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
Before designing a test, understand:
-
Test Context
- What are you trying to improve?
- What change are you considering?
- What made you want to test this?
-
Current State
- Baseline conversion rate?
- Current traffic volume?
- Any historical test data?
-
Constraints
- Technical implementation complexity?
- Timeline requirements?
- 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
- Save MVT for later
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
Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].
Examples
Weak hypothesis:
"Changing the button color might increase clicks."
Strong hypothesis:
"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."
Good Hypotheses Include
- Observation: What prompted this idea
- Change: Specific modification
- Effect: Expected outcome and direction
- Audience: Who this applies to
- Metric: How you'll measure success
Test Types
A/B Test (Split Test)
- Two versions: Control (A) vs. Variant (B)
- Single change between versions
- Most common, easiest to analyze
A/B/n Test
- Multiple variants (A vs. B vs. C...)
- Requires more traffic
- Good for testing several options
Multivariate Test (MVT)
- Multiple changes in combinations
- Tests interactions between changes
- Requires significantly more traffic
- Complex analysis
Split URL Test
- Different URLs for variants
- Good for major page changes
- Easier implementation sometimes
Sample Size Calculation
Inputs Needed
- Baseline conversion rate: Your current rate
- Minimum detectable effect (MDE): Smallest change worth detecting
- Statistical significance level: Usually 95%
- Statistical power: Usually 80%
Quick Reference
| Baseline Rate | 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 |
Formula Resources
Test Duration
Duration = Sample size needed per variant × Number of variants
───────────────────────────────────────────────────
Daily traffic to test page × Conversion rate
Minimum: 1-2 business cycles (usually 1-2 weeks)
Maximum: Avoid running too long (novelty effects, external factors)
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
- Help understand user behavior
Guardrail Metrics
- Things that shouldn't get worse
- Revenue, retention, satisfaction
- Stop test if significantly negative
Metric Examples by Test Type
Homepage CTA test:
- Primary: CTA click-through rate
- Secondary: Time to click, scroll depth
- Guardrail: Bounce rate, downstream conversion
Pricing page test:
- Primary: Plan selection rate
- Secondary: Time on page, plan distribution
- Guardrail: Support tickets, refund rate
Signup flow test:
- Primary: Signup completion rate
- Secondary: Field-level completion, time to complete
- Guardrail: User activation rate (post-signup quality)
Designing Variants
Control (A)
- Current experience, unchanged
- Don't modify during test
Variant (B+)
Best practices:
- Single, meaningful change
- Bold enough to make a difference
- True to the hypothesis
What to vary:
Headlines/Copy:
- Message angle
- Value proposition
- Specificity level
- Tone/voice
Visual Design:
- Layout structure
- Color and contrast
- Image selection
- Visual hierarchy
CTA:
- Button copy
- Size/prominence
- Placement
- Number of CTAs
Content:
- Information included
- Order of information
- Amount of content
- Social proof type
Documenting Variants
Control (A):
- Screenshot
- Description of current state
Variant (B):
- Screenshot or mockup
- Specific changes made
- Hypothesis for why this will win
Traffic Allocation
Standard Split
- 50/50 for A/B test
- Equal split for multiple variants
Conservative Rollout
- 90/10 or 80/20 initially
- Limits risk of bad variant
- Longer to reach significance
Ramping
- Start small, increase over time
- Good for technical risk mitigation
- Most tools support this
Considerations
- Consistency: Users see same variant on return
- Segment sizes: Ensure segments are large enough
- Time of day/week: Balanced exposure
Implementation Approaches
Client-Side Testing
Tools: PostHog, Optimizely, VWO, custom
How it works:
- JavaScript modifies page after load
- Quick to implement
- Can cause flicker
Best for:
- Marketing pages
- Copy/visual changes
- Quick iteration
Server-Side Testing
Tools: PostHog, LaunchDarkly, Split, custom
How it works:
- Variant determined before page renders
- No flicker
- Requires development work
Best for:
- Product features
- Complex changes
- Performance-sensitive pages
Feature Flags
- Binary on/off (not true A/B)
- Good for rollouts
- Can convert to A/B with percentage split
Running the Test
Pre-Launch Checklist
During the Test
DO:
- Monitor for technical issues
- Check segment quality
- Document any external factors
DON'T:
- Peek at results and stop early
- Make changes to variants
- Add traffic from new sources
- End early because you "know" the answer
Peeking Problem
Looking at results before reaching sample size and stopping when you see significance leads to:
- False positives
- Inflated effect sizes
- Wrong decisions
Solutions:
- Pre-commit to sample size and stick to it
- Use sequential testing if you must peek
- 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
Practical Significance
Statistical ≠ Practical
- Is the effect size meaningful for business?
- Is it worth the implementation cost?
- Is it sustainable over time?
What to Look At
-
Did you reach sample size?
- If not, result is preliminary
-
Is it statistically significant?
- Check confidence intervals
- Check p-value
-
Is the effect size meaningful?
- Compare to your MDE
- Project business impact
-
Are secondary metrics consistent?
- Do they support the primary?
- Any unexpected effects?
-
Any guardrail concerns?
- Did anything get worse?
- Long-term risks?
-
Segment differences?
- Mobile vs. desktop?
- New vs. returning?
- Traffic source?
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 |
Documenting and Learning
Test Documentation
Test Name: [Name]
Test ID: [ID in testing tool]
Dates: [Start] - [End]
Owner: [Name]
Hypothesis:
[Full hypothesis statement]
Variants:
- Control: [Description + screenshot]
- Variant: [Description + screenshot]
Results:
- Sample size: [achieved vs. target]
- Primary metric: [control] vs. [variant] ([% change], [confidence])
- Secondary metrics: [summary]
- Segment insights: [notable differences]
Decision: [Winner/Loser/Inconclusive]
Action: [What we're doing]
Learnings:
[What we learned, what to test next]
Building a Learning Repository
- Central location for all tests
- Searchable by page, element, outcome
- Prevents re-running failed tests
- Builds institutional knowledge
Output Format
Test Plan Document
# A/B Test: [Name]
## Hypothesis
[Full hypothesis using framework]
## Test Design
- Type: A/B / A/B/n / MVT
- Duration: X weeks
- Sample size: X per variant
- Traffic allocation: 50/50
## Variants
[Control and variant descriptions with visuals]
## Metrics
- Primary: [metric and definition]
- Secondary: [list]
- Guardrails: [list]
## Implementation
- Method: Client-side / Server-side
- Tool: [Tool name]
- Dev requirements: [If any]
## Analysis Plan
- Success criteria: [What constitutes a win]
- Segment analysis: [Planned segments]
Results Summary
When test is complete
Recommendations
Next steps based on results
Common Mistakes
Test Design
- Testing too small a change (undetectable)
- Testing too many things (can't isolate)
- No clear hypothesis
- Wrong audience
Execution
- Stopping early
- Changing things mid-test
- Not checking implementation
- Uneven traffic allocation
Analysis
- Ignoring confidence intervals
- Cherry-picking segments
- Over-interpreting inconclusive results
- Not considering practical significance
Questions to Ask
If you need more context:
- 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
- page-cro: For generating test ideas based on CRO principles
- analytics-tracking: For setting up test measurement
- copywriting: For creating variant copy
Credits & Attribution
This skill is based on the excellent work by
AITYTech.
Original repository: https://github.com/aitytech/agentkits-marketing
Special thanks to AITYTech for their generous open-source contributions, which helped shape this skill collection.
Adapted by webconsulting.at for this skill collection