marketing-cro

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.

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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

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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

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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.

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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.

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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.

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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

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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

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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?

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Core: In-App Monetization Gate Timing

Freemium and subscription apps must decide when to show an upgrade prompt (paywall, modal, soft gate). Getting this wrong is a silent activation killer.

The Rule

Never show a monetization gate before the user has received first value. A gate shown too early trains users to leave, not to pay.

Gate Trigger Patterns

Trigger	Mechanism	When to Use
Value-first	Show gate only after the user completes a core action (e.g., generates a report, sees results)	Default for all products
Scroll-based	Show gate after user scrolls past the first valuable content block	Content-heavy products, dashboards
Engagement timer	Show gate after 15-30s of active engagement (not wall-clock time)	Products with immediate visible value
Usage count	Show gate after N free uses (e.g., 3 questions, 5 exports)	Products with repeatable core actions
Feature boundary	Gate specific premium features; leave core experience free	Products with clear free/paid feature split

Timing Anti-Patterns

Anti-Pattern	Impact	Fix
Immediate gate on first visit	40-60% bounce before any value delivered	Defer until after first value moment
Timed gate < 5s	Users haven't oriented yet; feels like a trap	Minimum 15s active engagement OR scroll/action trigger
Gate before scroll	Blocks users from discovering content below fold	Use scroll-depth trigger (e.g., past first content section)
Full-screen blocker on free content	Punishes users for engaging	Use soft gates (banner, inline CTA) for free-tier content
Gate interrupting onboarding	Breaks first-run experience	Complete onboarding → deliver first value → then gate

Gate Placement Decision Tree

WHEN TO SHOW THE MONETIZATION GATE:

1. Has the user completed onboarding?
   └─ No → DO NOT gate. Let them finish.

2. Has the user seen their first valuable result?
   └─ No → DO NOT gate. Deliver value first.

3. Has the user had time to orient?
   (scrolled past first content block OR 15s+ active engagement)
   └─ No → DO NOT gate. Wait for engagement signal.

4. User has received value and engaged → GATE IS SAFE
   └─ Choose format:
      ├─ Soft gate (banner/inline) → for free-tier content the user can still access
      └─ Hard gate (modal/overlay) → for premium features the user cannot access

Measurement

Track the impact of gate timing on activation:

• gate_shown → gate_dismissed vs gate_converted (immediate)
• gate_shown → generated_reading or core value event within 7 days (downstream activation)
• Compare activation rates for cohorts who saw the gate at different points in their journey

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Reference: Triage, Speed, SOPs

For page speed targets, CRO triage decision tree, operating cadence, and anti-patterns, see references/triage-and-ops.md.

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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

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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.

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References

Reference	Description
advanced-testing.md	CUPED, sequential testing, MAB
ai-automation.md	AI personalization, tool stack
form-optimization.md	Field reduction, multi-step forms, validation UX
landing-page-optimization.md	Hero patterns, CTA placement, layout frameworks
mobile-cro.md	Thumb zones, tap targets, mobile checkout, speed
personalization-strategies.md	Dynamic content, behavioral targeting, tool comparison
social-proof-trust-signals.md	Testimonials, reviews, trust badges, B2B/B2C patterns
triage-and-ops.md	Page speed, triage, SOPs, anti-patterns
pricing-page-optimization.md	Pricing psychology, plan tiers, enterprise vs self-serve
checkout-optimization.md	Cart abandonment, payment UX, checkout flow design

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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.

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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

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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