hvac-marketing-skills/skills/ab-test-setup/SKILL.md

---
name: ab-test-setup
description: When the user wants to plan, design, or implement an A/B test or experiment. 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," or "how long should I run this test." Use this whenever someone is comparing two approaches and wants to measure which performs better. For tracking implementation, see analytics-tracking. For page-level conversion optimization, see page-cro.
metadata:
  version: 2.0.0
  compendium:
    mode: standalone
    tools: []
---

# 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/hvac-marketing-context.md` exists (or `.claude/hvac-marketing-context.md` 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 more visible will increase quote request submissions by 15%+ for new visitors. We'll measure quote request conversion rate from page view to form submission."

---

## 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 HVAC example:** If your current quote request rate is 3% and you want to detect a 20% lift, you need 12,000 visitors per variant, or 24,000 total.

---

## 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: Quote Request CTA Test
- **Primary**: Quote request submission rate
- **Secondary**: Click-through rate on CTA, form abandonment rate
- **Guardrail**: Page bounce rate (shouldn't go up), phone calls (shouldn't decrease)

---

## Designing Variants

### What to Vary

| Category | Examples |
|----------|----------|
| Headlines/Copy | Message angle, value prop, urgency, tone |
| CTA | Button copy, size, placement, color |
| Visual Design | Layout, hierarchy, images |
| Form | Fields required, button placement, form length |
| Timing | When CTA appears (immediately vs. after scroll) |

### Best Practices
- Single, meaningful change
- Bold enough to make a difference
- True to the hypothesis

### HVAC-Specific Test Ideas

**Example 1: CTA Copy Test**
- **Control**: "Get Free Quote"
- **Variant**: "Schedule Service Today"
- **Hypothesis**: Specific action language increases urgency and form completion

**Example 2: Urgency Test**
- **Control**: Standard headline
- **Variant**: "Emergency AC Service Available Now"
- **Hypothesis**: Urgency language increases quote requests for emergency services

**Example 3: Form Length Test**
- **Control**: 5-field quote form (Name, Phone, Service type, Issue, Address)
- **Variant**: 3-field form (Name, Phone, Service type)
- **Hypothesis**: Fewer required fields increase form completion rate

---

## 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, confidence intervals)
- Decision and learnings
- Next steps

**Example:**
```markdown
## Test: CTA Copy for Quote Requests

**Hypothesis:** "Schedule Service Today" (action-specific) will increase quote form
submissions more than "Get Free Quote" (generic).

**Duration:** Jan 15-29, 2024
**Sample Size:** 15,000 per variant

**Results:**
- Control: 3.2% conversion rate (485/15,000)
- Variant: 3.8% conversion rate (570/15,000)
- Lift: +18.8%
- Confidence: 97.3% (p=0.004)

**Decision:** Implement variant. Action-specific CTA performed significantly better.

**Learning:** Specificity drives urgency. Test "Call Now" vs "Schedule Today" next.
```

---

## 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

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