Where A/B Testing Goes Wrong: How Divergent Delivery Affects What Online Experiments Cannot (and Can) Tell You About How Customers Respond to Advertising

Marketers use online advertising platforms to compare user responses to different ad content. But platforms’ experimentation tools deliver different ads to distinct and undetectably optimized mixes of users that vary across ads, even during the test. Because expo­sure to ads in the test is nonrandom, the estimated comparisons confound the effect of the ad content with the effect of algorithmic targeting. This means that experimenters may not be learning what they think they are learning from ad A/B tests. The authors document these “divergent delivery” patterns during an online experiment for the first time. They explain how algorithmic targeting, user heterogeneity, and data aggregation conspire to confound the magnitude, and even the sign, of ad A/B test results. Analytically, the authors extend the potential outcomes model of causal inference to treat random assignment of ads and user exposure to ads as separate experimental design elements. Managerially, the authors explain why platforms lack incentives to allow experimenters to untangle the effects of ad content from proprietary algorithmic selection of users when running A/B tests. Given that experimenters have diverse reasons for comparing user responses to ads, the au­thors offer tailored prescriptive guidance to experimenters based on their specific goals.