A/B
Testing

We mine your analytics, heatmaps, session recordings, and user feedback to find friction points. Each finding becomes a written hypothesis with a predicted outcome and a priority score.

We calculate the required sample size and duration up front so the test can actually reach significance. Success metrics, guardrail metrics, and audience segments are defined before a single visitor sees a variant.

Variants are built to load without flicker and behave identically across devices and browsers. We QA tracking before launch so the data you get is data you can trust.

The experiment runs untouched until it reaches the planned sample size — no peeking, no premature stops. We monitor guardrail metrics daily to catch any negative side effects early.

We analyze results overall and by segment, separating genuine lifts from statistical noise. Every test ends with a clear decision: roll out, iterate, or archive with documented learnings.

Winning variants are implemented permanently in your codebase, not left running in the testing tool. Learnings feed the hypothesis backlog, and the next experiment starts immediately.