--- name: rampstackco/cro-optimization source: https://app.decimal.ai/s/rampstackco-cro-optimization@2/SKILL.md source_sha256: 37af882e6de6 --- # 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`. --- ## 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) --- ## 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) --- ## 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:** | Outcome | Decision | |---|---| | Variant clearly wins (>95% significance, exceeds minimum effect) | Ship variant. Document. Continue testing. | | Variant clearly loses | Kill. Capture the lesson. Iterate hypothesis. | | Inconclusive (neither significant) | Larger test, different angle, or move on. Don't ship "tied" variants. | | Small lift, lots of variance | Probably not worth shipping. Even if "winner," may not replicate. | | Wins overall, loses for important segment | Investigate segment. Consider segment-specific solution. | **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 --- ## Statistical foundations ### Significance and confidence A 95% significance level means: if there were truly no difference between variants, there's only a 5% chance you'd see results this extreme by chance. That's not the same as "95% chance the variant wins." Most CRO tools report Bayesian probabilities ("95% chance of being best"). Read the methodology your tool uses. ### Sample size Conversion testing needs more sample than people intuit. Quick reference: | Baseline rate | Minimum detectable effect | Sample per variant | |---|---|---| | 2% | 10% relative lift | ~75,000 | | 2% | 20% relative lift | ~19,000 | | 5% | 10% relative lift | ~30,000 | | 5% | 20% relative lift | ~7,500 | | 10% | 10% relative lift | ~14,000 | | 10% | 20% relative lift | ~3,500 | (Approximate. Use a calculator.) If your monthly conversions per variant don't reach these numbers, A/B testing won't produce reliable results. Iterate via design and qualitative research instead. ### Multiple testing The more variants and metrics tested simultaneously, the more false positives. Adjust significance thresholds for multiple comparisons (Bonferroni or similar). --- ## Workflow 1. **Audit.** Quantitative + qualitative + heuristic. 2. **Generate hypotheses.** From audit findings. Apply hypothesis structure. 3. **Prioritize.** ICE or PIE. Top 3 to 5 to test next. 4. **Design the test.** Sample size, duration, primary and guardrail metrics, decision criteria. 5. **Implement.** Build variants. QA carefully (broken variants invalidate tests). 6. **Run.** Don't peek. Don't stop early. 7. **Analyze.** Overall and by segment. Note interesting patterns regardless of significance. 8. **Decide.** Ship, kill, or extend. 9. **Document.** Hypothesis, design, results, decision, lesson. 10. **Compound.** Apply lessons to next round of hypotheses. --- ## Failure patterns - **Testing without audit.** Random changes, random results. - **Vague hypotheses.** "Make it better" is not a hypothesis. - **Peeking and early stopping.** Bias toward false positives. - **Underpowered tests.** Not enough sample for a real conclusion. - **HiPPO override.** Highest paid person's opinion overrides the data. - **Testing during atypical periods.** Holidays distort results. - **Single metric obsession.** Conversion ups but average order value craters. Net loss. - **No guardrail metrics.** Testing for one outcome, missing damage to others. - **Documentation gap.** Wins captured, losses forgotten. Same hypothesis re-tested 3 times. - **Treating each test in isolation.** Compounding learning across tests is where CRO programs really win. --- ## Output format Default output: a markdown test plan at `cro-test-[hypothesis-slug].md` per test. After the test runs, append the results section. Structure: ```markdown # Test: [Hypothesis short name] ## Hypothesis Because [observation], we believe that [change] will produce [outcome] for [segment], because [reason]. ## Audit evidence [What evidence supports this hypothesis] ## Test design - Primary metric: - Guardrail metrics: - Sample size required: - Duration: minimum X, maximum Y - Variant traffic split: - Segments to analyze: ## Decision criteria - Ship if: [conditions] - Kill if: [conditions] - Extend if: [conditions] ## Results (filled after test) - Sample reached: - Duration actual: - Primary metric: [variant vs control + significance] - Guardrail metrics: [results] - Segment analysis: [findings] ## Decision [Ship / Kill / Extend / Iterate] - [Why] ## Lesson [What this teaches us, regardless of outcome] ``` --- ## If required data is unavailable This skill's output depends on data, measurements, or tool results it cannot generate on its own. When a required input, tool, or data source is unavailable or unverifiable, the sanctioned output is the deliverable with the gap stated: what was needed, what was actually obtained or verified, and which parts of the output are affected. Fabricating, estimating, or interpolating a required number to complete the deliverable is never sanctioned. A stated gap is a complete answer. --- ## Reference files - [`references/hypothesis-library.md`](references/hypothesis-library.md) - Common high-impact hypothesis patterns by funnel stage.