Webinars
Meta Ad Testing Demystified: Divergent Delivery and What It Means for Your Results
Meta’s Lift and A/B tests are widely used to evaluate campaign performance, but they answer fundamentally different questions. Lift tests estimate true incrementality using a no-ad control, while A/B tests compare campaign variants without a control group.
A key challenge in A/B testing is “divergent delivery,” where Meta’s algorithms distribute each variant to different audience segments. This means observed performance differences may reflect both creative effectiveness and who saw the ads.
Drawing on large-scale evidence from thousands of Lift and A/B tests, this webinar shows when and why divergent delivery occurs, why it can be both informative and misleading, and how it compares to Lift test results. You’ll also learn practical ways to reduce imbalance—through campaign setup choices like targeting, budgets, bidding, and placements—to better isolate creative impact when that’s the goal.
speakers
Brett R. Gordon is the Charles H. Kellstadt Professor of Marketing at Northwestern University's Kellogg School of Management. His research interests include pricing, advertising, and promotions, which he studies using a diverse set of methods from causal inference, machine learning, and empirical industrial organization. He often collaborates with firms to help them measure and design more effective marketing strategies. He currently serves as Co-Editor at Journal of Marketing Research and co-hosts the How I Wrote This podcast, which helps demystify how great marketing papers came to be. Before joining Kellogg, he held faculty positions at Columbia Business School and visiting positions at Chicago Booth and Stanford GSB. He earned his Ph.D. in economics from Carnegie Mellon.
Robert Moakler is a Research Scientist at Meta where he works on projects related to privacy, causal inference methodologies, and the impact of cross-channel marketing campaigns. He received his Ph.D. in Information Systems from the NYU Stern School of Business in 2017. While attending NYU, Robert worked at Integral Ad Science where he did applied data science research that developed methods for causal inference using large-scale digital data with a focus on advertising.
I am a Professor of Information Systems, and Fellow of the Digital Business Institute at Boston University’s Questrom School of Business. My research, which focuses on the economic evaluation of information systems, employs empirical analyses rooted in econometrics and field experimentation to identify and quantify the drivers of individual participation in online social contexts. My work has been published in various leading journals, including Management Science, Information Systems Research, MIS Quarterly, Manufacturing & Service Operations Management, Organization Science, Production and Operations Management, the Journal of Law, Economics & Organization, and the Journal of Consumer Psychology. I am a recipient of both the AIS Early Career Award (2017) and the INFORMS ISS Sandra A. Slaughter Early Career Award (2017). I am a recipient of the INFORMS ISR and ISS best paper award (2014). My research has been supported by more than $2 million in grants from various corporate, non-profit and government organizations, including the NSF, Ewing Marion Kauffman Foundation, the 3M Foundation, Adobe, Facebook Research and the European Commission. My work and opinions have been cited by numerous outlets in the popular press, including The New York Times, the Wall Street Journal, NPR, Time Magazine, Forbes, Vice, Wired, the LA Times, Pacific Standard and PC Magazine. I am a recipient of both the Best Reviewer and Best Associate Editor Awards from Information Systems Research. I presently serve as an Associate Editor for two INFORMS journals: Management Science and ISR. I have previously served as track chair and associate editor for the International Conference on Information Systems (ICIS), twice as co-chair of the symposium on Statistical Challenges in eCommerce Research (SCECR), and twice as co-chair of the Workshop on Information Systems and Economics (WISE). Prior to entering academia, I was employed as an information systems auditor, a hardware design engineer, and most recently as a technology consultant with Accenture Canada in Toronto. I teach graduate courses on data analytics and IT management. I hold a Bachelor of Engineering and a Master of Business Administration from McMaster University, as well as a PhD in Business Administration from Temple University’s Fox School of Business.
Poppy Zhang, currently a Senior Research Scientist at Meta focusing on content understanding and ads creative optimization with LLM. Holds PhD in Quantitative Marketing from NYU Stern.