Audit websites and landing pages for conversion issues and design evidence-based A/B tests. Use when the user mentions "landing page isn''t converting", "conversion rate", "A/B test", "why visitors leave", or "objection handling". Covers funnel mapping, persuasion assets, and objection/counter-objection frameworks. For overall marketing strategy, see one-page-marketing. For usability issues, see ux-heuristics. Trigger with 'cro', 'methodology'.
Instrucciones de origen · Vista previa de solo lectura
name
cro-methodology
description
Audit websites and landing pages for conversion issues and design evidence-based A/B tests. Use when the user mentions "landing page isn''t converting", "conversion rate", "A/B test", "why visitors leave", or "objection handling". Covers funnel mapping, persuasion assets, and objection/counter-objection frameworks. For overall marketing strategy, see one-page-marketing. For usability issues, see ux-heuristics. Trigger with 'cro', 'methodology'.
allowed-tools
Read, Glob, Grep
license
MIT
author
Wondelai <hello@wondelai.com>
version
1.0.1
compatible-with
claude-code, codex, openclaw
tags
["business","testing","audit"]
CRO Methodology
Scientific, customer-centric approach to conversion rate optimization based on the CRE Methodology(TM). Extraordinary improvements come from understanding WHY visitors don't convert, not from copying competitors or applying generic tips.
Core Principle
Don't guess -- discover. The methodology rejects "best practices" and "magic buttons" in favor of evidence-based optimization. Most websites underperform not because of bad design, but because no one has systematically researched why visitors leave without converting.
The foundation: Every visitor who doesn't convert has a reason. Your job is to discover those reasons through research, then systematically eliminate them with evidence and proof. This customer-centric approach consistently outperforms intuition, competitor copying, and "expert" opinions.
Scoring
Goal: 10/10. When reviewing or creating landing pages, funnels, or conversion flows, rate them 0-10 based on adherence to the principles below. A 10/10 means full alignment with all guidelines; lower scores indicate gaps to address. Always provide the current score and specific improvements needed to reach 10/10.
The CRO Frameworks
1. The CRO Process
Core concept: A systematic 9-step process for optimizing conversion rates, moving from defining success metrics through research, experimentation, and scaling wins across the business.
Why it works: Random optimization efforts fail because they skip the critical research steps. The CRE process forces you to understand visitors before changing anything, ensuring changes are based on evidence rather than opinion.
Key insights:
Define success metrics aligned with business KPIs before touching any page
Map the entire conversion funnel to find "blocked arteries" (high-traffic underperforming paths) and "missing links" (absent funnel stages)
Understand visitors in three dimensions: who they are (types and intentions), what blocks them (UX problems), and what stops them (objections)
Gather market intelligence from competitors, reviews, and other industries
Prioritize ideas using ICE scoring (Impact, Confidence, Ease) before testing
Create bold experimental designs based on research, not "meek tweaks"
Run experiments with proper statistical rigor (95% confidence minimum, full business cycles)
Scale wins across landing pages, ad copy, email sequences, and offline materials
Product applications:
Context
CRO Process Step
Example
Landing page audit
Steps 1-3: Define goals, map funnel, research visitors
Identify that 70% of traffic bounces because value prop is unclear
A/B test two positioning approaches before full rollout
Email sequence
Step 9: Scale wins
Apply winning objection-handling copy from landing page to drip emails
Competitor response
Step 4: Market intelligence
Transfer proven strategies from adjacent industries
Copy patterns:
"What's preventing you from [action] today?" (exit survey question to discover objections)
"Here's what [X] customers found..." (counter-objection with social proof)
Document hypothesis: "If we [change X], then [metric Y] will improve because [reason from research]"
Always calculate required sample size BEFORE starting any test
Ethical boundary: Never manipulate test results or cherry-pick data. Report all tests, including failures, and wait for genuine statistical significance.
See: testing-methodology.md for detailed ICE scoring, A/B vs. multivariate guidance, and statistical rigor.
2. Customer Research & Objections
Core concept: Visitors don't convert for specific, discoverable reasons. Research methods -- exit surveys, chat logs, support tickets, sales calls, reviews -- reveal the "voice of the customer" and their real objections.
Why it works: Companies guess why visitors leave, but guesses are almost always wrong. Direct research consistently uncovers objections that teams never anticipated, and the language customers use is more persuasive than any copywriter's invention.
Key insights:
Primary sources (exit surveys, live chat logs, support tickets, sales call recordings) give you direct visitor language
Secondary sources (reviews, social media, competitor analysis) reveal industry-wide objections
Objections fall into two categories: explicit ("too expensive") and implicit ("I'm not sure I'll follow through")
The "Big 5" universal objections are Trust, Price, Fit, Timing, and Effort
Post-purchase surveys ("What almost stopped you from buying?") reveal the objections that matter most
Non-converter surveys should ask ONE question for maximum response rate
Quantitative research (analytics, heatmaps) shows WHERE problems are; qualitative research (surveys, interviews) shows WHY
Product applications:
Context
Research Method
Example
Exit intent
On-site survey (Hotjar, Qualaroo)
"What's preventing you from signing up today?"
Post-purchase
Email survey within 7 days
"What almost stopped you from buying?"
Objection mining
Support ticket analysis
Search for "but", "however", "worried about" patterns
Voice of customer
Sales call recordings
Capture exact language customers use to describe problems
Competitive gaps
Review mining (yours and competitors')
Negative reviews = unaddressed objections
Copy patterns:
Use exact customer language in headlines and body copy (more persuasive than polished marketing copy)
"What's the one thing we could change to make you [action]?"
"How would you describe [product] to a friend?" (reveals positioning in customer terms)
Ask open-ended questions for discovery; save multiple choice for validation
Ethical boundary: Respect customer privacy in research. Anonymize data, get consent for recordings, and don't survey so aggressively that you degrade the user experience.
See: RESEARCH.md for tools, survey questions, and data analysis methods.
3. Persuasion Assets
Core concept: Every company has overlooked proof elements -- testimonials not displayed, awards not mentioned, statistics not highlighted, guarantees not prominent, team credentials hidden. These are "persuasion assets" that must be inventoried, acquired, and displayed.
Why it works: Visitors make decisions based on evidence and proof, not claims. A bold claim without proof is just noise. A modest claim with overwhelming proof is irresistible. Most companies sit on goldmines of proof they never use.
Key insights:
Audit five categories: Credentials & Authority, Social Proof, Risk Reversal, Data & Specificity, Process & Methodology
Create a "wish list" for missing assets and actively acquire them (request testimonials, apply for awards, compile statistics)
The "proof sandwich" structure: Claim (bold promise) then Proof (evidence) then Reinforcement (secondary proof)
Hierarchy of proof from strongest to weakest: specific results with context, named testimonials with photos, case studies, statistics, logos/badges, generic testimonials
Place proof at points of friction, not hidden in FAQs
Specific numbers beat round numbers ("47,832 customers" beats "About 50,000")
Product applications:
Context
Persuasion Asset
Example
Landing page header
Logo bar + rating
"Trusted by 10,000+ companies" with 5 recognizable logos
Pricing page
Risk reversal
"30-day money-back guarantee, no questions asked"
Product page
Specific testimonial
Photo + name + company + "Increased conversion by 47% in 3 weeks"
Checkout flow
Trust badges near forms
Security certification, payment logos, guarantee seal
About page
Team credentials
Years of experience, certifications, publications, patents
Copy patterns:
"Here's how we did it for [Company X]..." (case study proof)
"And here's what their CEO says about working with us..." (testimonial reinforcement)
"[Specific number] businesses trust us" (not "thousands of customers")
Lead with benefits, not features: "Never delete another photo" beats "256GB storage"
Ethical boundary: Never fabricate testimonials, inflate statistics, or display fake trust badges. All proof must be genuine and verifiable.
See: PERSUASION.md for the full persuasion assets checklist and psychological triggers.
4. The O/CO Framework
Core concept: The Objection/Counter-Objection (O/CO) table is the core CRE technique. Create a two-column table mapping every visitor objection to specific, evidence-backed counter-objections.
Why it works: Visitors arrive with objections. If the page doesn't address them, visitors leave. The O/CO framework ensures no objection goes unanswered, and counter-objections are placed exactly where objections naturally arise during the reading flow.
Key insights:
Don't guess objections -- research them from surveys, chat logs, support tickets, and sales calls
Implicit objections (ones visitors won't admit) require "CO Only" approach: address the objection without stating it
Place counter-objections at the point of friction (credit card objection near payment form), not buried in FAQ
Address primary objections above the fold, secondary objections in the flow
Use multiple formats for the same counter-objection: text, video, testimonial, data
Canned support responses are goldmines of tested counter-objections
Product applications:
Context
Objection Type
O/CO Example
Trust
"Why should I believe you?"
Specific testimonials, media logos, awards, money-back guarantee
Price
"Is it worth the money?"
ROI calculator, cost comparison vs. alternatives, payment plans
Fit
"Will it work for MY situation?"
Case studies from similar customers, segmented landing pages, free trial
Timing
"Why should I act now?"
Cost of delay calculation, genuine limited-time offers, seasonal relevance
Effort
"How hard will this be?"
"Done for you" framing, "Set up in 5 minutes", step-by-step breakdown
Copy patterns:
Bad (stating implicit objection): "Worried you're too lazy to learn a language?"
Good (CO Only): "Let the audio do the work for you."
"What's preventing you from signing up today?" (survey to discover objections)
"What almost stopped you from buying?" (post-purchase survey to validate O/CO table)
Ethical boundary: Address real objections with honest counter-objections. Never dismiss legitimate concerns or use deception to overcome valid hesitations.
See: OBJECTIONS.md for the full O/CO framework, research methods, and counter-objection techniques.
5. Hypothesis Design
Core concept: Every experiment needs a documented hypothesis linking a specific change to an expected outcome with a reason grounded in research. Prioritize using ICE scoring (Impact, Confidence, Ease).
Why it works: Without a hypothesis, you're just changing things randomly. The hypothesis forces you to articulate WHY a change should work, which means it must be grounded in customer research. ICE scoring prevents teams from wasting time on low-impact "meek tweaks."
Key insights:
Hypothesis format: "If we [change X], then [metric Y] will improve because [reason based on research]"
Define primary metric (determines winner), secondary metrics (additional monitoring), and guardrail metrics (must not decrease)
ICE scores: Impact (1-10: could this double conversion?), Confidence (1-10: is research backing strong?), Ease (1-10: how easy to implement?)
Make BOLD changes, not "meek tweaks" -- small changes rarely reach statistical significance and waste resources
Before testing, ask: "Could this 10x our results?" If not, reconsider priority
Worth testing: complete page redesign, new value proposition, fundamentally different offer
Not worth testing: button color, font size, image swap
Product applications:
Context
Hypothesis Example
ICE Score
Headline rewrite
"If we use customer language from surveys, conversion will increase because visitors see their own words reflected"
I:8, C:9, E:10 = 9.0
Video testimonial
"If we add video testimonial addressing price objection, signups will increase because visitors need trust proof"
I:7, C:7, E:6 = 6.7
Checkout redesign
"If we simplify checkout to one page, completion will increase because analytics show 40% drop at step 2"
I:9, C:6, E:3 = 6.0
Button color
"If we change button from blue to green, clicks will increase because green means go"
I:2, C:2, E:10 = 4.7
Copy patterns:
"Based on our research, visitors' #1 objection is [X]. This test addresses it by [Y]."
Core concept: Run controlled experiments comparing page versions to determine which performs better, using proper statistical rigor to ensure results are real, not random noise.
Why it works: Without controlled experiments, it is impossible to distinguish real improvements from random variation. Proper A/B testing methodology prevents the most common errors: peeking and stopping early, insufficient sample size, ignoring practical significance, and the multiple comparison problem.
Start with A/B tests; only move to multivariate when you have 100k+ monthly visitors and a proven winning page
A failed test that teaches you something is more valuable than a winning test you don't understand
Promote winners to new control and iterate
Product applications:
Context
Test Type
Example
Concept validation
A/B test (2-4 variants)
Test two fundamentally different page layouts based on different customer insights
Element optimization
Multivariate (100k+ visitors)
Test 3 headlines x 3 images x 2 CTAs on proven winning page
Low traffic
Bold A/B test
Make dramatic changes detectable with smaller samples (~4,000 visitors for 50% lift)
High traffic
Rapid iteration
Run parallel tests on non-overlapping pages, 10-20 tests/month
Post-test
Scale wins
Apply winning insights across landing pages, ad copy, email sequences
Copy patterns:
"We increased [metric] by [X]% with [Y]% confidence over [Z] weeks"
"Test showed no significant difference, teaching us that [insight about customers]"
"Control outperformed challenger, suggesting visitors prefer [existing approach] because [reason]"
Always document learnings: Test, Hypothesis, Result, Learning, Applicable to
Ethical boundary: Never manipulate statistical methods to manufacture significance. Report confidence intervals honestly and acknowledge when results are inconclusive.
See: testing-methodology.md for statistical significance, sample size calculations, and platform comparison.
Common Mistakes
Mistake
Why It Fails
Fix
Copying competitors blindly
You don't know if their approach works for them, let alone for you
Research YOUR visitors' objections and build YOUR evidence
Testing button colors before understanding objections
funnel-analysis.md: Blocked arteries, missing links, industry funnels, cross-sell mapping
Further Reading
This skill is based on the CRE Methodology(TM) developed by Conversion Rate Experts. For the complete methodology, detailed case studies, and advanced techniques, read the original book:
Dr. Karl Blanks and Ben Jesson are the cofounders of Conversion Rate Experts (CRE), the world's leading agency specializing in conversion rate optimization. Their clients have included Google, Apple, Amazon, Facebook, Dropbox, and many other technology leaders. CRE's methodology has been recognized with a Queen's Award for Enterprise (Innovation), the UK's highest business honor. Blanks holds a PhD in user experience and previously managed teams of usability researchers at Hewlett-Packard. Jesson's background is in direct-response marketing and web development. Together they developed the CRE Methodology, which has been applied across hundreds of websites and consistently delivered significant conversion improvements. Their book Making Websites Win distills this methodology into a systematic, repeatable process for evidence-based website optimization.
Overview
Audit websites and landing pages for conversion issues and design evidence-based A/B tests.
Prerequisites
Access to the testing environment or API
Required CLI tools installed and authenticated
Familiarity with testing concepts and terminology
Instructions
Assess the current state of the testing configuration
Identify the specific requirements and constraints
Apply the recommended patterns from this skill
Validate the changes against expected behavior
Document the configuration for team reference
Output
Configuration files or code changes applied to the project
