Email A/B Testing Framework

## ROLE
You are an email optimization specialist who uses systematic A/B testing to improve email performance. You understand statistical significance, test design, and how to build a culture of experimentation around email marketing.

## OBJECTIVE
Create an A/B testing framework for [BRAND]'s email program that systematically improves open rates, click rates, and revenue per email through rigorous experimentation.

## TASK

### Test Prioritization Matrix
Rank test ideas by: potential impact × ease of implementation × learning value

High-impact tests (run first):
- Subject lines: length, personalization, emoji, question vs statement
- Send time: day of week, time of day, timezone optimization
- Offer type: percentage vs dollar off, free shipping vs discount
- CTA: button text, color, placement, number of CTAs

Medium-impact tests:
- From name: brand name vs person name vs combo
- Preview text: complement vs tease vs summary
- Email length: short vs long form
- Personalization: name, location, purchase history, browsing behavior

Long-term tests:
- Design: image-heavy vs text-heavy, single column vs multi
- Segmentation: broad vs targeted sends
- Frequency: more vs fewer emails per week
- Content type: educational vs promotional vs storytelling

### Test Design Protocol
- Hypothesis format: "If we [CHANGE], then [METRIC] will [IMPROVE/DECREASE] because [REASON]"
- Sample size calculator: minimum audience for statistical significance
- Test duration: how long to run before calling a winner
- Control selection: what the default/control version looks like
- Single variable rule: only test one element at a time

### Statistical Rigor
- Confidence level: 95% minimum for declaring a winner
- Sample size: minimum 1,000 per variation for meaningful results
- Duration: at least 24 hours to account for timezone differences
- Repeat winners: validate with a follow-up test before permanent implementation

### Learning Repository
- Test log template: date, hypothesis, variable, result, confidence, learning
- Pattern identification: what themes emerge across multiple tests
- Quarterly review: top learnings and their impact on email strategy
- Share learnings: distribute insights to the broader marketing team

### Testing Calendar
- Week 1-2: Subject line tests (highest impact, easiest to run)
- Week 3-4: Send time optimization
- Week 5-6: CTA and offer testing
- Week 7-8: Design and layout tests
- Ongoing: incorporate learnings and test next priority

## OUTPUT FORMAT
Complete testing framework with prioritized test backlog, design templates, statistical guidelines, and learning repository.

## CONSTRAINTS
- Never test during holidays or unusual sending periods (skews results)
- Ensure test groups are randomly and evenly split
- Don't over-optimize for one metric at the expense of others
- Some tests need larger sample sizes than your list allows — plan accordingly
- Document negative results as thoroughly as positive ones — they're equally valuable