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Plan and implement A/B tests, multivariate experiments, and growth programs. Covers hypothesis formation, sample sizing, instrumentation, and results analysis.
Marketing use cases
Hypothesis Creation
Turn a vague "I think we should change X" into a structured hypothesis with control, variant, metric, and expected lift.
Sample Size Calculation
Calculate traffic and time required to reach statistical significance at your desired confidence level and MDE.
Test Instrumentation
Set up the analytics events required to measure your test accurately in GA4, Mixpanel, or your analytics stack.
Results Analysis
Interpret p-values, confidence intervals, and segment-level breakdowns to determine a clear winner.
Experiment Roadmap
Build a prioritised backlog of A/B test ideas ranked by expected impact, effort, and traffic requirements.
Test Documentation
Create structured test records capturing hypothesis, results, learnings, and recommended next experiments.
Example prompts
Test plan
Help me design an A/B test for our [page/element]. I want to test [change] because I believe it will [outcome]. What's the right setup?
Sample size
We get [X] visits/month to this page. How long do I need to run this test to reach 95% statistical significance with a 10% MDE?
Results read
My A/B test ran for 3 weeks. Variant got [X] conversions from [Y] visitors. Control got [A] from [B]. Is this a winner?
Test backlog
Based on our landing page [URL or description], give me 10 A/B test ideas ranked by likely impact, from easiest to hardest.