RICE prioritization
Reach × impact × confidence ÷ effort. Score your list below — and then see the part every other calculator leaves out: how much the estimates would have to be wrong before the order changes.
Short answer
What is the RICE prioritization framework?
A scoring method from Intercom for ordering a backlog: reach times impact times confidence, divided by effort. The score has no units and no meaning on its own — it exists only to sort one list against itself.
The short version
- Reach is people per fixed period; impact is a coarse five-point scale (3, 2, 1, 0.5, 0.25); confidence is 100%, 80% or 50%; effort is person-months.
- Effort is the divisor, so it has the most leverage — and it is the number people misjudge most, especially for work they want to do.
- Confidence multiplies the whole score: a 50% item needs twice the reach to tie an otherwise identical 100% one.
- Two scores 3% apart are tied. An order that a single confidence notch reshuffles has not told you what to build.
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Score the list, then see how much it would take to reorder it
Reach is people per period — quarter, month, whatever you use, as long as it is the same for every row. Effort is person-months. The score has no units and matters only as an ordering.
| Item | Reach | Impact | Confidence | Effort, pm | |
|---|---|---|---|---|---|
The order
- 1CSV exportscore 500
- 2Self-serve onboardingscore 480
- 3Dark modescore 125
- 4SSO for enterprisescore 64
How solid is that order
CSV export over Self-serve onboarding survives a 4% error in reach or effort.
⚠️ “CSV export” drops below “Self-serve onboarding” if its impact goes one step down.
⚠️ “CSV export” drops below “Self-serve onboarding” if its confidence goes one step down.
⚠️ “Self-serve onboarding” overtakes “CSV export” if its impact goes one step up.
⚠️ “Self-serve onboarding” overtakes “CSV export” if its confidence goes one step up.
Self-serve onboarding over Dark mode survives a 284% error in reach or effort.
Dark mode over SSO for enterprise survives a 95% error in reach or effort.
# RICE PRIORITIZATION
Date: 2026-09-12
## Order
1. CSV export
reach 500 · impact Medium · confidence High · 100% · effort 1 pm → score 500
2. Self-serve onboarding
reach 1200 · impact High · confidence Medium · 80% · effort 4 pm → score 480
3. Dark mode
reach 2000 · impact Minimal · confidence Low · 50% · effort 2 pm → score 125
4. SSO for enterprise
reach 80 · impact Massive · confidence Medium · 80% · effort 3 pm → score 64
## How solid is this order
CSV export over Self-serve onboarding: survives a 4% error in reach or effort
⚠️ “CSV export” drops below “Self-serve onboarding” if its impact goes one step down
⚠️ “CSV export” drops below “Self-serve onboarding” if its confidence goes one step down
⚠️ “Self-serve onboarding” overtakes “CSV export” if its impact goes one step up
⚠️ “Self-serve onboarding” overtakes “CSV export” if its confidence goes one step up
Self-serve onboarding over Dark mode: survives a 284% error in reach or effort
Dark mode over SSO for enterprise: survives a 95% error in reach or effort
## What this does NOT say
The score is not a quantity. It has no units and means nothing on its own — only
the ORDER matters, and the order is only as good as estimates of reach and
effort that nobody has verified. Effort is a divisor: underestimate it by half
and the item doubles.
Calculated with the free tool at https://gonogo.team/rice-prioritization
What this cannot know. Every number above is an estimate nobody checked. Reach is guessed, impact is a five-point opinion, and effort is underestimated as a rule — which matters more than the rest because effort is a divisor: halve it and the item doubles. The score has no units and no meaning outside this list.
RICE ranks what you already decided to consider. Whether those are the right things to consider is a different question — the Kano model separates features people miss when absent from features they merely enjoy, which is not visible anywhere in a RICE table.
RICE orders a backlog somebody already decided was worth building. A 15-minute session ends with a written GO / WAIT / NO-GO and the reasoning behind it. Free tier, no card.
The four inputs
Reach
people per periodHow many people this affects in a fixed window — per quarter, per month, whatever you pick, as long as every row uses the same one. Count people, not events, and count the ones who actually encounter it rather than everyone on the list.
How it goes wrong: The most quietly invented number in the table. “All our users” is almost never the reach of anything.
Impact
3 / 2 / 1 / 0.5 / 0.25How much it moves the thing you care about, per person reached. Massive, high, medium, low, minimal. The scale is coarse on purpose: it is a judgement, and a finer scale would only make the judgement look measured.
How it goes wrong: Everything becomes “high” when the person who proposed it fills it in.
Confidence
100% / 80% / 50%How sure you are of the other three. It is the only input that admits you might be wrong, and it multiplies the whole score — so a 50% item needs twice the reach to tie with an otherwise identical 100% one.
How it goes wrong: Set to 100% everywhere, which removes the only brake the framework has.
Effort
person-monthsTotal work across everyone involved, design and testing included. It is the divisor, which gives it more leverage than any other input: halve the estimate and the item doubles.
How it goes wrong: Underestimated as a rule, and underestimated most for the things people want to build.
The question nobody asks of a ranked list
A sorted list is persuasive in a way the underlying numbers are not. Item one sits above item two, and the eye reads that as a finding — whether the gap is three times or three per cent.
But every input here is an estimate. Reach is guessed from a funnel nobody instrumented. Impact is an opinion on a five-point scale. Effort is underestimated as a matter of professional habit. If the top two items are two per cent apart, the framework has not chosen between them — it has reported the noise in your estimates with impressive precision.
So the calculator above answers the question directly: for each adjacent pair, how large an error in reach or effort the ordering survives, and which single notch on the impact or confidence scale would swap them. Where one notch is enough, the order is a coin landing, and it is better to know that before the quarter is planned around it.
What RICE cannot do
It cannot compare unlike things. A bug fix, a rebrand and a compliance deadline in one table produce a number for each and a decision for none. Rank within a kind, not across kinds.
It cannot see dependencies. The framework treats every item as an independent choice. Work that unblocks other work scores as though it stood alone, and therefore scores badly.
It cannot tell you what belongs on the list. RICE orders the candidates you already chose. Whether they are the right candidates is a different question — the Kano model separates the features people miss when absent from the ones they merely enjoy, and nothing in a RICE table shows that difference.
It cannot make an estimate true. Arithmetic on invented inputs produces confident invented outputs, which is the whole risk of scoring frameworks and the reason the fragility column exists.
Ordering the list assumes the list is worth building
A backlog is a good problem to have. It assumes people want the product and the question is only what to do first. If that assumption is the shaky part, a better ordering of the wrong list does not help.
A 15-minute session works through seven criteria — is the problem real, does a market exist, will they pay — and ends with a written verdict and the reasoning.
15 min · free tier, no card
Frequently asked questions
What is the RICE prioritization framework?+
How do you calculate a RICE score?+
Why does effort matter more than the other inputs?+
How reliable is a RICE ranking?+
When is RICE the wrong tool?+
Is this calculator free, and does my data leave the browser?+
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