Optimization

Funnel A/B Testing: How to Run Useful Experiments

A practical guide to funnel experimentation, including hypotheses, variants, metrics, interpretation, and common testing mistakes.

Useful framework
Understand the goal
Map the journey
Form a hypothesis
Measure what happens

Test a reason, not a whim

An experiment should begin with evidence or a clear hypothesis. Random changes produce random learning.

Choose one primary metric

Define the outcome that determines whether the change helped. Secondary metrics can explain the result but should not replace the main decision.

Change something meaningful

Test elements that can plausibly alter understanding, motivation, trust, or friction. Tiny cosmetic changes are often lower priority.

Respect sample uncertainty

Small samples can move dramatically by chance. Avoid treating an early swing as proof and consider whether the result is large and stable enough to act on.

Document the result

Record what changed, why, how long the test ran, the outcome, and what you learned. Failed hypotheses can still improve future decisions.

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