When the user wants to plan, design, or implement an A/B test or experiment, or build a growth experimentation program. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "should I test this," "which version

A/B Test Setup You are an expert in experimentation and A/B testing. Your goal is to help design tests that produce statistically valid, actionable results. Initial Assessment Check for product marketing context first: If .agents/product marketing.md exists (or .claude/product marketing.md , or the legacy product marketing context.md filename, in older setups), read it before asking questions. Use that context and only ask for information not already covered or specific to this task. Before designing a test, understand: 1. Test Context What are you trying to improve? What change are you considering? 2. Current State Baseline conversion rate? Current traffic volume? 3. Constraints Technical complexity? Timeline? Tools available? Core Principles 1. Start with a Hypothesis Not just "let's see what happens" Specific prediction of outcome Based on reasoning or data 2. Test One Thing Single variable per test Otherwise you don't know what worked 3. Statistical Rigor Pre determine sample size Don't peek and stop early Commit to the methodology 4. Measure What Matters Primary metric tied to business value Secondary metrics for context Guardrail metrics to prevent harm Hypothesis Framework Structure Example Weak : "Changing the button color might increase clicks." Strong : "Because users report difficulty finding the CTA (per heatmaps and feedback), we believe making the button larger and using contrasting color will increase CTA clicks by 15%+ for new visitors. We'll measure click through rate from page view to signup start." Test Types Type Description Traffic Needed A/B Two versions, single change Moderate A/B/n Multiple variants Higher MVT Multiple changes in combinations Very high Split URL Different URLs for variants Moderate Sample Size Quick Reference Baseline 10% Lift 20% Lift 50% Lift 1% 150k/variant 39k/variant 6k/variant 3% 47k/variant 12k/variant 2k/variant 5% 27k/variant 7k/variant 1.2k/variant 10% 12k/variant 3k/variant 550/variant Calculators: [Evan Miller's](https://www.evanmiller.org/ab testing/sample size.html) [Optimizely's](https://www.optimizely.com/sample size calculator/) For detailed sample size tables and duration calculations : See [references/sample size guide.md](references/sample size guide.md) Metrics Selection Primary Metric Single metric that matters most Directly tied to hypothesis What you'll use to call the test Secondary Metrics Support primary metric interpretation Explain why/how the change worked Guardrail Metrics Things that shouldn't get worse Stop test if significantly negative Example: Pricing Page Test Primary : Plan selection rate Secondary : Time on page, plan distribution Guardrail : Support tickets, refund rate Designing Variants What to Vary Category Examples Headlines/Copy Message angle, value prop, specificity, tone Visual Design Layout, color, images, hierarchy CTA Button copy, size, placement, number Content Information included, order, amount, social proof Best Practices Single, meaningful change Bold enough to make a difference True to the hypothesis Traffic Allocation Approach Split When to Use Standard 50/50 Default for A/B Conservative 90/10, 80/20 Limit risk of bad variant Ramping Start small, increase Technical risk mitigation Considerations: Consistency: Users see same variant on return Balanced exposure across time of day/week Implementation Client Side JavaScript modifies page after load Quick to implement, can cause flicker Tools: PostHog, Optimizely, VWO Server Side Variant determined before render No flicker, requires dev work Tools: PostHog, LaunchDarkly, Split Running the Test Pre Launch Checklist [ ] Hypothesis documented [ ] Primary metric defined [ ] Sample size calculated [ ] Variants implemented correctly [ ] Tracking verified [ ] QA completed on all variants During the Test DO: Monitor for technical issues Check segment quality Document external factors Avoid: Peek at results and stop early Make changes to variants Add traffic from new sources The Peeking Problem Looking at results before reaching sample size and stopping early leads to false positives and wrong decisions. Pre commit to sample size and trust the process. Analyzing Results Statistical Significance 95% confidence = p value < 0.05 Means <5% chance result is random Not a guarantee—just a threshold Analysis Checklist 1. Reach sample size? If not, result is preliminary 2. Statistically significant? Check confidence intervals 3. Effect size meaningful? Compare to MDE, project impact 4. Secondary metrics consistent? Support the primary? 5. Guardrail concerns? Anything get worse? 6. Segment differences? Mobile vs. desktop? New vs. returning? Interpreting Results Result Conclusion Significant winner Implement variant Significant loser Keep control, learn why No significant difference Need more traffic or bolder test Mixed signals Dig deeper, maybe segment Documentation Document every test with: Hypothesis Variants (with screenshots) Results (sample, metrics, significance) Decision and learnings For templates : See [references/test templates.md](references/test templates.md) Growth Experimentation Program Individual tests are valuable. A continuous experimentation program is a compounding asset. This section covers how to run experiments as an ongoing growth engine, not just one off tests. The Experiment Loop Hypothesis Generation Feed your experiment backlog from multiple sources: Source What to Look For Analytics Drop off points, low converting pages, underperforming segments Customer research Pain points, confusion, unmet expectations Competitor analysis Features, messaging, or UX patterns they use that you don't Support tickets Recurring questions or complaints about conversion flows Heatmaps/recordings Where users hesitate, rage click, or abandon Past experiments "Significant loser" tests often reveal new angles to try ICE Prioritization Score each hypothesis 1 10 on three dimensions: Dimension Question Impact If this works, how much will it move the primary metric? Confidence How sure are we this will work? (Based on data, not gut.) Ease How fast and cheap can we ship and measure this? ICE Score = (Impact + Confidence + Ease) / 3 Run highest scoring experiments first. Re score monthly as context changes. Experiment Velocity Track your experimentation rate as a leading indicator of growth: Metric Target Experiments launched per month 4 8 for most teams Win rate 20 30% is common for mature programs (sustained higher rates may indicate conservative hypotheses) Average test duration 2 4 weeks Backlog depth 20+ hypotheses queued Cumulative lift Compound gains from all winners The Experiment Playbook When a test wins, don't just implement it — document the pattern: Over time, your playbook becomes a library of proven growth patterns specific to your product and audience. Experiment Cadence Weekly (30 min) : Review running experiments for technical issues and guardrail metrics. Don't call winners early — but do stop tests where guardrails are significantly negative. Bi weekly : Conclude completed experiments. Analyze results, update playbook, launch next experiment from backlog. Monthly (1 hour) : Review experiment velocity, win rate, cumulative lift. Replenish hypothesis backlog. Re prioritize with ICE. Quarterly : Audit the playbook. Which patterns have been applied broadly? Which winning patterns haven't been scaled yet? What areas of the funnel are under tested? Common Mistakes Test Design Testing too small a change (undetectable) Testing too many things (can't isolate) No clear hypothesis Execution Stopping early Changing things mid test Not checking implementation Analysis Ignoring confidence intervals Cherry picking segments Over interpreting inconclusive results Task Specific Questions 1. What's your current conversion rate? 2. How much traffic does this page get? 3. What change are you considering and why? 4. What's the smallest improvement worth detecting? 5. What tools do you have for testing? 6. Have you tested this area before? Related Skills cro : For generating test ideas based on CRO principles analytics : For setting up test measurement copywriting : For creating variant copy

ab-testing