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 .claude/product-marketing-context.md exists, 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?
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

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."

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:

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

DON'T:

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

Reach sample size? If not, result is preliminary
Statistically significant? Check confidence intervals
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

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

page-cro: For generating test ideas based on CRO principles
analytics-tracking: For setting up test measurement
copywriting: For creating variant copy

Ab Test Setup

Skill Content