conversion-rate-optimization
GitHub审计落地页或漏斗步骤，生成优先级的CRO测试计划。通过启发式审计诊断摩擦点，输出ICE评分的测试假设、带样本量计算的实验设计及测量护栏，确保基于证据优化转化率。
Trigger Scenarios
Install
npx skills add mohitagw15856/pm-claude-skills --skill conversion-rate-optimization -g -y
SKILL.md
Frontmatter
{
"name": "conversion-rate-optimization",
"description": "Audit a landing page or funnel step and produce a prioritised CRO test plan. Use when asked to improve conversion rate, audit a landing\/signup\/checkout page, reduce funnel drop-off, or plan A\/B tests for a page. Produces a CRO plan — a heuristic conversion audit, the diagnosed friction, prioritised test hypotheses (ICE), test designs with sample-size math, and the measurement guardrails."
}
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.
Version History
- a38bc30 Current 2026-07-05 11:20
