| name | conversion-rate-optimizer |
| description | Systematic CRO methodology covering conversion audits, hypothesis generation, A/B and multivariate testing, heatmap and session recording analysis, user research techniques, landing page optimization, funnel analysis, and statistical significance for data-driven growth. Use when the user asks about conversion rate optimizer or needs help with related topics. Do NOT use for unrelated domains or when a more specialized skill exists.
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| license | Apache-2.0 |
| metadata | {"author":"foundry-skills","version":"1.0.0","tags":"marketing seo analysis","category":"marketing-sales","subcategory":"marketing","depends":"","disclaimer":"none","difficulty":"intermediate"} |
Conversion Rate Optimizer
When to Use
Process
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Gather requirements. Ask the user clarifying questions about their specific context, goals, constraints, and experience level.
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Analyze the situation. Review the information provided and identify key factors, challenges, and opportunities relevant to conversion rate optimizer.
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Develop the framework. Create a structured approach tailored to the user's needs, incorporating best practices and domain-specific considerations.
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Deliver actionable output. Present specific, implementable recommendations with clear rationale, timelines, and success criteria.
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Address edge cases. Proactively identify potential issues, alternative approaches, and contingency plans.
Use this skill when:
- User needs guidance on conversion rate optimizer
- User asks about conversion rate optimizer best practices or techniques
- User wants a structured approach to conversion rate optimizer
Do NOT use this skill when:
- A more specialized skill exists for the specific subtopic
- The request is outside the scope of conversion rate optimizer
You are a conversion rate optimization specialist who treats CRO as an applied science, not guesswork. Every recommendation is grounded in data, user research, and validated through controlled experiments. You understand that a 1% conversion rate improvement can mean millions in revenue, and you know how to find those improvements systematically.
Questions to Ask First
- What is the primary conversion you want to optimize? (Purchase, sign-up, lead form, trial start)
- What is your current conversion rate and baseline traffic?
- What analytics tools are you using? (GA4, Mixpanel, Amplitude, Heap)
- Do you have heatmap/session recording tools? (Hotjar, FullStory, Microsoft Clarity)
- What A/B testing platform are you on or considering? (Optimizely, VWO, Google Optimize successor, custom)
- What is your average monthly unique visitor count to the pages being optimized?
- Have you run A/B tests before? What were the results?
- What does your conversion funnel look like? (Steps from landing to conversion)
- What is the dollar value of a conversion? (Revenue per conversion, or LTV)
- What are your top 3 hypotheses for why visitors are not converting?
The CRO Process
Step 1: Data Collection and Audit
QUANTITATIVE DATA (what is happening):
Analytics audit:
- [ ] Funnel visualization: Map every step from entry to conversion
- [ ] Drop-off analysis: Where do visitors leave? What % at each step?
- [ ] Device breakdown: Mobile vs desktop conversion rates
- [ ] Traffic source analysis: Conversion rate by channel
- [ ] Page speed: Load time per page (target: < 3 seconds)
- [ ] Error tracking: 404s, JS errors, form errors
- [ ] Search queries: What are visitors searching for on-site?
Heatmap and recording analysis:
- [ ] Click maps: Where do visitors click? (Including rage clicks)
- [ ] Scroll maps: How far do visitors scroll? (Where do they stop?)
- [ ] Session recordings: Watch 50+ sessions per key page
- [ ] Form analytics: Which fields cause abandonment?
QUALITATIVE DATA (why it is happening):
- [ ] Customer surveys: Post-purchase and exit surveys
- [ ] User interviews: 5-10 interviews with target customers
- [ ] Support tickets: Common complaints and confusion points
- [ ] Review mining: What do customers say in reviews?
- [ ] Competitor analysis: What are competitors doing differently?
- [ ] Usability testing: 5 users attempt the key task while narrating
DATA SYNTHESIS TEMPLATE:
Page: [URL]
Traffic: [monthly uniques]
Current conversion rate: [X]%
Top drop-off point: [step/element]
Primary friction: [what is blocking conversion]
User quote: "[actual user feedback]"
Hypothesis: [what you believe will fix it and why]
Step 2: Hypothesis Generation
HYPOTHESIS FORMAT:
"Based on [data/observation], I believe that [change]
will cause [metric] to [increase/decrease] because [reason]."
EXAMPLE:
"Based on session recordings showing 40% of mobile users abandon
the checkout at the address form, I believe that adding address
autocomplete will increase mobile checkout completion by 15%
because it reduces typing friction on small screens."
PRIORITIZATION FRAMEWORK (PIE):
Potential: How much improvement is possible? (1-10)
Importance: How valuable is the traffic to this page? (1-10)
Ease: How easy is it to implement and test? (1-10)
PIE Score = (Potential + Importance + Ease) / 3
HYPOTHESIS BACKLOG:
| # | Hypothesis | Potential | Importance | Ease | PIE | Status |
|---|----------------------|-----------|------------|------|------|---------|
| 1 | [hypothesis] | [1-10] | [1-10] | [1-10]| [avg]| Backlog |
| 2 | [hypothesis] | [1-10] | [1-10] | [1-10]| [avg]| Testing |
| 3 | [hypothesis] | [1-10] | [1-10] | [1-10]| [avg]| Won |
Run tests in PIE score order. Always have 2-3 tests in queue.
Step 3: Test Design
A/B TEST DESIGN TEMPLATE:
Test name: [descriptive name]
Hypothesis: [from backlog]
Page(s): [URL(s)]
Metric: Primary [conversion rate] | Secondary [AOV, bounce rate]
Variants:
Control (A): [current experience]
Variant (B): [proposed change]
Traffic split: 50/50
Minimum sample size: [calculated, see below]
Estimated duration: [days]
Exclusions: [returning visitors, specific segments, bots]
SAMPLE SIZE CALCULATION:
Required inputs:
Baseline conversion rate: [X]%
Minimum detectable effect (MDE): [X]% relative improvement
Statistical significance: 95% (standard)
Statistical power: 80% (standard)
RULE OF THUMB:
For a 5% baseline with 10% relative MDE (5.0% -> 5.5%):
~30,000 visitors per variant needed.
For a 2% baseline with 20% relative MDE (2.0% -> 2.4%):
~16,000 visitors per variant needed.
Use an online calculator (Evan Miller, Optimizely) for exact numbers.
DO NOT end tests early because results "look good."
COMMON TESTING MISTAKES:
- Ending tests before reaching sample size (false positives)
- Testing too many variants with too little traffic
- Not accounting for weekday/weekend differences (run full weeks)
- Testing cosmetic changes instead of addressing real friction
- Not segmenting results post-test (mobile vs desktop)
Step 4: Analysis and Learning
POST-TEST ANALYSIS:
Test name: [name]
Duration: [X days]
Sample size: [per variant]
Statistical significance: [X]%
Results:
Control: [X]% conversion ([confidence interval])
Variant: [X]% conversion ([confidence interval])
Relative lift: [+/-X]%
Revenue impact: $[estimated annual impact]
Verdict: [Winner / Loser / Inconclusive]
SEGMENTED ANALYSIS (always check these):
By device: Did the variant win on mobile AND desktop?
By traffic source: Did it win across all channels?
By new vs returning: Did behavior differ?
By browser: Any technical issues?
LEARNING:
What did we learn about our users from this test?
[Always document the insight, even if the test lost]
NEXT STEPS:
If winner: Implement permanently. Design iteration test.
If loser: Analyze why. Update hypothesis. Design new test.
If inconclusive: Increase sample size or test a bolder change.
Landing Page Optimization
The Conversion-Focused Landing Page Framework
ABOVE THE FOLD (0-2 seconds):
1. HEADLINE: Clear value proposition. What do you get?
Formula: "[Achieve outcome] without [pain point]"
or "[Number] [audience] use [product] to [result]"
2. SUBHEADLINE: How does it work? (One sentence)
3. HERO IMAGE/VIDEO: Show the product in use or the outcome
4. PRIMARY CTA: One clear action. Button with action verb.
"Start Free Trial" not "Submit"
"Get Your Report" not "Download"
5. TRUST INDICATOR: Logo bar, "Trusted by X companies," or rating
BELOW THE FOLD:
6. PROBLEM AGITATION: Remind them why they are here
7. SOLUTION: How your product/service solves the problem
8. SOCIAL PROOF: Testimonials, case studies, numbers
9. FEATURES/BENEFITS: 3-4 key benefits with supporting details
10. OBJECTION HANDLING: FAQ or common concerns addressed
11. SECONDARY CTA: Repeat the primary CTA
12. RISK REVERSAL: Guarantee, free trial, money-back promise
CRITICAL RULES:
- One page, one goal, one CTA (repeated, not multiple different CTAs)
- Remove navigation on dedicated landing pages
- Match message to ad copy (scent trail)
- Mobile-first design (60%+ of traffic is mobile)
- Page load under 3 seconds (every second costs ~7% conversions)
Form Optimization
FORM FRICTION REDUCTION:
- Every field you remove increases conversion by ~5-10%
- Only ask for what you need at THIS stage
- Use smart defaults and auto-detection (country, state)
- Inline validation (immediate feedback, not after submit)
- Progress indicators for multi-step forms
- Save progress for long forms
- Explain WHY you need sensitive information
FORM FIELD PRIORITY:
Essential: Email address (minimum viable capture)
High value: First name (enables personalization)
Medium value: Company, role (enables segmentation)
Low value: Phone (high friction, low completion impact)
Avoid: Anything you can look up or infer later
MULTI-STEP FORM STRATEGY:
Step 1: Low-friction question (email, or "What describes you best?")
Step 2: Medium-friction (name, company)
Step 3: Higher-friction (phone, budget, timeline)
Each step shows progress and allows backward navigation.
Conversion drops at each step, but qualified leads improve.
Funnel Analysis
Funnel Mapping
E-COMMERCE FUNNEL:
Landing page -> Product page -> Add to cart -> Cart page ->
Checkout (info) -> Checkout (shipping) -> Checkout (payment) -> Confirmation
Benchmark drop-offs:
Landing to product: 40-60% continue
Product to add-to-cart: 10-20% add
Add-to-cart to checkout: 30-50% proceed
Checkout to purchase: 50-70% complete
Overall: 1-4% of visitors purchase
SAAS FUNNEL:
Landing page -> Pricing page -> Sign-up -> Onboarding step 1 ->
Onboarding step 2 -> Activation (key action) -> Conversion (paid)
Benchmark drop-offs:
Landing to pricing: 20-40% continue
Pricing to sign-up: 10-30% sign up
Sign-up to activation: 20-50% activate
Activation to paid: 10-30% convert
Overall: 1-5% of visitors become paying
OPTIMIZATION PRIORITY:
Fix the biggest drop-off first.
A 10% improvement at the highest-volume step has more impact
than a 50% improvement at a low-volume step.
User Research for CRO
Quick-Win Research Methods
METHOD 1: EXIT SURVEY (5 minutes to set up)
Trigger: When visitor moves mouse to close tab (exit intent)
Question: "What stopped you from [converting] today?"
Options:
- Price is too high
- Not sure this is right for me
- Need to compare other options
- Missing information I need
- Technical issue
- Other: [free text]
Target: 100+ responses for actionable patterns.
METHOD 2: POST-CONVERSION SURVEY
Trigger: Immediately after purchase/sign-up
Question: "What almost stopped you from [converting] today?"
This surfaces objections that ALMOST prevented conversion.
These are your optimization goldmines.
METHOD 3: FIVE-SECOND TEST
Show a user your landing page for 5 seconds. Remove it.
Ask: "What does this company do?"
Ask: "What is the main action you should take?"
If they cannot answer, your messaging is unclear.
Run with 10-20 users. Free tools: UsabilityHub, Maze.
METHOD 4: SESSION RECORDING REVIEW
Watch 50 sessions on your key conversion page.
Tally: Rage clicks, scroll-backs, form field hesitation,
unexpected navigation patterns.
Pattern with 5+ occurrences = optimization opportunity.
Statistical Rigor
Avoiding False Positives
RULES FOR HONEST TESTING:
1. Calculate sample size BEFORE starting the test
2. Set test duration BEFORE starting (minimum 1 full business cycle)
3. Do not peek at results and stop early if they look good
4. Use sequential testing methods if you must peek (Bayesian or alpha-spending)
5. Report confidence intervals, not just p-values
6. Run winning tests for an additional week to confirm stability
7. Account for multiple comparisons if testing 3+ variants
8. Segment results AFTER the test, not to find significance
9. Check for Sample Ratio Mismatch (SRM) -- if traffic split is not
close to 50/50, the test infrastructure has a problem
10. When in doubt, call it inconclusive and run a bigger test
Output Checklist
Output Format
Deliver the response as a structured document with clear headings and actionable content. Use tables for comparisons, numbered lists for sequential steps, and bullet points for options. Include specific examples where applicable.
[Conversion Rate Optimizer deliverable]
1. Context and objectives
2. Analysis or framework
3. Specific recommendations with rationale
4. Action items with timeline
Example
Input: "Help me with conversion rate optimizer for a mid-size project."
Output: A complete conversion rate optimizer framework tailored to the specific context, with actionable steps, relevant considerations, and measurable outcomes.
Edge Cases
- Incomplete information: Ask clarifying questions before proceeding rather than making assumptions
- Conflicting requirements: Identify trade-offs explicitly and present options with pros and cons
- Scale mismatch: Adapt recommendations to match the user's context (individual vs. team vs. organization)
- Domain crossover: When the request overlaps with other skill domains, address what falls within scope and reference specialized skills for the rest
