7 Conversion Levers
A diagnostic model for any page that isn't converting — friction, clarity, proof, urgency, value, trust, and the one lever most teams forget to pull.
Hypotheses you can actually disprove.
Write test hypotheses that are falsifiable, prioritized, and tied to a real metric — a four-field structure that turns I think we should try into a test worth running.
Most “tests” are just opinions with a coin flip attached. A real hypothesis commits to a prediction specific enough to be wrong — and that’s exactly what makes the result worth something.
This framework structures every test the same way, so a backlog of vague ideas becomes a ranked queue of experiments you can defend to anyone who asks why you’re spending traffic on it.
Each hypothesis fills four fields: the observation (what the data shows), the change (what you’ll do about it), the predicted effect (on which metric, by roughly how much), and the reasoning (why you expect it).
If you can’t fill the predicted-effect field with a real metric, the test isn’t ready. The structure forces that rigor up front, then prioritizes the queue by expected impact against effort.
New templates, frameworks, and SOPs ship regularly — built from real growth work and genericized to use today.