Before launching, we documented historical adoption patterns and external shocks—holidays, policy changes, competitor releases. During the rollout, we compared participating regions with matched controls, adjusting for confounders. Sensitivity analyses helped us avoid overclaiming. When a spike aligned with unrelated events, we documented it, learned, and rebalanced expectations for the next wave.
Pure randomization isn’t always feasible, so we use pragmatic designs. Holdout groups preserve comparison, stepped-wedge schedules phase in exposure ethically, and difference-in-differences controls for macro trends. Documenting assumptions and pre-registering analysis plans reduced disputes later, letting teams focus on learning, iteration, and practical decisions about where to invest scarce resources next.
All Rights Reserved.