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Estimating the Value of Offsite Tracking Data to Advertisers: Evidence from Meta

Author

Listed:
  • Nils Wernerfelt

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Anna Tuchman

    (Kellogg School of Management, Northwestern University, Evanston, Illinois 60208)

  • Bradley T. Shapiro

    (University of Chicago Booth School of Business, Chicago, Illinois 60637)

  • Robert Moakler

    (Meta Platforms, Inc., Menlo Park, California 94025)

Abstract

Third-party cookies and related “offsite” tracking technologies are frequently used to share user data across applications in support of ad delivery. These data are viewed as highly valuable for online advertisers, but their usage faces increasing headwinds. In this paper, we quantify the benefit to advertisers from using such offsite tracking data in their ad delivery. With this goal in mind, we conduct a large-scale, randomized experiment that includes more than 70,000 advertisers on Facebook and Instagram. We first estimate advertising effectiveness at baseline across our broad sample. We then estimate the change in effectiveness of the same campaigns were advertisers to lose the ability to optimize ad delivery with offsite data. In each of these cases, we use recently developed deconvolution techniques to flexibly estimate the underlying distribution of effects. We find a median cost per incremental customer at baseline of $38.16 that under the median loss in effectiveness would rise to $49.93, a 31% increase. Further, we find ads targeted using offsite data generate more long-term customers per dollar than those without, and losing offsite data disproportionately hurts small scale advertisers. Taken together, our results suggest that offsite data bring large benefits to a wide range of advertisers.

Suggested Citation

  • Nils Wernerfelt & Anna Tuchman & Bradley T. Shapiro & Robert Moakler, 2025. "Estimating the Value of Offsite Tracking Data to Advertisers: Evidence from Meta," Marketing Science, INFORMS, vol. 44(2), pages 268-286, March.
    • Handle: RePEc:inm:ormksc:v:44:y:2025:i:2:p:268-286
    DOI: 10.1287/mksc.2023.0274
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    References listed on IDEAS

    as
    1. Peter E. Rossi & Robert E. McCulloch & Greg M. Allenby, 1996. "The Value of Purchase History Data in Target Marketing," Marketing Science, INFORMS, vol. 15(4), pages 321-340.
    2. Bradley Efron, 2016. "Empirical Bayes deconvolution estimates," Biometrika, Biometrika Trust, vol. 103(1), pages 1-20.
    3. Tianshu Sun & Zhe Yuan & Chunxiao Li & Kaifu Zhang & Jun Xu, 2024. "The Value of Personal Data in Internet Commerce: A High-Stakes Field Experiment on Data Regulation Policy," Management Science, INFORMS, vol. 70(4), pages 2645-2660, April.
    4. repec:hal:journl:hal-04810695 is not listed on IDEAS
    5. 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.
    6. Avi Goldfarb & Catherine E. Tucker, 2011. "Privacy Regulation and Online Advertising," Management Science, INFORMS, vol. 57(1), pages 57-71, January.
    7. 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.
    8. Dirk Bergemann & Jacques Crémer & David Dinielli & Carl-Christian Groh & Paul Heidhues & Maximilian Schäfer & Monika Schnitzer & Fiona Scott Morton & Katja Seim & Michael Sullivan, 2023. "Market design for personal data," Post-Print hal-04213208, HAL.
    9. Jian Jia & Ginger Zhe Jin & Liad Wagman, 2018. "The Short-Run Effects of GDPR on Technology Venture Investment," NBER Working Papers 25248, National Bureau of Economic Research, Inc.
    10. Steven Tadelis & Christopher Hooton & Utsav Manjeer & Daniel Deisenroth & Nils Wernerfelt & Nick Dadson & Lindsay Greenbaum, 2023. "Learning, Sophistication, and the Returns to Advertising: Implications for Differences in Firm Performance," NBER Working Papers 31201, National Bureau of Economic Research, Inc.
    11. Rebecca Janßen & Reinhold Kesler & Michael E. Kummer & Joel Waldfogel, 2022. "GDPR and the Lost Generation of Innovative Apps," NBER Working Papers 30028, National Bureau of Economic Research, Inc.
    12. Susan Athey & Christian Catalini & Catherine Tucker, 2017. "The Digital Privacy Paradox: Small Money, Small Costs, Small Talk," NBER Working Papers 23488, National Bureau of Economic Research, Inc.
    13. Samuel G. Goldberg & Garrett A. Johnson & Scott K. Shriver, 2024. "Regulating Privacy Online: An Economic Evaluation of the GDPR," American Economic Journal: Economic Policy, American Economic Association, vol. 16(1), pages 325-358, February.
    14. James Campbell & Avi Goldfarb & Catherine Tucker, 2015. "Privacy Regulation and Market Structure," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(1), pages 47-73, March.
    15. Jean-Pierre Dubé & Sanjog Misra, 2023. "Personalized Pricing and Consumer Welfare," Journal of Political Economy, University of Chicago Press, vol. 131(1), pages 131-189.
    16. Garrett A. Johnson & Scott K. Shriver & Samuel G. Goldberg, 2023. "Privacy and Market Concentration: Intended and Unintended Consequences of the GDPR," Management Science, INFORMS, vol. 69(10), pages 5695-5721, October.
    17. Manohar U. Kalwani & Alvin J. Silk, 1982. "On the Reliability and Predictive Validity of Purchase Intention Measures," Marketing Science, INFORMS, vol. 1(3), pages 243-286.
    18. 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.
    19. Bradley T. Shapiro & Günter J. Hitsch & Anna E. Tuchman, 2021. "TV Advertising Effectiveness and Profitability: Generalizable Results From 288 Brands," Econometrica, Econometric Society, vol. 89(4), pages 1855-1879, July.
    20. Tesary Lin, 2022. "Valuing Intrinsic and Instrumental Preferences for Privacy," Marketing Science, INFORMS, vol. 41(4), pages 663-681, July.
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    Cited by:

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    2. Beknazar-Yuzbashev, George & Jiménez-Durán, Rafael & Simonov, Andrey & Stalinski, Mateusz, 2026. "Social Media Advertising Loads as Prices," CAGE Online Working Paper Series 792, Competitive Advantage in the Global Economy (CAGE).
    3. Zhengrong Gu & Garrett A. Johnson & Shunto J. Kobayashi, 2025. "Can Privacy Technologies Replace Cookies? Ad Revenue in a Field Experiment," CESifo Working Paper Series 11931, CESifo.
    4. JoonHo Lee & Daihe Sui, 2025. "Fully Bayesian Inference for Meta-Analytic Deconvolution Using Efron’s Log-Spline Prior," Mathematics, MDPI, vol. 13(16), pages 1-49, August.
    5. Anya Shchetkina, 2025. "Blind Targeting: Personalization under Third-Party Privacy Constraints," Papers 2507.05175, arXiv.org.

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