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.
Retail media networks and commerce media are redefining the data and measurement landscape, creating new opportunities—and complexity—for marketers and researchers alike. At Commerce & Shopper Intelligence 2026, brands, retailers, and researchers revealed how they’re adapting methodologies and frameworks to better understand increasingly fluid, data-rich shopping journeys.
Member Only Access
Random controlled trials (RCTs) can assess causal effects of marketing but are expensive and incur opportunity costs by excluding control groups. Predicted Incrementality by Experimentation (PIE) uses samples of RCT-run campaigns to determine which characteristics map to causal outcomes and then applies that mapping to campaigns not run as RCTs.
