RICE Prioritization Template
Score and rank product features using Reach, Impact, Confidence, and Effort. This template includes a ready-to-use scoring table, guidance for each dimension, and practical tips for avoiding the most common RICE scoring mistakes.
Why product teams use RICE prioritization
Every product team faces the same problem: more ideas than capacity. Without a structured way to compare features, prioritization defaults to whoever argues loudest or whichever stakeholder has the most political weight. RICE replaces opinion with a repeatable scoring system.
Developed by Intercom, RICE works because it forces you to quantify four things that matter: how many users benefit, how much they benefit, how confident you are in those estimates, and how much work it takes. The resulting score gives you a single number to compare features against each other. It does not make the decision for you, but it makes the trade-offs visible.
RICE is particularly effective for teams with 10-50 items on their backlog. For smaller lists, a quick stack rank may be sufficient. For larger lists, you may want to first filter with MoSCoW categories and then RICE score the “Must” and “Should” items to determine build order.
RICE scoring table
Score each feature across all four dimensions. The RICE score = (Reach x Impact x Confidence) / Effort.
| Feature | Reach | Impact | Confidence | Effort | RICE Score |
|---|---|---|---|---|---|
| Example: In-app onboarding tooltips | 5,000 users/quarter | 2 (High) | 80% | 3 person-months | 2,667 |
| Example: CSV bulk import | 800 users/quarter | 3 (Massive) | 60% | 5 person-months | 288 |
| Example: Dark mode | 12,000 users/quarter | 0.5 (Low) | 90% | 2 person-months | 2,700 |
| [Your feature] | [Users affected per quarter] | [0.25 / 0.5 / 1 / 2 / 3] | [50-100%] | [Person-months] | (R x I x C) / E |
How to score each dimension
Each RICE dimension has specific guidelines. Follow these to keep scores consistent across your team.
Reach
How many users or customers will this feature affect in a given time period? Use a concrete number, not a percentage. Measure reach per quarter for consistency. Pull this number from your analytics tool, not from gut feeling.
Example: "This feature will affect 5,000 users per quarter based on the number of users who currently visit the onboarding screen and drop off before completing setup."
Tips
- Use a fixed time window (per quarter or per month)
- Pull the number from analytics, not estimates
- Count unique users, not sessions or pageviews
- Segment by the specific user group affected
Impact
How much will this feature move the needle for each user it reaches? Use a standardized scale: 3 = massive impact, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal. Be honest about the difference between "nice to have" and "changes behavior."
Example: "Impact score: 2 (High). Users who complete onboarding are 3.2x more likely to convert to paid. Reducing onboarding friction directly affects our primary conversion metric."
Tips
- Use the standard 0.25 / 0.5 / 1 / 2 / 3 scale
- Tie impact to a measurable outcome, not a feeling
- Consider both direct and indirect effects
- Ask: would a user notice if this shipped tomorrow?
Confidence
How confident are you in your Reach and Impact estimates? Express as a percentage. 100% means you have strong data. 80% means you have some data and reasonable assumptions. 50% means it is mostly a guess. Low confidence scores penalize features where the upside is uncertain.
Example: "Confidence: 80%. We have analytics data showing the drop-off rate and a customer interview series (n=12) confirming the pain point. We are less confident about the exact conversion lift."
Tips
- Be honest: most estimates deserve 50-80%, not 100%
- Data-backed estimates get higher confidence than gut feel
- Customer research increases confidence
- Prototype or A/B test results push confidence above 80%
Effort
How many person-months will this feature take to build? Include design, engineering, QA, and any cross-team coordination. Effort is the denominator in the RICE formula, so higher effort lowers the score. Be realistic about scope creep and hidden complexity.
Example: "Effort: 3 person-months. 1 month design, 1.5 months engineering, 0.5 months QA and launch. Assumes one designer and two engineers."
Tips
- Include all functions: design, engineering, QA, PM
- Use person-months, not calendar months
- Account for dependencies and coordination overhead
- Add 20-30% buffer for scope creep and unknowns
Related templates
MoSCoW Prioritization Template
Categorize features into Must, Should, Could, and Will Not for clearer scope decisions.
View template →Impact-Effort Matrix Template
Plot features on a 2x2 grid to find quick wins and avoid time sinks.
View template →Feature Prioritization Template
A general framework for ranking and comparing product features.
View template →Frequently asked questions
Prioritize features with data, not gut feel
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