My Approach to Test & Learn with GA4 and A/B Tasty
A repeatable experimentation loop that turns a clear hypothesis into evidence, a decision and the next useful question.
Every test should reduce uncertainty.
I use A/B Tasty to control the experience and GA4 to understand behavior around the result. A winning variant matters, but knowing why it wins creates the reusable learning.
From assumption to documented learning.
Write a falsifiable hypothesis
I define the audience, proposed change, expected behavior and primary success metric before building anything.
Output: hypothesis cardControl the experience. Explain the behavior.
A/B Tasty
Audience allocation, variants, exposure and experiment governance.
- Build controlled variants
- Define goals and targeting
- Monitor allocation integrity
GA4
Journey context, audience behavior and downstream business impact.
- Analyze engagement signals
- Compare segments and journeys
- Read effects beyond the primary KPI
Four guardrails protect the decision.
One primary metric
The result is judged against the KPI selected before launch.
Stable exposure
Traffic allocation, device mix and audience eligibility remain controlled.
Enough evidence
Sample size, duration and external effects are reviewed together.
No hidden damage
Secondary metrics reveal whether a local gain harms the wider journey.
Slots for the test story.
Ship, iterate or stop are all useful outcomes.
The evidence supports implementation and continued monitoring.
The signal is promising, but the hypothesis or execution needs refinement.
The test protects the team from investing in an unsupported assumption.