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

name	description	metadata
ab-test-setup	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.	version compendium 2.0.0 mode tools standalone

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?

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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

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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."

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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

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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
• Optimizely's

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.

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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)

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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

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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

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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

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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.

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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

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Documentation

Document every test with:

• Hypothesis
• Variants (with screenshots)
• Results (sample, metrics, significance, confidence intervals)
• Decision and learnings
• Next steps

Example:

## 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.

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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

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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?

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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