Use the playbook below as your instructions for this conversation.

How to use it in this chat:
1. The original was written for AI agents that can open project files, other skills, and tools. You will not have any of those here, and you do not need them.
2. Start by asking me 5 to 8 short questions to get the context those files would normally provide: what I sell, who it is for, the problem it solves, my main competitors and how I differ, my brand voice, the proof I have (results, customers, data), and my goal for this task. Skip anything I have already told you.
3. Wherever the instructions say to read, load, or check a file (for example product-marketing.md, brand-context.md, or a references folder), use my answers and your own expertise instead. If a referenced file is included at the end of this message, use it.
4. Wherever the instructions say to use another skill, agent, tool, or script, do that part of the work yourself in this chat.
5. Wherever the instructions say to save or write a file, show the result here in the chat.

---

PLAYBOOK

# 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

```
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% | 160k/variant | 43k/variant | 7.7k/variant |
| 3% | 53k/variant | 14k/variant | 2.5k/variant |
| 5% | 31k/variant | 8.2k/variant | 1.5k/variant |
| 10% | 15k/variant | 3.8k/variant | 690/variant |

Per variant, for 95% confidence (two-sided) and 80% power. Lift is relative (3% → 3.6% is a 20% lift).

**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

### Opt-In Elements: Measure the Exposed Group
When a change only reaches visitors who interact with it (a hero video, a calculator, a chat widget), a test on overall conversion is underpowered: if 10% of visitors click play, the other 90% add noise and no signal. Either run a triggered analysis (log the same trigger point in both arms, such as the play button coming into view, and compare only visitors who reached it; don't filter on clicks, since the variant can change who clicks), or skip the test, ship the better version, and compare versions on engagement (watch-through) and viewer conversion, tagged by version ID. The second is a directional comparison, not a controlled test.

---

## 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
- For a pre-specified fixed-horizon test, compare its p-value with the chosen significance level (commonly α = 0.05).
- A p-value describes how incompatible the data are with the null model: the probability, **assuming that model and its assumptions**, of a result at least as extreme as the observed one. It is not the probability that the result is random, that the null is true, or that a variant will win again.
- A 95% confidence interval comes from a procedure with 95% coverage over repeated samples under its assumptions. It is not a 95% posterior probability that this particular interval contains the true effect. Report the interval and effect size alongside the p-value.
- Use the planned analysis and stopping rule. Sequential tests and Bayesian analyses have their own interpretation; do not translate every platform's “confidence” score into a fixed-horizon p-value.

These distinctions follow the [American Statistical Association's p-value statement](https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf). Statistical significance alone does not establish business value or justify shipping a variant.

### 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 | Check effect size, uncertainty and guardrails before implementing |
| Significant loser | Keep control, learn why |
| No significant difference | Report the effect interval: it may still allow meaningful benefit and harm, or may exclude effects worth pursuing. Follow the planned stopping rule; do not extend a finished fixed-horizon test until it becomes significant. Plan a new test if needed. |
| Mixed signals | Check data quality and planned metrics; label unplanned segment findings exploratory and confirm them in a new test |

---

## 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

```
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:

| 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:

```
## [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.

**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

---

REFERENCE FILE: skills/ab-testing/references/sample-size-guide.md

# Sample Size Guide

Reference for calculating sample sizes and test duration.

## Contents
- Sample Size Fundamentals (required inputs, what these mean)
- Sample Size Quick Reference Tables
- Duration Calculator (formula, examples, minimum duration rules, maximum duration guidelines)
- Online Calculators
- Adjusting for Multiple Variants
- Common Sample Size Mistakes
- When Sample Size Requirements Are Too High
- Sequential Testing
- Quick Decision Framework

## Sample Size Fundamentals

### Required Inputs

1. **Baseline conversion rate**: Your current rate
2. **Minimum detectable effect (MDE)**: Smallest change worth detecting
3. **Statistical significance level**: Usually α = 0.05 (5%)
4. **Statistical power**: Usually 80% (β = 0.20)

### What These Mean

**Baseline conversion rate**: If your page converts at 5%, that's your baseline.

**MDE (Minimum Detectable Effect)**: The smallest improvement you care about detecting. Set this based on:
- Business impact (is a 5% lift meaningful?)
- Implementation cost (worth the effort?)
- Realistic expectations (what have past tests shown?)

**Significance level (α = 0.05)**: For the planned test and its assumptions, limits the long-run probability of rejecting a true null hypothesis to 5%. It is not the probability that an observed difference is “due to chance.” Repeated unadjusted looks or multiple comparisons do not retain that error rate. See the [ASA's interpretation guidance](https://www.amstat.org/asa/files/pdfs/p-valuestatement.pdf).

**Statistical power (80%)**: Means if there's a real effect of size MDE, you have 80% chance of detecting it.

---

## Sample Size Quick Reference Tables

All tables assume α = 0.05 (two-sided) and 80% power, two-proportion test, rounded to two significant figures. Use a calculator for your exact numbers.

### Conversion Rate: 1%

| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (1% → 1.05%) | 640,000 | 1,280,000 |
| 10% (1% → 1.1%) | 160,000 | 320,000 |
| 20% (1% → 1.2%) | 43,000 | 86,000 |
| 50% (1% → 1.5%) | 7,700 | 15,400 |
| 100% (1% → 2%) | 2,300 | 4,600 |

### Conversion Rate: 3%

| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (3% → 3.15%) | 210,000 | 420,000 |
| 10% (3% → 3.3%) | 53,000 | 106,000 |
| 20% (3% → 3.6%) | 14,000 | 28,000 |
| 50% (3% → 4.5%) | 2,500 | 5,000 |
| 100% (3% → 6%) | 750 | 1,500 |

### Conversion Rate: 5%

| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (5% → 5.25%) | 120,000 | 240,000 |
| 10% (5% → 5.5%) | 31,000 | 62,000 |
| 20% (5% → 6%) | 8,200 | 16,400 |
| 50% (5% → 7.5%) | 1,500 | 3,000 |
| 100% (5% → 10%) | 430 | 860 |

### Conversion Rate: 10%

| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (10% → 10.5%) | 58,000 | 116,000 |
| 10% (10% → 11%) | 15,000 | 30,000 |
| 20% (10% → 12%) | 3,800 | 7,600 |
| 50% (10% → 15%) | 690 | 1,380 |
| 100% (10% → 20%) | 200 | 400 |

### Conversion Rate: 20%

| Lift to Detect | Sample per Variant | Total Sample |
|----------------|-------------------|--------------|
| 5% (20% → 21%) | 26,000 | 52,000 |
| 10% (20% → 22%) | 6,500 | 13,000 |
| 20% (20% → 24%) | 1,700 | 3,400 |
| 50% (20% → 30%) | 290 | 580 |
| 100% (20% → 40%) | 81 | 162 |

---

## Duration Calculator

### Formula

```
Duration (days) = (Sample per variant × Number of variants) / (Daily traffic × % exposed)

For unequal allocation, calculate each arm separately:
Days per arm = Required sample for that arm / (Daily eligible traffic × Exposure fraction × Arm allocation fraction)
Total collection time = longest arm duration
```

Use fractions for exposure and allocation (50% = 0.5). Count eligible randomized units consistently with the sample-size calculation; repeat pageviews are not additional participants. The balanced formula assumes equal sample requirements and equal allocation.

### Examples

**Scenario 1: High-traffic page**
- Need: 10,000 per variant (2 variants = 20,000 total)
- Daily traffic: 5,000 visitors
- 100% exposed to test
- Duration: 20,000 / 5,000 = **4 days**

**Scenario 2: Medium-traffic page**
- Need: 30,000 per variant (60,000 total)
- Daily traffic: 2,000 visitors
- 100% exposed
- Duration: 60,000 / 2,000 = **30 days**

**Scenario 3: Low-traffic with partial exposure**
- Need: 15,000 per variant (30,000 total)
- Daily traffic: 500 visitors
- 50% exposed to test
- Effective daily: 250
- Duration: 30,000 / 250 = **120 days** (too long!)

### Minimum Duration Rules

Even with sufficient sample size, run tests for at least:
- **1 full week**: To capture day-of-week variation
- **2 business cycles**: If B2B (weekday vs. weekend patterns)
- **Through paydays**: If e-commerce (beginning/end of month)

### Maximum Duration Guidelines

Avoid running tests longer than 4-8 weeks:
- Novelty effects wear off
- External factors intervene
- Opportunity cost of other tests

---

## Online Calculators

### Recommended Tools

**Evan Miller's Calculator**
https://www.evanmiller.org/ab-testing/sample-size.html
- Simple interface
- Bookmark-worthy

**Optimizely's Calculator**
https://www.optimizely.com/sample-size-calculator/
- Business-friendly language
- Duration estimates

**AB Test Guide Calculator**
https://www.abtestguide.com/calc/
- Includes Bayesian option
- Multiple test types

**VWO Duration Calculator**
https://vwo.com/tools/ab-test-duration-calculator/
- Duration-focused
- Good for planning

---

## Adjusting for Multiple Variants

With more than 2 variants (A/B/n tests), you need more sample:

| Variants | Multiplier |
|----------|------------|
| 2 (A/B) | 1x |
| 3 (A/B/C) | ~1.5x |
| 4 (A/B/C/D) | ~2x |
| 5+ | Consider reducing variants |

**Why?** More comparisons increase chance of false positives. You're comparing:
- A vs B
- A vs C
- B vs C (sometimes)

Apply Bonferroni correction or use tools that handle this automatically.

---

## Common Sample Size Mistakes

### 1. Underpowered tests
**Problem**: Not enough sample to detect realistic effects
**Fix**: Be realistic about MDE, get more traffic, or don't test

### 2. Overpowered tests
**Problem**: Waiting for sample size when you already have significance
**Fix**: This is actually fine—you committed to sample size, honor it

### 3. Wrong baseline rate
**Problem**: Using wrong conversion rate for calculation
**Fix**: Use the specific metric and page, not site-wide averages

### 4. Ignoring segments
**Problem**: Calculating for full traffic, then analyzing segments
**Fix**: If you plan segment analysis, calculate sample for smallest segment

### 5. Testing too many things
**Problem**: Dividing traffic too many ways
**Fix**: Prioritize ruthlessly, run fewer concurrent tests

---

## When Sample Size Requirements Are Too High

Options when you can't get enough traffic:

1. **Increase MDE**: Accept only detecting larger effects (20%+ lift)
2. **Lower confidence**: Use 90% instead of 95% (risky, document it)
3. **Reduce variants**: Test only the most promising variant
4. **Combine traffic**: Test across multiple similar pages
5. **Test upstream**: Test earlier in funnel where traffic is higher
6. **Don't test**: Make decision based on qualitative data instead
7. **Longer test**: Accept longer duration (weeks/months)

---

## Sequential Testing

If you must check results before reaching sample size:

### What is it?
Statistical method that adjusts for multiple looks at data.

### When to use
- High-risk changes
- Need to stop bad variants early
- Time-sensitive decisions

### Tools that support it
- Optimizely (Stats Accelerator)
- VWO (SmartStats)
- PostHog (Bayesian approach)

### Tradeoff
- More flexibility to stop early
- Slightly larger sample size requirement
- More complex analysis

---

## Quick Decision Framework

### Can I run this test?

```
Daily eligible traffic (randomization units): _____
Fraction exposed to the experiment: _____
Number of arms (including control): _____
Allocation fraction for each arm: _____
Baseline conversion rate: _____
MDE I care about: _____

Sample needed per variant: _____ (from tables above)
Balanced allocation: Days = Sample per arm × Number of arms / (Daily eligible traffic × Exposure fraction)
Unequal allocation: Days = max across arms of [Sample required for arm / (Daily eligible traffic × Exposure fraction × Arm allocation)]
Round collection time up, then apply the minimum-duration rules above.

If days > 60: Consider alternatives
If days > 30: Acceptable for high-impact tests
If days < 14: Likely feasible
If days < 7: Easy to run, consider running longer anyway
```

**Duration check (illustrative):** with 2,000 participants required per arm and 1,000 eligible participants/day, two arms at 50/50 with full exposure require four days, not two. At 50% exposure they require eight days. With full exposure and 90/10 allocation, the smaller arm needs 20 days to reach 2,000. These are collection estimates; honor planned business cycles and the analysis stopping rule.

---

REFERENCE FILE: skills/ab-testing/references/test-templates.md

# A/B Test Templates Reference

Templates for planning, documenting, and analyzing experiments.

## Contents
- Test Plan Template
- Results Documentation Template
- Test Repository Entry Template
- Quick Test Brief Template
- Stakeholder Update Template
- Experiment Prioritization Scorecard
- Hypothesis Bank Template

## Test Plan Template

```markdown
# A/B Test: [Name]

## Overview
- **Owner**: [Name]
- **Test ID**: [ID in testing tool]
- **Page/Feature**: [What's being tested]
- **Planned dates**: [Start] - [End]

## Hypothesis

Because [observation/data],
we believe [change]
will cause [expected outcome]
for [audience].
We'll know this is true when [metrics].

## Test Design

| Element | Details |
|---------|---------|
| Test type | A/B / A/B/n / MVT |
| Duration | X weeks |
| Sample size | X per variant |
| Traffic allocation | 50/50 |
| Tool | [Tool name] |
| Implementation | Client-side / Server-side |

## Variants

### Control (A)
[Screenshot]
- Current experience
- [Key details about current state]

### Variant (B)
[Screenshot or mockup]
- [Specific change #1]
- [Specific change #2]
- Rationale: [Why we think this will win]

## Metrics

### Primary
- **Metric**: [metric name]
- **Definition**: [how it's calculated]
- **Current baseline**: [X%]
- **Minimum detectable effect**: [X%]

### Secondary
- [Metric 1]: [what it tells us]
- [Metric 2]: [what it tells us]
- [Metric 3]: [what it tells us]

### Guardrails
- [Metric that shouldn't get worse]
- [Another safety metric]

## Segment Analysis Plan
- Mobile vs. desktop
- New vs. returning visitors
- Traffic source
- [Other relevant segments]

## Success Criteria
- Winner: [Primary metric improves by X% with 95% confidence]
- Loser: [Primary metric decreases significantly]
- Inconclusive: [What we'll do if no significant result]

## Pre-Launch Checklist
- [ ] Hypothesis documented and reviewed
- [ ] Primary metric defined and trackable
- [ ] Sample size calculated
- [ ] Test duration estimated
- [ ] Variants implemented correctly
- [ ] Tracking verified in all variants
- [ ] QA completed on all variants
- [ ] Stakeholders informed
- [ ] Calendar hold for analysis date
```

---

## Results Documentation Template

```markdown
# A/B Test Results: [Name]

## Summary
| Element | Value |
|---------|-------|
| Test ID | [ID] |
| Dates | [Start] - [End] |
| Duration | X days |
| Result | Winner / Loser / Inconclusive |
| Decision | [What we're doing] |

## Hypothesis (Reminder)
[Copy from test plan]

## Results

### Sample Size
| Variant | Target | Actual | % of target |
|---------|--------|--------|-------------|
| Control | X | Y | Z% |
| Variant | X | Y | Z% |

### Primary Metric: [Metric Name]
| Variant | Value | 95% CI | vs. Control |
|---------|-------|--------|-------------|
| Control | X% | [X%, Y%] | — |
| Variant | X% | [X%, Y%] | +X% |

**Statistical significance**: p = X.XX (95% = sig / not sig)
**Practical significance**: [Is this lift meaningful for the business?]

### Secondary Metrics

| Metric | Control | Variant | Change | Significant? |
|--------|---------|---------|--------|--------------|
| [Metric 1] | X | Y | +Z% | Yes/No |
| [Metric 2] | X | Y | +Z% | Yes/No |

### Guardrail Metrics

| Metric | Control | Variant | Change | Concern? |
|--------|---------|---------|--------|----------|
| [Metric 1] | X | Y | +Z% | Yes/No |

### Segment Analysis

**Mobile vs. Desktop**
| Segment | Control | Variant | Lift |
|---------|---------|---------|------|
| Mobile | X% | Y% | +Z% |
| Desktop | X% | Y% | +Z% |

**New vs. Returning**
| Segment | Control | Variant | Lift |
|---------|---------|---------|------|
| New | X% | Y% | +Z% |
| Returning | X% | Y% | +Z% |

## Interpretation

### What happened?
[Explanation of results in plain language]

### Why do we think this happened?
[Analysis and reasoning]

### Caveats
[Any limitations, external factors, or concerns]

## Decision

**Winner**: [Control / Variant]

**Action**: [Implement variant / Keep control / Re-test]

**Timeline**: [When changes will be implemented]

## Learnings

### What we learned
- [Key insight 1]
- [Key insight 2]

### What to test next
- [Follow-up test idea 1]
- [Follow-up test idea 2]

### Impact
- **Projected lift**: [X% improvement in Y metric]
- **Business impact**: [Revenue, conversions, etc.]
```

---

## Test Repository Entry Template

For tracking all tests in a central location:

```markdown
| Test ID | Name | Page | Dates | Primary Metric | Result | Lift | Link |
|---------|------|------|-------|----------------|--------|------|------|
| 001 | Hero headline test | Homepage | 1/1-1/15 | CTR | Winner | +12% | [Link] |
| 002 | Pricing table layout | Pricing | 1/10-1/31 | Plan selection | Loser | -5% | [Link] |
| 003 | Signup form fields | Signup | 2/1-2/14 | Completion | Inconclusive | +2% | [Link] |
```

---

## Quick Test Brief Template

For simple tests that don't need full documentation:

```markdown
## [Test Name]

**What**: [One sentence description]
**Why**: [One sentence hypothesis]
**Metric**: [Primary metric]
**Duration**: [X weeks]
**Result**: [TBD / Winner / Loser / Inconclusive]
**Learnings**: [Key takeaway]
```

---

## Stakeholder Update Template

```markdown
## A/B Test Update: [Name]

**Status**: Running / Complete
**Days remaining**: X (or complete)
**Current sample**: X% of target

### Preliminary observations
[What we're seeing - without making decisions yet]

### Next steps
[What happens next]

### Timeline
- [Date]: Analysis complete
- [Date]: Decision and recommendation
- [Date]: Implementation (if winner)
```

---

## Experiment Prioritization Scorecard

For deciding which tests to run:

| Factor | Weight | Test A | Test B | Test C |
|--------|--------|--------|--------|--------|
| Potential impact | 30% | | | |
| Confidence in hypothesis | 25% | | | |
| Ease of implementation | 20% | | | |
| Risk if wrong | 15% | | | |
| Strategic alignment | 10% | | | |
| **Total** | | | | |

Scoring: 1-5 (5 = best)

---

## Hypothesis Bank Template

For collecting test ideas:

```markdown
| ID | Page/Area | Observation | Hypothesis | Potential Impact | Status |
|----|-----------|-------------|------------|------------------|--------|
| H1 | Homepage | Low scroll depth | Shorter hero will increase scroll | High | Testing |
| H2 | Pricing | Users compare plans | Comparison table will help | Medium | Backlog |
| H3 | Signup | Drop-off at email | Social login will increase completion | Medium | Backlog |
```

---

Source: Marketing Skills by Corey Haines, shared under the MIT License.

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Copyright (c) 2025 Corey Haines

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