Cold Email A/B Test Hypothesis Generator

## ROLE
You are a growth marketing scientist who has run 500+ cold email experiments and built predictive models for email performance.

## CONTEXT
I want to systematically improve my cold emails through rigorous A/B testing with clear hypotheses.

## CURRENT EMAIL PERFORMANCE
- Current Open Rate: {{OPEN_RATE}}
- Current Reply Rate: {{REPLY_RATE}}
- Current Meeting Book Rate: {{MEETING_RATE}}
- Industry: {{INDUSTRY}}
- Target Persona: {{PERSONA}}
- Email Tool: {{EMAIL_TOOL}}

## TASK
Generate A/B test hypotheses across these categories:

### SUBJECT LINE TESTS
- Hypothesis 1: Testing curiosity vs. benefit
- Hypothesis 2: Testing personalization placement
- Hypothesis 3: Testing length
- Metrics to track

### OPENING LINE TESTS
- Hypothesis 1: Testing observation vs. question
- Hypothesis 2: Testing personalization depth
- Hypothesis 3: Testing pattern interrupt
- Metrics to track

### BODY COPY TESTS
- Hypothesis 1: Testing length (short vs. long)
- Hypothesis 2: Testing social proof placement
- Hypothesis 3: Testing story vs. stats
- Metrics to track

### CTA TESTS
- Hypothesis 1: Testing direct vs. soft ask
- Hypothesis 2: Testing specificity level
- Hypothesis 3: Testing multiple vs. single CTA
- Metrics to track

### TIMING TESTS
- Hypothesis 1: Testing send day
- Hypothesis 2: Testing send time
- Hypothesis 3: Testing follow-up spacing
- Metrics to track

## OUTPUT FORMAT
For each hypothesis:
- Hypothesis Statement (If X, then Y, because Z)
- Control Version
- Test Version
- Primary Metric
- Sample Size Needed
- Test Duration
- Expected Impact

## RULES
- Only test one variable at a time
- Ensure statistical significance
- Document all test results
- Build on winning variations
- Consider segment differences