CRO has always been hard because the bottleneck was hypothesis generation, not test execution. AI flips that. Claude can generate 50 specific test hypotheses in 20 minutes — the hard part now is choosing which ones to actually run and interpreting results. Here is the workflow.
Traditional CRO bottleneck: a strategist studies the funnel, comes up with 3-5 testable hypotheses, prioritizes one, runs it for 4 weeks, repeats. Total cycle: ~2 months per test, 3-6 tests per year.
With AI: hypothesis generation compresses from days to hours. You can generate 50 hypotheses and choose the strongest 3 to test in parallel. Same test execution time, but with much higher hypothesis quality because you are choosing from many options rather than the first thing that came to mind.
1. Feed Claude the data. Page analytics (where users drop off), heatmap data (where they click), session recordings (what they do), funnel performance, current copy.
2. Generate hypothesis list. Use the prompt below. Claude generates 30-50 specific test hypotheses ranked by likely impact.
3. Prioritize and design tests. Pick the top 3-5 hypotheses you can actually test. Design the variants (Claude can help draft alternate copy).
4. Run and interpret. Execute tests in your testing tool (Optimizely, VWO, or even just A/B in your analytics). When results come in, Claude can help draft the analysis narrative.
Generate test hypotheses for our [PAGE TYPE] at [URL].
Current conversion rate: [X%]
Key drop-off points in the funnel: [LIST]
What users are clicking (heatmap data): [SUMMARY]
What session recordings reveal: [SUMMARY]
Our target persona: [SHORT]
The action we want them to take: [SPECIFIC]
Current page copy: [PASTE OR LINK]
Generate 30 specific test hypotheses. For each:
- The specific change we would test (be concrete, not "improve the headline")
- The hypothesis (why this might work)
- Expected impact (small / medium / large)
- Implementation effort (low / medium / high)
- Why this hypothesis (which user behavior signal points to it)
Then rank the top 5 by likely-impact-divided-by-effort.
Avoid generic hypotheses ("try a different button color"). Be specific to what the data is telling us. AI does not execute the tests. You still need a testing tool and traffic volume.
AI does not tell you which hypothesis is "right" before you test it. Most hypotheses fail. Be prepared.
AI cannot interpret tests with limited traffic. Statistical significance still requires sample size.
AI does not know your business context perfectly. Some hypotheses it generates will be wrong for reasons it cannot see. You filter.