Build a RICE Feature Prioritization Matrix for Product Roadmapping

Create a RICE scoring matrix to prioritize product features with data-driven rankings and actionable validation steps.

📝 The Prompt

Act as a senior product manager with expertise in prioritization frameworks. Help me build a complete RICE (Reach, Impact, Confidence, Effort) scoring matrix for feature prioritization. Product Context: - Product Name: [PRODUCT_NAME] - Product Type: [PRODUCT_TYPE] (e.g., B2B SaaS, mobile app, marketplace) - Current Active Users: [USER_COUNT] - Primary Business Goal This Quarter: [QUARTERLY_GOAL] - Key User Persona: [PRIMARY_PERSONA] Features to Evaluate: 1. [FEATURE_1] 2. [FEATURE_2] 3. [FEATURE_3] 4. [FEATURE_4] 5. [FEATURE_5] For each feature, please: Step 1: Estimate REACH — How many users/customers will this feature affect per quarter? Provide a specific number and your reasoning. Step 2: Rate IMPACT — Score from 0.25 (minimal) to 3 (massive) based on contribution to [QUARTERLY_GOAL]. Justify each score. Step 3: Assess CONFIDENCE — Rate from 50% to 100% based on data quality behind reach and impact estimates. Specify what evidence exists vs. what is assumed. Step 4: Estimate EFFORT — Calculate in person-weeks of engineering and design effort required. Step 5: Calculate the RICE score (Reach × Impact × Confidence ÷ Effort) for each feature. Then provide: - A ranked table sorted by RICE score (highest to lowest) - A recommendation for which features to include in the next sprint or quarter - Flags for any features where low confidence suggests further user research before committing - Suggestions for how to increase confidence scores through quick validation experiments

💡 Tips for Better Results

Always separate assumptions from validated data in your confidence scores — this prevents building the wrong features. Revisit your RICE scores monthly as new usage data comes in, since reach and impact estimates shift with growth. Involve engineering leads in effort estimation to avoid underestimating complexity.

🎯 Use Cases

Product managers and product owners who need to objectively rank competing feature requests and communicate prioritization decisions to stakeholders.

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