Create a RICE Feature Prioritization Matrix for Product Development
Build a RICE scoring matrix to rank and prioritize product features with calculated scores, sensitivity analysis, and recommendations.
📝 The Prompt
Act as a product management expert skilled in quantitative prioritization frameworks. Help me build a comprehensive RICE (Reach, Impact, Confidence, Effort) scoring matrix to prioritize the feature backlog for [PRODUCT NAME], a [PRODUCT DESCRIPTION] targeting [TARGET AUDIENCE].
Here is my current feature backlog to evaluate:
1. [FEATURE 1 — brief description]
2. [FEATURE 2 — brief description]
3. [FEATURE 3 — brief description]
4. [FEATURE 4 — brief description]
5. [FEATURE 5 — brief description]
6. [FEATURE 6 — brief description]
7. [FEATURE 7 — brief description]
8. [FEATURE 8 — brief description]
For each feature, please:
**Step 1: Define Scoring Criteria**
- **Reach**: Estimate the number of users/customers affected per [TIME PERIOD, e.g., quarter]. Explain your reasoning based on [TOTAL USER BASE] active users.
- **Impact**: Score on a scale (3 = massive, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal) based on contribution to [PRIMARY METRIC, e.g., conversion rate, retention, revenue].
- **Confidence**: Assign a percentage (100% = high confidence backed by data, 80% = moderate with some evidence, 50% = low confidence/gut feel). Specify what evidence supports each score.
- **Effort**: Estimate in person-months of engineering, design, and QA work combined.
**Step 2: Calculate RICE Scores**
Apply the formula: RICE = (Reach × Impact × Confidence) / Effort. Present results in a ranked table.
**Step 3: Analysis & Recommendations**
- Identify the top 3 features to build next and explain why
- Flag any features where confidence is below 60% and recommend validation experiments
- Highlight any high-effort features that could be broken into smaller deliverables for incremental value
- Note potential dependencies between features
**Step 4: Sensitivity Check**
Show how rankings change if Impact or Confidence scores shift by one level for the top 5 features.
Format everything in clean tables with a final executive summary paragraph.
💡 Tips for Better Results
Be brutally honest with Confidence scores — inflated confidence is the most common RICE mistake and leads to misallocation of engineering resources. Break large features into smaller shippable increments to reduce Effort scores and deliver value faster. Run the RICE exercise collaboratively with engineering, design, and customer-facing teams to reduce bias from any single perspective.
🎯 Use Cases
Product managers and engineering leads who need a data-driven, defensible method to prioritize their feature backlog and communicate decisions to stakeholders.