IDEAS home Printed from https://ideas.repec.org/p/arx/papers/2303.10906.html
   My bibliography  Save this paper

The Economic Value of User Tracking for Publishers

Author

Listed:
  • Rene Laub
  • Klaus M. Miller
  • Bernd Skiera

Abstract

Regulators and browsers increasingly restrict user tracking to protect users' privacy online. In two large-scale empirical studies, we study the economic implications for publishers relying on selling advertising space to finance their content. In our first study, we draw on 42 million ad impressions from 111 publishers covering EU desktop browsing traffic in 2016. In our second study, we use 218 million ad impressions from 10,526 publishers (i.e., apps) covering EU and US mobile in-app browsing traffic in 2023. The two studies differ in the share of trackable users (Study 1: 85%; Study 2: Apple: 17%, Android: 91%). Still, we find similar average ad impression price decreases (Study 1: 18% and Study 2: 23%) when user tracking is unavailable. More than 90% of the publishers realize lower prices when selling ad impressions for untrackable users. Publishers offering content on sports, cars, lifestyle & shopping, and news & information suffer the most. Premium publishers with high-quality edited content and strong reputations, thematic-focused (niche) publishers, and smaller publishers suffer less from the unavailability of user tracking. In contrast, non-premium publishers with non-edited or user-generated content, thematic-broad (general news) publishers, and larger publishers suffer more. The availability of a user ID generates the highest value for publishers, whereas collecting a user's browsing history, perceived as intrusive by most users, generates only a small value for publishers. These results affirm that ensuring user privacy online has substantial costs for online publishers, but those costs differ across publishers and the type of collected data. This article offers suggestions to reduce these costs.

Suggested Citation

  • Rene Laub & Klaus M. Miller & Bernd Skiera, 2023. "The Economic Value of User Tracking for Publishers," Papers 2303.10906, arXiv.org, revised Apr 2024.
  • Handle: RePEc:arx:papers:2303.10906
    as

    Download full text from publisher

    File URL: http://arxiv.org/pdf/2303.10906
    File Function: Latest version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Nico Neumann & Catherine E. Tucker & Timothy Whitfield, 2019. "Frontiers: How Effective Is Third-Party Consumer Profiling? Evidence from Field Studies," Marketing Science, INFORMS, vol. 38(6), pages 918-926, November.
    2. James J. Heckman, 1976. "The Common Structure of Statistical Models of Truncation, Sample Selection and Limited Dependent Variables and a Simple Estimator for Such Models," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 5, number 4, pages 475-492, National Bureau of Economic Research, Inc.
    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. Heejung Bang & James M. Robins, 2005. "Doubly Robust Estimation in Missing Data and Causal Inference Models," Biometrics, The International Biometric Society, vol. 61(4), pages 962-973, December.
    5. Simon Board, 2009. "Revealing information in auctions: the allocation effect," Economic Theory, Springer;Society for the Advancement of Economic Theory (SAET), vol. 38(1), pages 125-135, January.
    6. Vilma Todri & Anindya Ghose & Param Vir Singh, 2020. "Trade-Offs in Online Advertising: Advertising Effectiveness and Annoyance Dynamics Across the Purchase Funnel," Information Systems Research, INFORMS, vol. 31(1), pages 102-125, March.
    7. Christopher A. Summers & Robert W. Smith & Rebecca Walker Reczek, 2016. "An Audience of One: Behaviorally Targeted Ads as Implied Social Labels," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 43(1), pages 156-178.
    8. Jonathan Levin & Paul Milgrom, 2010. "Online Advertising: Heterogeneity and Conflation in Market Design," American Economic Review, American Economic Association, vol. 100(2), pages 603-607, May.
    9. Anderl, Eva & Becker, Ingo & von Wangenheim, Florian & Schumann, Jan Hendrik, 2016. "Mapping the customer journey: Lessons learned from graph-based online attribution modeling," International Journal of Research in Marketing, Elsevier, vol. 33(3), pages 457-474.
    10. 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.
    11. Guido W. Imbens, 2004. "Nonparametric Estimation of Average Treatment Effects Under Exogeneity: A Review," The Review of Economics and Statistics, MIT Press, vol. 86(1), pages 4-29, February.
    12. 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.
    13. Michael Trusov & Liye Ma & Zainab Jamal, 2016. "Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting," Marketing Science, INFORMS, vol. 35(3), pages 405-426, May.
    14. Tamara Dinev & Paul Hart, 2006. "An Extended Privacy Calculus Model for E-Commerce Transactions," Information Systems Research, INFORMS, vol. 17(1), pages 61-80, March.
    15. Glynn, Adam N. & Quinn, Kevin M., 2010. "An Introduction to the Augmented Inverse Propensity Weighted Estimator," Political Analysis, Cambridge University Press, vol. 18(1), pages 36-56, January.
    16. Naresh K. Malhotra & Sung S. Kim & James Agarwal, 2004. "Internet Users' Information Privacy Concerns (IUIPC): The Construct, the Scale, and a Causal Model," Information Systems Research, INFORMS, vol. 15(4), pages 336-355, December.
    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. David A. Schweidel & Yakov Bart & J. Jeffrey Inman & Andrew T. Stephen & Barak Libai & Michelle Andrews & Ana Babić Rosario & Inyoung Chae & Zoey Chen & Daniella Kupor & Chiara Longoni & Felipe Thomaz, 2022. "How consumer digital signals are reshaping the customer journey," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1257-1276, November.
    2. 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.
    3. 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.
    4. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2013. "The performance of estimators based on the propensity score," Journal of Econometrics, Elsevier, vol. 175(1), pages 1-21.
    5. Nico Neumann & Catherine E. Tucker & Kumar Subramanyam & John Marshall, 2023. "Is first- or third-party audience data more effective for reaching the ‘right’ customers? The case of IT decision-makers," Quantitative Marketing and Economics (QME), Springer, vol. 21(4), pages 519-571, December.
    6. Huber, Martin & Lechner, Michael & Wunsch, Conny, 2010. "How to Control for Many Covariates? Reliable Estimators Based on the Propensity Score," IZA Discussion Papers 5268, Institute of Labor Economics (IZA).
    7. Stylianos Despotakis & Jungju Yu, 2023. "Multidimensional Targeting and Consumer Response," Management Science, INFORMS, vol. 69(8), pages 4518-4540, August.
    8. Konan Alain N'Ghauran & Corinne Autant-Bernard, 2020. "Assessing the collaboration and network additionality of innovation policies: a counterfactual approach to the French cluster policy," Post-Print halshs-03128972, HAL.
    9. Òscar Jordà & Alan M. Taylor, 2016. "The Time for Austerity: Estimating the Average Treatment Effect of Fiscal Policy," Economic Journal, Royal Economic Society, vol. 126(590), pages 219-255, February.
    10. Jiwoong Shin & Jungju Yu, 2021. "Targeted Advertising and Consumer Inference," Marketing Science, INFORMS, vol. 40(5), pages 900-922, September.
    11. Felipe Thomaz & Carolina Salge & Elena Karahanna & John Hulland, 2020. "Learning from the Dark Web: leveraging conversational agents in the era of hyper-privacy to enhance marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 43-63, January.
    12. K. Sudhir & Seung Yoon Lee & Subroto Roy, 2021. "Lookalike Targeting on Others' Journeys: Brand Versus Performance Marketing," Cowles Foundation Discussion Papers 2302R, Cowles Foundation for Research in Economics, Yale University, revised Jun 2022.
    13. Michela Bia & Martin Huber & Luk'av{s} Laff'ers, 2020. "Double machine learning for sample selection models," Papers 2012.00745, arXiv.org, revised Jul 2021.
    14. Dridi, Ichrak & Boughrara, Adel, 2023. "Flexible inflation targeting and stock market volatility: Evidence from emerging market economies," Economic Modelling, Elsevier, vol. 126(C).
    15. Bleier, Alexander & Eisenbeiss, Maik, 2015. "The Importance of Trust for Personalized Online Advertising," Journal of Retailing, Elsevier, vol. 91(3), pages 390-409.
    16. K. Sudhir & Seung Yoon Lee & Subroto Roy, 2021. "Lookalike Targeting on Others' Journeys: Brand Versus Performance Marketing," Cowles Foundation Discussion Papers 2302, Cowles Foundation for Research in Economics, Yale University.
    17. Martin Huber, 2014. "Treatment Evaluation in the Presence of Sample Selection," Econometric Reviews, Taylor & Francis Journals, vol. 33(8), pages 869-905, November.
    18. Gupta, Shaphali & Leszkiewicz, Agata & Kumar, V. & Bijmolt, Tammo & Potapov, Dmitriy, 2020. "Digital Analytics: Modeling for Insights and New Methods," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 26-43.
    19. Potoglou, Dimitris & Palacios, Juan & Feijoo, Claudio & Gómez Barroso, Jose-Luis, 2015. "The supply of personal information: A study on the determinants of information provision in e-commerce scenarios," 26th European Regional ITS Conference, Madrid 2015 127174, International Telecommunications Society (ITS).
    20. Corey Angst, 2009. "Protect My Privacy or Support the Common-Good? Ethical Questions About Electronic Health Information Exchanges," Journal of Business Ethics, Springer, vol. 90(2), pages 169-178, November.

    More about this item

    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:arx:papers:2303.10906. 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: arXiv administrators (email available below). General contact details of provider: http://arxiv.org/ .

    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.