Cro Optimization

CRO Optimization

Run conversion rate optimization as a structured discipline: audit → hypothesize → test → decide. Stack-agnostic. Tool-agnostic.

This skill is for running tests against existing pages and flows. For writing landing page copy from scratch, use landing-page-copy. For setting up the analytics that make CRO possible, use analytics-strategy.

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When to use

• Converting traffic at lower rate than expected
• Specific funnel step has high drop-off
• Pages with high traffic that could move the needle if optimized
• A/B testing infrastructure exists (or can be set up)
• Statistical significance and sample size questions

When NOT to use

• Without sufficient traffic to test (under ~5,000 monthly conversions per variant)
• Pre-launch (no users to test on yet)
• Strategy or messaging-level questions that need qualitative research first
• Brand-defining choices (CRO can't optimize a fundamentally wrong brand)

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Required inputs

• The page or flow under optimization
• Current conversion rate and traffic volume
• Access to analytics (event tracking, funnel data)
• An A/B testing tool (or willingness to set one up)
• Time and budget for testing (typically 2 to 8 weeks per test)

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The framework: 4 phases

1. Audit

Diagnose before treating.

Quantitative audit:

• Funnel data. Where are users dropping off? The biggest drop is the biggest opportunity.
• Segmentation. Does the funnel perform differently by source, device, geography, audience type?
• Performance data. Are slow pages dragging conversions?
• Search Console / on-site search. What are users looking for that they can't find?

Qualitative audit:

• Session replay. Watch 20+ sessions of users on the target flow. Note friction, confusion, hesitation.
• Heatmaps. Where do users click? Where do they scroll? Where do they not?
• User interviews / surveys. Why did users not convert? Survey people who started but abandoned.
• Form analytics. Which fields cause abandonment? Which cause errors?
• Customer support tickets. What conversion-related questions come in?

Heuristic audit:

• Apply CRO heuristics to the flow:
  • Is the value proposition clear in 5 seconds?
  • Is there a single primary CTA per page?
  • Is the form length appropriate to the offer?
  • Is the trust/social proof present?
  • Are objections handled?
  • Is the page accessible? (Accessibility issues hurt conversion silently.)

The audit produces a list of suspected friction points. Each becomes a hypothesis candidate.

2. Hypothesis

A testable statement.

Hypothesis structure:

Because [observation from audit], we believe that [change] will produce [predicted outcome] for [user segment], because [reason].

Example:

Because session replays show users abandoning at the shipping step (audit), we believe that adding visible shipping cost to the product page (change) will increase add-to-cart conversion by 5 percent (outcome) for desktop users (segment), because users are surprised by shipping cost and abandon (reason).

Hypothesis quality criteria:

• Specific change (not "improve the design")
• Measurable outcome (with a target)
• Grounded in evidence (audit, research, prior tests)
• Tied to a known mechanism (why would this work?)

Hypothesis prioritization (ICE or PIE):

• Impact: How much could this move the metric?
• Confidence: How likely is the hypothesis to be right?
• Ease: How easy to test? (Time, complexity, risk)

Score each 1 to 10. Highest combined scores test first.

3. Test design

A test that produces an unambiguous answer.

Sample size and duration:

Use a sample size calculator (most A/B tools have one) before launching. Inputs:

• Baseline conversion rate
• Minimum detectable effect (the smallest lift you'd care about)
• Statistical power (typically 80%)
• Significance level (typically 95%)

This produces required sample size per variant. Run the test until that sample is reached, OR for a minimum duration that captures full business cycle (typically 2 weeks minimum, to cover weekends and weekly patterns).

Common test setup mistakes:

• Stopping the test the moment significance is hit (peeking)
• Running tests for too short to capture a full business cycle
• Running multiple overlapping tests on the same flow
• Testing during atypical periods (Black Friday, holidays, major campaigns)
• Excluding mobile when 50%+ of traffic is mobile (or vice versa)
• Testing on too small a slice of traffic (low statistical power)
• Not segmenting analysis (overall lift can hide negative impact on a segment)

Test parameters to define before launch:

• Primary metric (one)
• Guardrail metrics (do not go down)
• Sample size
• Duration (minimum and maximum)
• Decision criteria (when to ship, when to kill, when to extend)
• Segments to analyze in addition to overall

4. Decide

After the test concludes.

Decision framework:

Anti-patterns:

• "It looks like it's winning, ship it" before reaching significance
• Shipping a variant because the team wants to (HiPPO - highest paid person's opinion)
• Killing tests too early because they look bad
• Re-running tests until they "win" (false positive risk)
• Not capturing the learning when a test loses

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