RICE Prioritization Framework with Assumption Mapping

Full prompt

You are a product prioritization expert. Help me score and rank features using the RICE framework while making assumptions explicit and testable.

RICE scoring:
- **Reach**: How many users will this affect in a given time period (e.g., 90 days)? Express as a number or percentage of your user base.
- **Impact**: How much will this improve the user experience or business metric for affected users? Use a scale: 3 (massive), 2 (high), 1 (medium), 0.5 (small), 0.25 (minimal).
- **Confidence**: How confident are you in your Reach and Impact estimates? Express as a percentage (100% = certain, 50% = uncertain).
- **Effort**: How many person-weeks of engineering work is required? Be realistic; include design, testing, and deployment.

**RICE Score = (Reach × Impact × Confidence) / Effort**

Here are the features to prioritize:
{{features}}

For each feature, provide:
1. **RICE Score** (calculated)
2. **Reach assumption**: What user segment? What's your evidence? (e.g., "Based on support tickets, 40% of users request this")
3. **Impact assumption**: What metric improves? By how much? (e.g., "Reduces churn by 5% among power users")
4. **Confidence level**: What would increase your confidence? (e.g., "Need user research with 10 power users")
5. **Effort assumption**: What's included? Any unknowns? (e.g., "Assumes no API redesign; excludes mobile")
6. **Risk flag**: Any assumption that could invalidate the score?

Rank features by RICE score. Then identify:
- **Quick wins**: High score, low effort
- **Strategic bets**: High score, high effort, high confidence
- **Validation needed**: High score but low confidence—what's the cheapest test?

Format as a table for easy sharing with stakeholders.

How to use

1. List your features or ideas in the placeholder. 2. Run the prompt. 3. Review the RICE scores and assumption breakdowns. 4. Validate high-confidence assumptions with data (analytics, user research). 5. For low-confidence features, design cheap tests (surveys, prototypes, beta cohorts) before committing effort. 6. Share the output with your team and stakeholders to align on trade-offs.

Customization tips

• Adjust the Reach scale to match your business model. For B2B, use 'number of accounts'; for B2C, use 'percentage of monthly active users'.
• Modify Impact scale if you have domain-specific metrics (e.g., for a marketplace, use 'increase in transaction volume' instead of generic UX improvement).
• Add a 'Urgency' dimension if you have hard deadlines (e.g., compliance, competitive threats). Multiply RICE by an urgency factor (1–1.5).
• Include 'Dependencies' column if features block each other or require shared infrastructure.
• For early-stage products, lower your Confidence threshold (e.g., 40% is acceptable) because data is sparse; focus on learning velocity instead.
• Use this prompt quarterly to re-score as you gather new data. Track which assumptions were correct and refine your estimation accuracy.