About

Brett Gordon is the Charles H. Kellstadt Professor of Marketing at Northwestern University's Kellogg School of Management. His research focuses on pricing, advertising, promotions, retailing, and experimentation, utilizing methods from causal inference, machine learning, and empirical industrial organization. He partners with companies to measure and enhance marketing effectiveness, with recent work examining digital advertising measurement methods and developing new evaluation approaches. His research has been published in leading journals including the American Economic Review, Journal of Political Economy, Marketing Science, Management Science, and the Journal of Marketing Research. Gordon has received multiple awards for his research, including the John D. C. Little Award and the Robert D. Buzzell Best Paper Award, and serves as Co-Editor at the Journal of Marketing Research. He has held academic positions at Columbia Business School, University of Chicago's Booth School of Business, and Stanford GSB, and has been recognized for his contributions to marketing scholarship and education.

Research topics

• Computer Science
• Political Science
• Business
• Advertising
• World Wide Web
• Marketing
• Psychology
• Econometrics
• Geography
• Economics
• Data science
• Social psychology

Selected publications

• Predicted Incrementality by Experimentation (PIE) for Ad Measurement

  SSRN Electronic Journal · 2026-01-01 · 2 citations

  preprintOpen access1st authorCorresponding
  PublisherDOI

• Characterizing and Minimizing Divergent Delivery in Meta Advertising Experiments

  ArXiv.org · 2025-08-28

  preprintOpen access

  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.

  PublisherOA PDFDOI

• Multicell Experiments for Marginal Treatment Effect Estimation of Digital Ads

  Management Science · 2025-01-15 · 1 citations

  articleSenior author

  Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance extant empirical approaches may not produce the estimands a decision maker needs to solve the problem of interest. For example, these experimental designs are common in digital advertising settings but typical methods do not yield effects that inform the intensive margin: how many consumers should be reached or how much should be spent on a campaign. We propose a solution that combines a novel multicell experimental design with modern estimation techniques that enables decision makers to solve problems with an intensive margin. Our design is straightforward to implement and does not require additional budget. We illustrate our method through simulations calibrated using an advertising experiment at Facebook, demonstrating its superior performance in various scenarios and its advantage over direct optimization approaches. This paper was accepted by Jean-Pierre Dubé, marketing. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.01185 .

  PublisherDOI

• Personalization and targeting: how to experiment, learn & optimize

  International Journal of Research in Marketing · 2025-07-01 · 5 citations

  articleOpen access

  Personalization has become the heartbeat of modern marketing. The rapid expansion of individual-level data, the proliferation of personalized communication channels, and advancements in experimentation have fundamentally reshaped how firms tailor their marketing strategies. Furthermore, causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic overview of these developments. We formalize personalization as a causal inference problem embedded in the test and learn framework. We review key challenges and solutions that arise when personalization is approached through causal inference, including data limitations, treatment effect heterogeneity, policy evaluation, and ethical considerations. Finally, we identify emerging research trends stemming from new methodologies such as generic and double machine learning, direct policy learning, foundation models, and generative AI.

  PublisherDOI

• Amazon Ads Multi-Touch Attribution

  ArXiv.org · 2025-08-11

  preprintOpen access

  Amazon's new Multi-Touch Attribution (MTA) solution allows advertisers to measure how each touchpoint across the marketing funnel contributes to a conversion. This gives advertisers a more comprehensive view of their Amazon Ads performance across objectives when multiple ads influence shopping decisions. Amazon MTA uses a combination of randomized controlled trials (RCTs) and machine learning (ML) models to allocate credit for Amazon conversions across Amazon Ads touchpoints in proportion to their value, i.e., their likely contribution to shopping decisions. ML models trained purely on observational data are easy to scale and can yield precise predictions, but the models might produce biased estimates of ad effects. RCTs yield unbiased ad effects but can be noisy. Our MTA methodology combines experiments, ML models, and Amazon's shopping signals in a thoughtful manner to inform attribution credit allocation.

  PublisherOA PDFDOI

• Personalization and Targeting: How to Experiment, Learn & Optimize

  SSRN Electronic Journal · 2024-01-01 · 3 citations

  preprintOpen access
  PublisherDOI

• Multicell experiments for marginal treatment effect estimation of digital ads

  arXiv (Cornell University) · 2023-02-27

  preprintOpen accessSenior author

  Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance extant empirical approaches may not produce the estimands a decision maker needs to solve the problem of interest. For example, these experimental designs are common in digital advertising settings but typical methods do not yield effects that inform the intensive margin: how many consumers should be reached or how much should be spent on a campaign. We propose a solution that combines a novel multicell experimental design with modern estimation techniques that enables decision makers to solve problems with an intensive margin. Our design is straightforward to implement and does not require additional budget. We illustrate our method through simulations calibrated using an advertising experiment at Facebook, demonstrating its superior performance in various scenarios and its advantage over direct optimization approaches.

  PublisherOA PDFDOI

• Disentangling the Effects of Ad Tone on Voter Turnout and Candidate Choice in Presidential Elections

  Management Science · 2022 · 14 citations

  1st authorCorresponding
  • Political Science
  • Computer Science
  • Econometrics

  We study the effects of positive and negative advertising in presidential elections. We develop a model to disentangle these effects on voter turnout and candidate choice. The central empirical challenges are highly correlated and endogenous advertising quantities that are measured with error. To address these challenges, we construct a large set of potential instruments, including interactions with incumbency that we demonstrate provide the critical identifying variation, and apply machine-learning causal inference methods. Using data from the 2000 and 2004 U.S. presidential elections, we find that positive and negative ads play fundamentally different roles. Negative ads are more effective at driving relative candidate shares, whereas positive ads stimulate turnout. These results indicate that a candidate geographically targeting tone trades off local relative share gains and local increases in turnout for localities with a strong base. Counterfactual simulations, where the candidates adjust the quantity of positive and negative advertising while budgets remain fixed, indicate that ad tone alone can impact the outcome of close elections. Our analysis also provides potential explanations as to why past studies have produced mixed findings on both ad-tone and turnout effects. This paper was accepted by Matthew Shum, marketing. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2022.4347 .

  PublisherDOI

• Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement

  arXiv (Cornell University) · 2022-01-18 · 25 citations

  preprintOpen access1st authorCorresponding

  Despite their popularity, randomized controlled trials (RCTs) are not always available for the purposes of advertising measurement. Non-experimental data is thus required. However, Facebook and other ad platforms use complex and evolving processes to select ads for users. Therefore, successful non-experimental approaches need to "undo" this selection. We analyze 663 large-scale experiments at Facebook to investigate whether this is possible with the data typically logged at large ad platforms. With access to over 5,000 user-level features, these data are richer than what most advertisers or their measurement partners can access. We investigate how accurately two non-experimental methods -- double/debiased machine learning (DML) and stratified propensity score matching (SPSM) -- can recover the experimental effects. Although DML performs better than SPSM, neither method performs well, even using flexible deep learning models to implement the propensity and outcome models. The median RCT lifts are 29%, 18%, and 5% for the upper, middle, and lower funnel outcomes, respectively. Using DML (SPSM), the median lift by funnel is 83% (173%), 58% (176%), and 24% (64%), respectively, indicating significant relative measurement errors. We further characterize the circumstances under which each method performs comparatively better. Overall, despite having access to large-scale experiments and rich user-level data, we are unable to reliably estimate an ad campaign's causal effect.

  PublisherOA PDFDOI

• Close Enough? A Large-Scale Exploration of Non-Experimental Approaches to Advertising Measurement

  Marketing Science · 2022 · 80 citations

  1st authorCorresponding
  • Computer Science
  • Advertising
  • Computer Science

  A large-scale comparison of experimental advertising effects and those obtained using two state-of-the-art methods.

  PublisherDOI

Frequent coauthors

• Dennis Epple

  10 shared

• Ronald L. Goettler

  University of Rochester

  8 shared

• Wesley R. Hartmann

  Stanford University

  8 shared

• Florian Zettelmeyer

  7 shared

• Holger Sieg

  7 shared

• Kenneth C. Wilbur

  5 shared

• Jiwoong Shin

  5 shared

• Sridhar Narayanan

  5 shared

Awards & honors

• John D.C. Little Best Paper Award, INFORMS Society for Marke…
• Robert D. Buzzell Best Paper Award from the Marketing Scienc…
• Runner-Up for the Dick Wittink Prize at Quantitative Marketi…
• Ned Smith Research Mentorship Award
• Sidney J. Levy Teaching Award