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Characterizing and Minimizing Divergent Delivery in Meta Advertising Experiments

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Listed:
  • Gordon Burtch
  • Robert Moakler
  • Brett R. Gordon
  • Poppy Zhang
  • Shawndra Hill

Abstract

Many digital platforms offer advertisers experimentation tools like Meta's Lift and A/B tests to optimize their ad campaigns. Lift tests compare outcomes between users eligible to see ads versus users in a no-ad control group. In contrast, A/B tests compare users exposed to alternative ad configurations, absent any control group. The latter setup raises the prospect of divergent delivery: ad delivery algorithms may target different ad variants to different audience segments. This complicates causal interpretation because results may reflect both ad content effectiveness and changes to audience composition. We offer three key contributions. First, we make clear that divergent delivery is specific to A/B tests and intentional, informing advertisers about ad performance in practice. Second, we measure divergent delivery at scale, considering 3,204 Lift tests and 181,890 A/B tests. Lift tests show no meaningful audience imbalance, confirming their causal validity, while A/B tests show clear imbalance, as expected. Third, we demonstrate that campaign configuration choices can reduce divergent delivery in A/B tests, lessening algorithmic influence on results. While no configuration guarantees eliminating divergent delivery entirely, we offer evidence-based guidance for those seeking more generalizable insights about ad content in A/B tests.

Suggested Citation

  • Gordon Burtch & Robert Moakler & Brett R. Gordon & Poppy Zhang & Shawndra Hill, 2025. "Characterizing and Minimizing Divergent Delivery in Meta Advertising Experiments," Papers 2508.21251, arXiv.org.
    • Handle: RePEc:arx:papers:2508.21251
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    References listed on IDEAS

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    1. 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.
    2. Brett R. Gordon & Robert Moakler & Florian Zettelmeyer, 2023. "Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement," Marketing Science, INFORMS, vol. 42(4), pages 768-793, July.
    3. Orazi, Davide C. & Johnston, Allen C., 2020. "Running field experiments using Facebook split test," Journal of Business Research, Elsevier, vol. 118(C), pages 189-198.
    4. Michael Braun & Bart de Langhe & Stefano Puntoni & Eric M Schwartz, 2024. "Leveraging Digital Advertising Platforms for Consumer Research," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 51(1), pages 119-128.
    5. Kosuke Imai & Gary King & Elizabeth A. Stuart, 2008. "Misunderstandings between experimentalists and observationalists about causal inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 171(2), pages 481-502, April.
    6. Ho, Daniel E. & Imai, Kosuke & King, Gary & Stuart, Elizabeth A., 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, Cambridge University Press, vol. 15(3), pages 199-236, July.
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