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Marketing

A/B Test Hypothesis Generator

Optimized for: general • TEXT
You are a conversion rate optimization (CRO) expert. Generate structured A/B test hypotheses for the described page or funnel.

**Page/Funnel to Optimize:** [URL or description]
**Current Conversion Rate:** [X%]
**Traffic Volume:** [Monthly visitors]
**Primary Conversion Goal:** [Sign up / Purchase / Click / Download]
**Known Pain Points:** [User feedback, heatmap insights, analytics data]

**For each hypothesis, provide:**

1. **Hypothesis Statement** (use this format):
   "If we [CHANGE], then [METRIC] will [INCREASE/DECREASE] by [ESTIMATED %], because [REASONING BASED ON DATA/PSYCHOLOGY]."

2. **Test Details:**
   - Element to change: [Button, headline, layout, form, pricing, image, copy]
   - Control (A): Current version description
   - Variant (B): Proposed change description
   - Variant (C): Alternative change (optional)

3. **Prioritization Score** (ICE Framework):
   - Impact (1-10): How much improvement if the hypothesis is correct
   - Confidence (1-10): How confident are you based on data and best practices
   - Ease (1-10): How easy is it to implement
   - ICE Score: Impact x Confidence x Ease

4. **Statistical Requirements:**
   - Minimum sample size per variant
   - Estimated test duration based on traffic
   - Minimum detectable effect (MDE)
   - Statistical significance threshold (typically 95%)

5. **Behavioral Psychology Principle:**
   - Which principle supports this change (anchoring, social proof, loss aversion, cognitive load reduction, Hick's Law, etc.)

**Generate 10 test hypotheses, ranked by ICE score.** Include a mix of quick wins (high ease) and high-impact changes. For each, explain the behavioral science principle at work.

**Output**: Prioritized list ready to load into an A/B testing tool (Optimizely, VWO, Google Optimize).

Conversion rate optimization, experiment planning, data-driven marketing decisions

Business

A/B Test Hypothesis Builder

Optimized for: general • TEXT
Create structured A/B test hypothesis:

**Test Subject**: [PAGE/FEATURE]
**Current Metric**: [VALUE]

**Hypothesis**: If [CHANGE] then [EXPECTED OUTCOME] because [REASONING]

**Control (A)**: Current version
**Variant (B)**: Modified version with [SPECIFIC CHANGES]

**Success Metrics**:
- Primary: [METRIC]
- Secondary: [METRICS]

**Sample Size**: Calculate with 95% confidence, 80% power
**Duration**: Minimum [X] days
**Segment**: [ALL/SPECIFIC]

**Risks**: [POTENTIAL ISSUES]
**Rollback Plan**: [IF NEGATIVE IMPACT]

Conversion optimization, product testing, data-driven decisions

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