AI Growth Experiments

AI makes it easy to generate campaign ideas. The harder part is deciding which uncertainty deserves a test and what result would justify a change.

A growth experiment is a structured attempt to learn about a proposed improvement. It needs a hypothesis, an appropriate design, reliable measurement, and a decision rule.

Start with the uncertain mechanism

For a fictional scheduling product, suppose research suggests that suitable visitors are unsure how much setup a trial requires.

A useful hypothesis is: explaining the first setup step on the landing page will increase the proportion of eligible visitors who create a trial and complete that step, without worsening the quality of those trials.

This names a possible reason, a change, and an outcome. Make the page more persuasive is too vague to evaluate.

Choose a primary metric

Decide what counts as success before seeing the data. A click may be an intermediate behavior rather than the business outcome.

For the example, the primary metric might be the proportion of eligible assigned visitors who create a trial and complete first setup within a defined window. Document how visitors and accounts are linked.

Choose a guardrail for an important downside, such as a downstream failure or increased support burden. It should have a reason, not be added merely to make the plan look comprehensive.

Define who enters the test

Specify the eligible audience, exclusions, assignment unit, and period. If using random assignment, keep that assignment consistent with the analysis.

Avoid comparing a new version's mobile visitors with an old version's desktop visitors and calling the result an A/B test. Audience differences can be confused with the treatment.

Where a randomized test is unsuitable, choose another learning method and label it accurately. A small usability study can reveal misunderstanding without estimating a conversion lift.

Write the decision rule in advance

Define the analysis method, relevant uncertainty, and a meaningful effect before launching. Determine the observation needs using the baseline, variability, and effect you need to detect.

Do not invent a universal rule such as run every test for seven days. The required evidence depends on traffic, outcome timing, and design.

Plan how you will handle a broken test, an important adverse signal, or an inconclusive result. Repeatedly checking results and stopping when a favorable number appears can invalidate a naive analysis.

Use AI to improve the brief

Ask AI to identify ambiguous definitions, missing assumptions, alternative explanations, and ways tracking could fail.

Then review those suggestions. A model can help expose a gap without knowing your actual event implementation or business constraints.

Do not let it manufacture a baseline or claim a sample-size calculation it has not performed with an appropriate method.