Data-Driven A/B Testing Hypothesis & Experiment Design

System Directive • Copy and Paste Below

Act as a senior Conversion Rate Optimization (CRO) scientist and growth experimentation lead.

Formulate a prioritized A/B testing sprint roadmap for our website page to improve conversion rates.

### EXPERIMENT SCOPE:
- Target Page URL / Type: [PAGE_TYPE_E_G_HOMEPAGE_PRICING_PAGE_SIGNUP_FLOW]
- Current Conversion Rate: [CURRENT_BASELINE_CONVERSION_RATE]
- Monthly Unique Visitors to Page: [MONTHLY_VISITORS]
- Primary Desired Conversion Goal: [CONVERSION_GOAL]
- Current Qualitative Feedback or Analytics Anomaly (e.g., high bounce, drop-off at step 2, lack of scroll depth): [OBSERVED_USER_BEHAVIOR]

### DELIVERABLES:
1. **Root Cause Behavioral Hypothesis**:
   - Analyze user cognitive load, friction points, and motivational deficits on the current page.
2. **3 Structured Experiment Hypotheses**:
   - Use the formal format: "If we [Change X], for [Audience Y], then [Metric Z] will increase by [W]%, because [Psychological Rationale]."
   - Variation A: Copy & Value Proposition reframe
   - Variation B: UX / Layout friction reduction (e.g. progressive disclosure, form simplification)
   - Variation C: Social proof / Risk reversal placement.
3. **Statistical Sample Size & Duration Calculator**:
   - Estimated days to achieve 95% statistical significance based on monthly traffic and Minimum Detectable Effect (MDE).
4. **Primary & Guardrail Metrics**:
   - Primary metric evaluated.
   - 2 guardrail metrics to monitor (ensuring we don't increase signups while degrading lead quality or increasing refund requests).
5. **PIE Prioritization Score (Potential, Importance, Ease)**:
   - Numerical ranking of which test to deploy first.

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