives, the ad platform targets consumers that are more likely to purchase. Importantly, we find no evidence that ads are more effective for consumers with higher baseline purchase probability, rendering the ad platform’s bidding suboptimal for the firm. A welfare analysis suggests that the ad platform’s bidding optimization leads to a loss in profit for the firm and an overall decline in welfare. To remedy the incentive misalignment, we propose a solution in which the firm restricts the ad platform to target only consumers that are profitable based on individual consumer-level estimates for baseline purchase probability and ad effectiveness.

Suggested Citation

  • 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.
  • Handle: RePEc:inm:ormnsc:v:69:y:2023:i:3:p:1665-1686
    DOI: 10.1287/mnsc.2022.4438
    as

    Download full text from publisher

    File URL: http://dx.doi.org/10.1287/mnsc.2022.4438
    Download Restriction: no

    File URL: https://libkey.io/10.1287/mnsc.2022.4438?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. Eric M. Schwartz & Eric T. Bradlow & Peter S. Fader, 2017. "Customer Acquisition via Display Advertising Using Multi-Armed Bandit Experiments," Marketing Science, INFORMS, vol. 36(4), pages 500-522, July.
    2. Santiago R. Balseiro & Jon Feldman & Vahab Mirrokni & S. Muthukrishnan, 2014. "Yield Optimization of Display Advertising with Ad Exchange," Management Science, INFORMS, vol. 60(12), pages 2886-2907, December.
    3. 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.
    4. 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.
    5. Maurice J. G. Bun & Teresa D. Harrison, 2019. "OLS and IV estimation of regression models including endogenous interaction terms," Econometric Reviews, Taylor & Francis Journals, vol. 38(7), pages 814-827, August.
    6. Ron Berman, 2018. "Beyond the Last Touch: Attribution in Online Advertising," Marketing Science, INFORMS, vol. 37(5), pages 771-792, September.
    7. 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.
    8. 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.
    9. 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.
    10. Edward Clarke, 1971. "Multipart pricing of public goods," Public Choice, Springer, vol. 11(1), pages 17-33, September.
    11. Ashish Agarwal & Tridas Mukhopadhyay, 2016. "The Impact of Competing Ads on Click Performance in Sponsored Search," Information Systems Research, INFORMS, vol. 27(3), pages 538-557.
    12. 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.
    13. Daniel Zantedeschi & Eleanor McDonnell Feit & Eric T. Bradlow, 2017. "Measuring Multichannel Advertising Response," Management Science, INFORMS, vol. 63(8), pages 2706-2728, August.
    14. 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.
    15. Gediminas Adomavicius & Shawn P. Curley & Alok Gupta & Pallab Sanyal, 2012. "Effect of Information Feedback on Bidder Behavior in Continuous Combinatorial Auctions," Management Science, INFORMS, vol. 58(4), pages 811-830, April.
    16. Juan Feng & Jinhong Xie, 2012. "Research Note ---Performance-Based Advertising: Advertising as Signals of Product Quality," Information Systems Research, INFORMS, vol. 23(3-part-2), pages 1030-1041, September.
    17. 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.
    18. Nick Arnosti & Marissa Beck & Paul Milgrom, 2016. "Adverse Selection and Auction Design for Internet Display Advertising," American Economic Review, American Economic Association, vol. 106(10), pages 2852-2866, October.
    19. 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.
    20. William Vickrey, 1961. "Counterspeculation, Auctions, And Competitive Sealed Tenders," Journal of Finance, American Finance Association, vol. 16(1), pages 8-37, March.
    21. 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.
    22. Kursad Asdemir & Nanda Kumar & Varghese S. Jacob, 2012. "Pricing Models for Online Advertising: CPM vs. CPC," Information Systems Research, INFORMS, vol. 23(3-part-1), pages 804-822, September.
    23. Yixin Lu & Alok Gupta & Wolfgang Ketter & Eric van Heck, 2019. "Information Transparency in Business-to-Business Auction Markets: The Role of Winner Identity Disclosure," Management Science, INFORMS, vol. 65(9), pages 4261-4279, September.
    24. Groves, Theodore, 1973. "Incentives in Teams," Econometrica, Econometric Society, vol. 41(4), pages 617-631, July.
    25. Randall A. Lewis & Justin M. Rao, 2015. "The Unfavorable Economics of Measuring the Returns to Advertising," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(4), pages 1941-1973.
    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. 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.
    2. Brett R Gordon & Kinshuk Jerath & Zsolt Katona & Sridhar Narayanan & Jiwoong Shin & Kenneth C Wilbur, 2019. "Inefficiencies in Digital Advertising Markets," Papers 1912.09012, arXiv.org, revised Feb 2020.
    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. Garrett Johnson & Julian Runge & Eric Seufert, 2022. "Privacy-Centric Digital Advertising: Implications for Research," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 9(1), pages 49-54, June.
    5. Johannes Hermle & Giorgio Martini, 2022. "Valid and Unobtrusive Measurement of Returns to Advertising through Asymmetric Budget Split," Papers 2207.00206, arXiv.org.
    6. 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.
    7. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Predictive Incrementality by Experimentation (PIE) for Ad Measurement," Papers 2304.06828, arXiv.org.
    8. Omid Rafieian, 2023. "Optimizing User Engagement Through Adaptive Ad Sequencing," Marketing Science, INFORMS, vol. 42(5), pages 910-933, September.
    9. Du, Ruihuan & Zhong, Yu & Nair, Harikesh S. & Cui, Bo & Shou, Ruyang, 2019. "Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network," Research Papers 3761, Stanford University, Graduate School of Business.
    10. Stephan Seiler & Song Yao & Wenbo Wang, 2017. "Does Online Word of Mouth Increase Demand? (And How?) Evidence from a Natural Experiment," Marketing Science, INFORMS, vol. 36(6), pages 838-861, November.
    11. 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.
    12. Karthik Kannan & Rajib L. Saha & Warut Khern-am-nuai, 2022. "Identifying Perverse Incentives in Buyer Profiling on Online Trading Platforms," Information Systems Research, INFORMS, vol. 33(2), pages 464-475, June.
    13. 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.
    14. Wesley R. Hartmann & Daniel Klapper, 2018. "Super Bowl Ads," Marketing Science, INFORMS, vol. 37(1), pages 78-96, January.
    15. Benjamin Heymann & Alexandre Gilotte & R'emi Chan-Renous, 2023. "Repeated Bidding with Dynamic Value," Papers 2308.01755, arXiv.org.
    16. Navdeep S. Sahni & Harikesh S. Nair, 2020. "Sponsorship Disclosure and Consumer Deception: Experimental Evidence from Native Advertising in Mobile Search," Marketing Science, INFORMS, vol. 39(1), pages 5-32, January.
    17. Caio Waisman & Navdeep S. Sahni & Harikesh S. Nair & Xiliang Lin, 2019. "Parallel Experimentation and Competitive Interference on Online Advertising Platforms," Papers 1903.11198, arXiv.org, revised Feb 2024.
    18. 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.
    19. 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.
    20. 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.

    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:ormnsc:v:69:y:2023:i:3:p:1665-1686. 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.