Rank your feature ideas with four plain questions: how many people does it help, how much, how sure are you, and how long will it take? Export the ranked list as Markdown or CSV. Saved in your browser, shared with the priority calculator.
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RICE is short for Reach, Impact, Confidence, and Effort: how many people a feature helps, how much, how sure you are, and how long it takes.
Score = (Reach × Impact × Confidence) / Effort
How many people this feature touches in a set period, usually per month. Use real numbers where you can: monthly active users hitting the affected screen, signups going through the flow, or customers who filed the request. Reach is what stops loud requests from a handful of users outranking quiet needs of thousands.
How much the feature moves the needle for each person it reaches. The standard scale is 0.25 (minimal), 0.5 (low), 1 (medium), 2 (high), and 3 (massive). The coarse scale is deliberate: you cannot estimate per-user impact precisely, so do not pretend to.
How sure you are about your Reach and Impact estimates. 100% means you have data, 80% means a solid hypothesis, 50% means a guess. Multiplying by Confidence discounts speculative bets so they do not outrank features backed by evidence.
Total work in person-weeks across design, engineering, and testing. Effort is the divisor: doubling the work halves the score. Estimate in whole or quarter weeks; precision beyond that is false precision.
Say you are choosing between an onboarding checklist and a mobile app. The checklist reaches 1,500 signups per month, with high impact (2) on each, an 80% confidence backed by funnel data, and about 3 person-weeks of work:
(1,500 × 2 × 0.8) / 3 = 800
The mobile app reaches 1,200 users with high impact (2), but it is a bet: 50% confidence and roughly 12 person-weeks:
(1,200 × 2 × 0.5) / 12 = 100
The checklist scores eight times higher. Both features sounded equally exciting in the planning meeting: RICE makes the trade-off explicit instead of a gut call.
A simple impact/effort score is two ratings per feature and works well for fast backlog triage. RICE earns its extra inputs when audience sizes differ: without Reach, a fix for 50 power users and a fix for 5,000 casual users can look identical. If your features all serve roughly the same audience, skip the overhead and use the feature priority calculator with its impact/effort matrix instead. Both tools share the same saved list, so you can switch frameworks without re-entering anything.
When RICE beats gut feel: stakeholders disagree about how many users a feature affects, a pet project keeps floating to the top of the roadmap, or you have analytics that nobody is using in planning. Writing down how many people a feature helps, and how sure you are about it, turns those arguments into checkable numbers.
Practical answers about agents, voting, embeds, and pricing.
RICE stands for Reach, Impact, Confidence, and Effort. The score is Reach times Impact times Confidence, divided by Effort. Reach is how many people the feature touches per month, Impact is how much it moves the needle per person (0.25 to 3), Confidence discounts guesses (50% to 100%), and Effort is the work in person-weeks. Higher scores mean better return on effort.
There is no universal threshold: RICE scores only mean something relative to the other features on your own list, because they scale with your audience size. A feature reaching 10,000 users will outscore one reaching 100 even at identical impact and effort. Rank your backlog and compare within it rather than against numbers from other teams.
Use the best proxy you have: support tickets mentioning the problem, votes on a feature request board, signups per month, or the share of your user base the feature applies to. Then lower your Confidence to 50% to reflect that Reach is a guess. As real data arrives, update the numbers.
Confidence keeps optimistic guesses from beating solid data. Two features with the same Reach, Impact, and Effort should not tie if one is backed by analytics and the other by a hunch. Multiplying by 100%, 80%, or 50% discounts the hunch accordingly.
Use RICE when your features serve audiences of very different sizes, or when stakeholders disagree about how many users something affects: the Reach number forces that debate into the open. For quick backlog triage where everything targets roughly the same users, the simpler impact/effort score on our priority calculator gets you aligned faster.
Yes, in your browser. Your features are stored in localStorage on your device and never sent to a server. Your list is also available on the priority calculator page: both tools share the same saved data. Export as Markdown or CSV needs no email.
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The hardest RICE input is Reach: how many users actually care. FeatQ answers that directly: users submit requests, vote on them, and your Reach column fills itself in.