conversion-rate-optimization

Conversion Rate Optimization Skill

CRO is not "make the button green" — it's systematically removing the friction and doubt between a

visitor and the action. This skill audits a page against conversion heuristics, diagnoses the biggest

blockers, and turns them into prioritised, properly-powered tests — so you change conversion on purpose,

with evidence, not by redesign-by-opinion.

Required Inputs

Ask for these only if they aren't already provided:

• The page/step & its one goal — the single action it should drive (signup, purchase, demo).
• Current performance — conversion rate and traffic volume (volume decides whether A/B testing is even viable).
• The audience & their intent — where they come from and how warm they are.
• Known data — analytics, session recordings, or survey signals on where people drop or hesitate.

Output Format

CRO Plan: [page/step]

1. Conversion audit — score the page against the core heuristics, each with the specific issue found:

• Clarity — is the value proposition and next action instantly obvious?
• Relevance — does it match the source/ad/intent that brought them?
• Motivation — are benefits and proof (social proof, results) present at the decision point?
• Friction — form length, steps, load speed, cognitive load.
• Anxiety — trust signals, risk reversal (guarantee, "no card needed"), privacy.
• Distraction — competing CTAs and links pulling away from the one goal.

2. Diagnosis — the top 2–3 conversion blockers, ranked by likely impact (grounded in the data, not taste).

3. Test backlog — each blocker as a hypothesis, scored (ICE):

| Hypothesis ("If we ___, conversion will ___ because ___") | Heuristic | Impact | Confidence | Ease | ICE |

|---|---|---|---|---|---|

4. Test designs (top 2–3) — the variant, primary metric + guardrails (e.g. don't lift signups while tanking paid conversion), and the sample size & duration needed to detect the expected lift. If traffic is too low for A/B significance, say so and recommend sequential/qualitative methods instead.

5. Measurement — how it's tracked, the significance threshold set before running, and the decision rule (ship / iterate / revert).

Quality Checks

• [ ] The audit cites a specific issue per heuristic, not a generic checklist tick
• [ ] Test ideas are hypotheses tied to a diagnosed blocker, prioritised by ICE
• [ ] Each test states the sample size/duration to detect the expected lift
• [ ] Low-traffic reality is acknowledged — A/B testing is only recommended when volume supports it
• [ ] Guardrail metrics prevent a local conversion win that harms downstream value

Anti-Patterns

• [ ] Do not test trivial cosmetics (button colour) before fixing clarity, friction, and anxiety — the big levers
• [ ] Do not A/B test on traffic too low to ever reach significance — use qualitative research or sequential changes instead
• [ ] Do not optimise the step in isolation — a signup lift that lowers paid conversion is a loss; watch the downstream metric
• [ ] Do not call a test on day two because it looks good — set the threshold and sample size before you start
• [ ] Do not redesign by opinion — every change should trace to a diagnosed blocker and a hypothesis

Based On

Conversion-optimization heuristics (clarity / relevance / motivation / friction / anxiety / distraction — LIFT-style) and properly-powered A/B testing.