What it does
Analyzes funnel metrics, heatmaps, and qualitative feedback to propose prioritized A/B test ideas complete with hypothesis, expected impact, and measurement plan.
Why I recommend it
Conversion teams spend hours brainstorming. Automation surfaces data-backed experiments so you can focus on execution.
Expected benefits
- Full backlog of prioritized tests
- Ideas tied to actual data, not guesses
- Faster experimentation cadence
- Shared visibility for growth and product teams
How it works
Weekly ingestion of analytics (GA, Mixpanel), session recordings, survey data -> Claude identifies biggest drop-offs and patterns -> outputs hypothesis statements, variant ideas, and KPIs -> syncs to testing backlog in Airtable/Notion for grooming.
Quick start
Provide Claude with a screenshot of your funnel report plus key observations and ask for three hypotheses. Validate usefulness before wiring data feeds.
Level-up version
Score ideas by effort vs impact, tag owners, auto-generate experiment briefs, and loop results back to improve future recommendations.
Tools you can use
Analytics: GA4, Mixpanel, Amplitude
Session replay: Hotjar, FullStory
Experiment tracking: Airtable, GrowthBook
AI: Claude, ChatGPT
Also works with
Email/testing platforms like Optimizely, VWO, Convert.
Technical implementation solution
- No-code: Export funnel CSV -> Zapier -> Claude prompt -> append to Notion database with fields.
- API-based: Data warehouse query -> Python summarises -> Claude generates hypotheses -> pushes to Jira product backlog.
Where it gets tricky
Feeding clean structured data, avoiding duplicate ideas, and ensuring experiments align with product roadmap priorities.
