RICE Score Calculator

What is the RICE framework?

The RICE framework is a product prioritization framework used by product managers to rank features and initiatives by expected value per unit of effort. RICE scoring became popular at Intercom in the 2010s and is now one of the most widely adopted prioritization methods in product management, alongside ICE, MoSCoW, and weighted scoring.

RICE stands for Reach, Impact, Confidence, Effort. The four dimensions force you to consider both the upside of an initiative (how many users, how much it moves the metric, how sure you are) and the cost (how much engineering work it takes). The result is a single comparable RICE score you can use to sort a backlog.

The RICE scoring formula

The RICE formula is:

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

That’s it. The numerator captures expected value (how much benefit you expect, weighted by your confidence in the estimate). The denominator captures cost. Higher RICE scores mean higher value per unit of effort, which is what you want when prioritizing a backlog.

The RICE model: Reach, Impact, Confidence, Effort

Reach

Reach measures how many users or customers an initiative will affect over a fixed time period (typically per quarter). Use absolute numbers, not percentages. If a feature will be used by 5,000 users per quarter, Reach = 5,000.

Pull Reach from your analytics, not your gut. Common sources: monthly active users for the affected segment, conversion funnel volume, or customer count for B2B features.

Impact

Impact measures how much the initiative will move your goal metric for each user it reaches. Most teams use a 5-point scale: 0.25 (minimal), 0.5 (low), 1 (medium), 2 (high), 3 (massive). Don’t use raw percentages here; the relative scale is the point.

Tie Impact to a specific outcome: activation rate, retention, revenue per user, or whatever north star metric you’re optimizing for. “Impact = 2” should mean “this is twice as impactful per user as a baseline feature.”

Confidence

Confidence is your certainty in the Reach and Impact estimates. Express it as a percentage: 100% (you have data), 80% (solid intuition), 50% (speculative).

Confidence is the most powerful debiasing element of RICE. It penalizes wishful-thinking estimates. A feature with Reach 10,000, Impact 3, Confidence 50% has the same RICE numerator as one with Reach 5,000, Impact 3, Confidence 100%. The latter is the better bet.

Effort

Effort is how many person-months the initiative will take across product, design, and engineering. Use months, not story points. 1 person-month = 1 person working full-time for one month.

Get this estimate from engineering. Your gut estimate is almost always wrong. Engineering should give you a range; use the upper bound for the RICE calculation to stay honest.

RICE prioritization examples

Three worked examples to make this concrete:

Example 1: Notification preferences feature

• Reach: 12,000 users per quarter (everyone who hits the new-user notification flow)
• Impact: 1 (medium impact on activation, based on past notification work)
• Confidence: 80% (solid intuition + some qualitative data)
• Effort: 2 person-months
• RICE Score: (12,000 × 1 × 0.8) / 2 = 4,800

Example 2: Onboarding redesign

• Reach: 8,000 users per quarter (new signups)
• Impact: 2 (high impact on activation, with A/B test backing)
• Confidence: 100% (we have the A/B test data)
• Effort: 4 person-months
• RICE Score: (8,000 × 2 × 1.0) / 4 = 4,000

Example 3: Speculative AI feature

• Reach: 50,000 users per quarter (could be everyone)
• Impact: 3 (massive, if it works)
• Confidence: 30% (highly speculative)
• Effort: 6 person-months
• RICE Score: (50,000 × 3 × 0.3) / 6 = 7,500

In this example, the speculative AI feature wins the RICE comparison because the upside is so large that even a 30% confidence times 50,000 reach beats a more certain bet. That’s the point of RICE: it lets you reason about high-upside speculative work alongside small-but-certain wins.

RICE vs ICE vs MoSCoW: when to use which

RICE is one of several prioritization frameworks. Pick the right one for your decision:

• RICE when you have a long backlog of comparable features, want a numerical ranking, and have enough data to estimate reach and impact. Best for product teams with a steady cadence and analytics in place.
• ICE (Impact, Confidence, Ease) when you want a faster, less rigorous version. ICE drops Reach and uses Ease instead of Effort. Good for early-stage products without much data.
• MoSCoW (Must, Should, Could, Won’t) for binary must-have vs. nice-to-have decisions. Best for scoping a single release or quarter.
• Weighted scoring when you have multiple stakeholder priorities (revenue, strategic fit, customer satisfaction) and want to weight them differently.
• Kano model when you want to classify features by user delight (must-have, performance, delighter).

See our RICE vs ICE vs MoSCoW comparison for a side-by-side breakdown of when each method wins.

Common mistakes in RICE scoring

1. Estimating Effort yourself. Always get engineering’s number. PM gut estimates are systematically too low.
2. Using 100% confidence by default. If you don’t have data, you don’t have 100% confidence. Be honest.
3. Mixing Impact scales. Either everyone uses the 0.25/0.5/1/2/3 scale or you all agree on a different one. Don’t mix.
4. Re-scoring features differently each time. Anchor on past scores. If a feature was Impact 1 last quarter, it should still be Impact 1 unless something changed.
5. Treating RICE as the answer. RICE is a starting point, not a verdict. Strategic considerations, dependencies, and team morale all override raw scores. Use it to inform the conversation, not end it.

How to use this RICE calculator

1. Add your features. Enter the name of each feature or initiative you want to compare.
2. Score each dimension. Estimate Reach (users per quarter), Impact (0.25 to 3x), Confidence (percentage), and Effort (person-months).
3. Review the ranking. The calculator sorts features by RICE score so you can see which items deliver the most value per unit of effort.
4. Export or share. Save your results to revisit during sprint planning or stakeholder reviews.

RICE prioritization method FAQ

When should I use RICE instead of other prioritization methods?

RICE works best when you have a long backlog and need a repeatable, data-informed way to rank items. If you want a faster, less granular approach, consider ICE scoring. For binary must-have vs. nice-to-have decisions, MoSCoW may be a better fit. See our RICE vs ICE vs MoSCoW comparison for a detailed breakdown.

What confidence level should I use?

Use 100% only when you have strong data (analytics, user research, A/B test results). Drop to 80% for solid intuition backed by qualitative signals, and 50% or lower for speculative bets. The confidence multiplier keeps high-uncertainty items from dominating your roadmap.

How often should I re-score my backlog?

Re-score at least once per quarter or whenever your prioritization inputs change significantly. New user research, shifting business goals, or changes in team capacity all warrant a fresh pass.

Where did the RICE framework come from?

The RICE scoring model was developed at Intercom by Sean McBride and the product team in 2016, formalized as a way to make prioritization decisions repeatable and discussable across PMs. It built on existing scoring frameworks like ICE but added Reach as a separate dimension to prevent volume-blind prioritization.

Can I use RICE for non-product work?

Yes. RICE works for any backlog of comparable initiatives: marketing campaigns, growth experiments, infrastructure projects, even hiring tradeoffs. The four dimensions translate cleanly. The most common adaptation is replacing “users” with whichever entity matters (customers, employees, deals).

Want to learn more? Read our full RICE framework guide, browse prioritization templates, or use Forge to generate a prioritization brief from your RICE scores.