CRO — CONVERSION OPTIMIZATION OS (OPERATIONAL)
Built as a no-fluff execution skill for systematic conversion rate optimization.
Structure: Core CRO fundamentals first. Advanced testing in dedicated sections. AI/ML optimization in clearly labeled "Optional: AI / Automation" sections.
Modern Best Practices (January 2026) Google Optimize sunset: Use VWO, Optimizely, or PostHog Statistical significance: https://www.evanmiller.org/ab-testing/ CXL Institute: https://cxl.com/ Baymard Institute UX: https://baymard.com/ Cookie deprecation + stricter privacy defaults: prefer first-party measurement, validate assignment/tracking, and treat lifts as uncertain without clean instrumentation When to Use This Skill Landing page optimization: Hero, CTA, proof, form optimization A/B testing: Hypothesis design, sample size, statistical significance Funnel analysis: Drop-off identification, micro-conversion mapping Form optimization: Field reduction, multi-step forms, friction removal Trust/credibility: Social proof, security signals, guarantees When NOT to Use Brand awareness campaigns → Use marketing-paid-advertising User research methodology → Use software-ux-research Product analytics setup → Use marketing-product-analytics SEO/organic traffic → Use marketing-seo-complete Expert: CRO Mental Model (Quick Calibration)
Use this to avoid local wins / global losses.
CRO: Increase the rate of valuable commitments (purchase, qualified lead, activation) while protecting business outcomes (revenue, margin, LTV, support load). UX optimization: Reduce friction/errors so users can do what they already intend; good UX does not guarantee better conversions. Funnel optimization: Optimize the system across steps and handoffs (traffic quality → intent match → page → form/checkout → sales/onboarding → retention). Experimentation: A causal learning method; not every decision belongs in a test.
Do not delegate these to A/B tests (even with infinite traffic): legal/compliance/ethics, dark patterns, misleading claims, and irreversible brand trust decisions.
Core: CRO Framework The CRO Process 1. ANALYZE → Identify conversion problems (data + qualitative) 2. HYPOTHESIZE → Form testable hypotheses 3. PRIORITIZE → Score by impact/effort (ICE/PIE) 4. TEST → Run A/B tests with statistical rigor 5. LEARN → Document results, iterate 6. IMPLEMENT → Roll out winners, test next
Conversion Rate Benchmarks Page Type Poor Average Good Great Landing page <1% 2-3% 4-5% >6% Checkout <40% 50-60% 65-75% >80% Form completion <20% 30-40% 45-55% >60% Add to cart <3% 5-8% 9-12% >15%
Note: Benchmarks vary significantly by industry. Use as directional only.
Core: Landing Page Optimization Above-the-Fold Checklist
Every landing page needs these elements visible without scrolling:
Element Requirement Common Issues Headline Clear value proposition Vague, company-focused Subheadline Specific benefit or outcome Missing or weak Hero image/video Relevant, shows outcome Stock photos, irrelevant CTA Prominent, action-oriented Hidden, generic text Trust signal Logo strip, rating, or stat Missing entirely Headline Formula [Outcome] + [Timeframe/Ease] + [Without Pain Point]
Examples: "Get 10 qualified leads per week without cold calling" "File your tax return in 15 minutes with expert review" "Double your email conversions without hiring a copywriter"
CTA Button Best Practices Do Don't "Start Free Trial" "Submit" "Get My Quote" "Click Here" "Book My Demo" "Learn More" (bottom of funnel) "Download the Guide" "Send"
CTA Button Optimization:
Size: Large enough to tap on mobile (min 44px height) Color: Contrasts with page background Position: Above fold AND after key sections Text: First person ("Get My...") often outperforms second person Whitespace: Use spacing to isolate the primary CTA from competing elements; treat big lift claims as case-dependent and verify in your context Trust Elements Hierarchy STRONGEST TRUST SIGNALS (use at least 3): ├─ Customer logos (recognizable brands) ├─ Review score (4.5+ stars with count) ├─ Security badges (SSL, payment, compliance) ├─ Money-back guarantee └─ Phone number visible
SUPPORTING TRUST SIGNALS: ├─ Customer testimonials (with photo, name, company) ├─ Case study snippets (specific metrics) ├─ "As seen in" media logos ├─ Team photos (for services) ├─ Live chat widget └─ Physical address (for services)
User-Generated Content (UGC)
UGC often increases conversions in SaaS and e-commerce, but lift magnitude varies widely by category, placement, and traffic intent.
UGC Type Placement Impact Customer videos Hero or below fold High trust, high engagement Review excerpts Near CTA Reduces uncertainty Case study quotes Consideration section Builds credibility Community mentions Footer or social proof bar Volume signal
Implementation: Pull from G2, Capterra, or in-app feedback. Verify permissions before use.
Core: Form Optimization Form Field Rules Rule Why Impact Minimum fields Every field adds friction Often lowers completion (magnitude varies) Email first Captures partial submissions +15-30% lead capture Persistent labels Placeholders disappear, cause errors +10% completion Single column Easier flow +5-10% completion Inline validation Catch errors early +22% completion Browser autofill Reduces typing, fewer errors +15-20% completion
2026 Benchmark: Average checkout = 5.1 steps, 11.3 fields (Baymard). Target ≤5 fields for lead gen.
Field Priority (Ask Only What You Need) Priority Field When Required 1 Email Always 2 Name If personalization needed 3 Company B2B only 4 Phone Sales-ready leads only 5 Job title Enterprise targeting 6+ Everything else Gate behind progressive profiling Multi-Step Form Pattern Step 1: Low commitment (email) ├─ "What's your email?" ├─ Progress indicator: 1 of 3 └─ CTA: "Continue"
Step 2: Qualifying info ├─ Company size / Industry ├─ Progress indicator: 2 of 3 └─ CTA: "Almost there"
Step 3: Contact info ├─ Name / Phone (optional) ├─ Progress indicator: 3 of 3 └─ CTA: "Get My [Deliverable]"
Multi-step benefits:
Commitment and consistency principle Captures partial data (even if abandoned) Feels less overwhelming Can qualify leads progressively Core: A/B Testing Methodology Hypothesis Template IF we [change/add/remove X] THEN [metric] will [increase/decrease] by [estimate] BECAUSE [reasoning based on data/research]
Example: IF we add customer logos to the hero section THEN form conversion will increase by 15% BECAUSE trust signals reduce perceived risk for new visitors
Sample Size Calculator
Minimum sample size formula (simplified):
n = (16 × p × (1-p)) / MDE²
Where: - n = sample per variant - p = baseline conversion rate - MDE = minimum detectable effect (e.g., 0.10 for 10% lift)
Example: Baseline CVR: 3% (0.03) MDE: 20% relative lift (looking for 3.6% or higher)
n = (16 × 0.03 × 0.97) / (0.006)² n ≈ 12,933 per variant
Total traffic needed: ~26,000 visitors
Quick reference:
Baseline CVR 10% MDE 20% MDE 30% MDE 1% 63,000 15,800 7,000 3% 20,700 5,200 2,300 5% 12,200 3,050 1,350 10% 5,800 1,450 650
Per variant. Multiply by 2 for total traffic needed.
Statistical Significance
Requirements for valid test:
95% confidence level (minimum) 80% power (default) unless you have a reason to change it Run for at least 1-2 full business cycles (7-14 days) Don't peek and stop early (increases false positives) Document before test: hypothesis, primary metric, guardrails, sample size, duration Avoid post-hoc slicing; pre-register segments or adjust for multiple comparisons
Reality check (expert defaults):
Statistical significance does not mean the change is worth shipping (check practical impact + guardrails) Ignore "significant" results when experiment integrity is in doubt (tracking issues, traffic mix shifts, SRM, broken randomization) Stop early only for clear harm (guardrail breaches) or invalidity (instrumentation/assignment problems), not for "early wins" Experiment Integrity (2026 Default Checks) Assignment sanity: A/A test periodically; check SRM on day 1 and day 3 Tracking sanity: confirm event definitions, dedupe, cross-domain, and consent-mode behavior before interpreting results Contamination: avoid showing multiple variants to the same user across devices/sessions; prefer stable IDs when possible Change control: freeze other major changes to the same flow during the test window CUPED: Faster Tests via Variance Reduction
CUPED (Controlled-experiment Using Pre-Existing Data) can reduce variance by ~40-60%, allowing tests to reach significance faster.
Aspect Details How it works Uses pre-experiment user behavior to control for inherent variance Lookback window 1-2 weeks (optimal balance) Limitation Doesn't work for new users (no history) Platforms VWO, Optimizely, Statsig, Eppo, PostHog
When to use: High-traffic sites where test velocity matters. See advanced-testing.md for implementation details.
Test Prioritization: ICE Framework Factor Score (1-10) Description Impact How much will this move the metric? Confidence How sure are we this will work? Ease How easy is this to implement? ICE Score (Impact + Confidence + Ease) / 3
ICE Score interpretation:
8-10: High priority, test immediately 5-7: Medium priority, add to queue 1-4: Low priority, revisit later or skip Core: Funnel Analysis Funnel Diagnostic Framework STEP 1: Map your funnel Page Visit → Key Action → Form Start → Form Complete → Confirmation
STEP 2: Measure drop-off at each step ├─ Page Visit to Key Action: % (bounce rate inverse) ├─ Key Action to Form Start: % ├─ Form Start to Complete: % └─ Complete to Confirmation: %
STEP 3: Identify biggest drop-off Biggest percentage drop = highest priority to fix
STEP 4: Diagnose root cause ├─ High bounce? → Relevance, load speed, messaging ├─ Low engagement? → Content, CTA visibility ├─ Form abandonment? → Form friction, trust └─ Checkout drop? → Pricing, shipping, trust
Expert note: The "biggest drop-off" is not always the best target. Confirm it's a defect (not intentional filtering), not a measurement artifact, and not caused upstream (traffic quality / offer mismatch).
Micro-Conversion Mapping Funnel Stage Micro-Conversions to Track Awareness Scroll depth, time on page, video views Interest CTA hover, tab/section views, resource clicks Consideration Pricing page visit, comparison page, demo video Decision Form start, add to cart, checkout start Conversion Form complete, purchase, signup Heatmap & Recording Analysis
What to look for:
Click heatmaps: Are users clicking CTAs? Clicking non-clickable elements? Scroll maps: Where do users stop scrolling? Key content below fold? Session recordings: Where do users hesitate? Rage clicks? Form confusion? Form analytics: Which fields cause abandonment? Error patterns? Reference: Triage, Speed, SOPs
For page speed targets, CRO triage decision tree, operating cadence, and anti-patterns, see references/triage-and-ops.md.
Templates Template Purpose landing-audit.md Full landing page audit ab-test-plan.md A/B test planning form-audit.md Form optimization checklist funnel-analysis.md Funnel diagnostic ice-scoring.md Test prioritization Expert: Hypothesis Quality (Silent Failure Checklist)
A good CRO hypothesis is not "change X to raise CVR." It must specify mechanism and risk.
Strong hypothesis includes:
Which constraint it targets: clarity, trust, motivation, friction Who it's for: segment/intent/channel/device (at least one) What moves: primary metric + guardrails (value, quality, downstream) Why it should work: evidence + mechanism (not vibes)
How CRO fails silently (common):
Conversions go up but value goes down (lower-quality leads, higher refunds/chargebacks, worse retention) Overall looks flat but a high-value segment is harmed (mix effects hide damage) "Win" is novelty or seasonality; it doesn't repeat
Use assets/ab-test-plan.md to pre-register guardrails and invalidation criteria.
References Reference Description advanced-testing.md CUPED, sequential testing, MAB ai-automation.md AI personalization, tool stack triage-and-ops.md Page speed, triage, SOPs, anti-patterns International Markets
This skill uses US/UK defaults. For international CRO:
Need See Skill Regional payment methods marketing-geo-localization Cultural trust signals marketing-geo-localization Regional CTA adaptation marketing-geo-localization RTL/localized design marketing-geo-localization
Auto-triggers: When your query mentions regional markets or cultural adaptation, both skills load automatically.
Related Skills marketing-geo-localization — International markets, cultural CRO marketing-leads-generation — Lead capture strategies marketing-paid-advertising — Traffic sources marketing-seo-complete — Page speed, Core Web Vitals software-ui-ux-design — Design patterns software-ux-research — User research methods Usage Notes (Claude) Stay operational: return checklists, audit results, test plans Always include statistical significance requirements for testing Recommend qualitative research for low-traffic sites Use benchmark ranges, not absolute numbers Do not invent conversion data; state "varies by industry" when uncertain