Score and rank your product backlog using the proven RICE framework. Calculate Reach × Impact × Confidence ÷ Effort for data-driven prioritization.
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Calculate the RICE score for your feature or initiative
How many users will this impact?
How much will this impact each user?
How confident are you in your estimates?
How much effort will this require?
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Step-by-step guide to get accurate results with RICE Prioritization Calculator
Start by adding all the features or initiatives you're considering. Be specific about what each feature accomplishes.
Pro Tip: Keep feature descriptions concise but clear - aim for one sentence that explains the user benefit
Estimate how many users will be affected by this feature within a given time period. Use your analytics data for accuracy.
Pro Tip: Be consistent with your time frame - if using 'monthly active users' for one feature, use it for all
How much will this feature impact each user? Consider the magnitude of the improvement to the user experience.
Pro Tip: Use a consistent scale: 3=massive impact, 2=high impact, 1=medium impact, 0.5=low impact, 0.25=minimal
How confident are you in your Reach and Impact estimates? Base this on research, data, and past experience.
Pro Tip: High confidence (80-100%) = solid data. Medium (50-80%) = some evidence. Low (<50%) = mostly assumptions
How much work will this take? Consider design, development, testing, and rollout time in 'person-months'.
Pro Tip: Include all team members' time, not just developers. Factor in coordination overhead for large projects
The RICE score automatically calculates. Review the ranking and adjust scores if the results don't match your intuition.
Pro Tip: If something feels wrong, revisit your assumptions. RICE is a tool to guide decisions, not replace judgment
The RICE prioritization framework is a proven product management methodology developed by Intercom for scoring and ranking product features. RICE stands for Reach (how many users affected), Impact (benefit magnitude per user), Confidence (certainty in estimates), and Effort (resources required). The formula (Reach × Impact × Confidence) ÷ Effort produces a quantitative score that enables objective feature prioritization decisions. This systematic approach replaces subjective discussions with data-driven rankings.
RICE framework eliminates prioritization bias and political decision-making in product teams. Research shows that teams using structured prioritization frameworks like RICE ship 40% more valuable features compared to those relying on intuition. The framework forces product managers to quantify assumptions, leading to better resource allocation and stakeholder alignment. Companies like Intercom, Airbnb, and Shopify use RICE to maintain focus on high-impact initiatives while minimizing wasted development effort.
Start by listing all potential features or initiatives. Score each item on four dimensions: Reach (1-10 scale based on user numbers), Impact (0.25-3 scale for benefit magnitude), Confidence (percentage converted to decimal), and Effort (person-months required). The calculator automatically computes RICE scores using the formula. Higher scores indicate better ROI features that should be prioritized first in your product roadmap.
RICE stands for Reach, Impact, Confidence, and Effort. It's a prioritization framework developed by Intercom that helps product managers make data-driven decisions about which features to build first by scoring each factor and calculating (Reach × Impact × Confidence) ÷ Effort. The framework is used by companies like Airbnb, Shopify, and hundreds of product teams globally.
Reach represents how many users will be affected by the feature within a specific time period (usually per quarter or month). Use actual user data when possible: DAU, MAU, or specific user segments. For example, if 1,000 monthly active users would use this feature, and your total MAU is 10,000, score Reach as 10% of your user base. Consistent time periods are crucial for fair comparison.
Reach measures quantity (how many users affected), while Impact measures intensity (how much each user benefits). Use a consistent scale: 3 = massive impact (transforms user experience), 2 = high impact (significant improvement), 1 = medium impact (noticeable improvement), 0.5 = low impact (minor improvement), 0.25 = minimal impact (barely noticeable).
RICE vs ICE: RICE adds 'Reach' consideration, better for user-centric decisions. RICE vs Value/Effort: RICE breaks down 'value' into Reach and Impact components. RICE vs Kano: RICE is quantitative, Kano is qualitative for user satisfaction. RICE vs MoSCoW: RICE provides numerical ranking, MoSCoW gives categorical priorities. RICE works best for feature prioritization with measurable user impact.
RICE scores are relative to your specific context, but general patterns emerge: Scores >100 are typically high-priority features worth immediate attention. Scores 50-100 are good candidates for next quarter. Scores 20-50 are medium priority for future consideration. Scores <20 are usually low priority unless strategically critical. Most successful features score between 25-200 in well-calibrated systems.
RICE works best for comparing similar types of features or initiatives with measurable user impact. Don't use RICE for: Technical debt (use separate frameworks), Strategic compliance requirements, Urgent bug fixes, Platform decisions. Use RICE for: New features, Product improvements, Growth initiatives, User experience enhancements. Combine RICE with strategic judgment for best results.
Update RICE scores monthly during roadmap planning or when significant new data becomes available. Triggers for updates include: New user research findings, Analytics data changes, Market condition shifts, Technical complexity revelations, Resource availability changes. Set calendar reminders for quarterly full reviews and monthly spot updates of high-priority items.
Start with calibration sessions where the team scores 5-10 sample features together, discussing rationale for each score. Create scoring guidelines specific to your product and user base. Hold regular 'RICE review' meetings to discuss scores and ensure consistency. Use voting or averaging when team members disagree significantly. Document scoring decisions for future reference and onboarding new team members.