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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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