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 is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
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ab-testing
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 is better," "test two versions," "statistical significance," "how long should I run this test," "growth experiments," "experiment velocity," "experiment backlog," "ICE score," "experimentation program," or "experiment playbook." Use this whenever someone is comparing two approaches and wants to measure which performs better, or when they want to build a systematic experimentation practice. For tracking implementation, see analytics. For page-level conversion optimization, see cro.
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:
Test Context - What are you trying to improve? What change are you considering?
Current State - Baseline conversion rate? Current traffic volume?
Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].
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."
For detailed sample size tables and duration calculations: See 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.
Effect size meaningful? Compare to MDE, project impact
Secondary metrics consistent? Support the primary?
Guardrail concerns? Anything get worse?
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
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
1. Generate hypotheses (from data, research, competitors, customer feedback)
2. Prioritize with ICE scoring
3. Design and run the test
4. Analyze results with statistical rigor
5. Promote winners to a playbook
6. Generate new hypotheses from learnings
→ Repeat
Hypothesis Generation
Feed your experiment backlog from multiple sources:
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:
## [Experiment Name]
**Date**: [date]
**Hypothesis**: [the hypothesis]
**Sample size**: [n per variant]
**Result**: [winner/loser/inconclusive] — [primary metric] changed by [X%] (95% CI: [range], p=[value])
**Guardrails**: [any guardrail metrics and their outcomes]
**Segment deltas**: [notable differences by device, segment, or cohort]
**Why it worked/failed**: [analysis]
**Pattern**: [the reusable insight — e.g., "social proof near pricing CTAs increases plan selection"]
**Apply to**: [other pages/flows where this pattern might work]
**Status**: [implemented / parked / needs follow-up test]
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.
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
What's your current conversion rate?
How much traffic does this page get?
What change are you considering and why?
What's the smallest improvement worth detecting?
What tools do you have for testing?
Have you tested this area before?
Related Skills
cro: For generating test ideas based on CRO principles
