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skills/mohitagw15856/pm-claude-skills/conversion-rate-optimization

conversion-rate-optimization

1
mohitagw15856/pm-claude-skills·Audit passed·Snapshot d5982bf10f71

Summary

This source did not publish a separate summary. Review SKILL.md before using the skill.

SKILL.md

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 ___")HeuristicImpactConfidenceEaseICE

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

sample size & duration

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.

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