What the RICE Prioritization Calculator does
This calculator scores and ranks a backlog with RICE - reach times impact times confidence, divided by effort - and shows the working for every item. Paste items or import a CSV, optionally normalise reach so its units stop dominating, and export the ranked list.
RICE was published by Intercom's product team in 2016. It is a convention for making trade-offs explicit, not a measurement: the scores are only as good as the estimates behind them. Your backlog stays in the browser.
How to use it
- List each item with its reach (people or events affected per period - use the same period for every item), impact, confidence and effort.
- Use Intercom's scales for impact (3 massive, 2 high, 1 medium, 0.5 low, 0.25 minimal) and confidence (100% high, 80% medium, 50% low). Other values are accepted but flagged, because mixing scales breaks comparisons.
- Estimate effort in person-months (or person-weeks - just be consistent).
- Read the ranked list and the bar chart. Tick normalise reach if one item's reach is so large that it swamps everything else.
- Download the CSV, which re-imports unchanged, to share or to revisit next quarter.
Reading the results
A RICE score is value per unit of effort. Doubling reach or impact doubles the score; doubling effort halves it.
Close scores are ties in practice. A 10% difference between two items is well inside the error of the estimates; decide those on strategy, dependencies or risk.
Low confidence is a signal to learn before building. An item with high reach and impact but 50% confidence may deserve a cheap experiment rather than a place at the top.
Worked example: three backlog items
An onboarding checklist reaches 500 customers a quarter, has high impact (2), 80% confidence and 4 person-months of effort: 500 x 2 x 0.8 / 4 = 200.
Bulk export reaches 1,000 with medium impact (1), 50% confidence and 2 person-months: 1,000 x 1 x 0.5 / 2 = 250. SSO reaches 200 with massive impact (3), full confidence and 1 person-month: 200 x 3 x 1.0 / 1 = 600.
SSO ranks first despite the smallest reach, because it is cheap and certain. Normalising reach (largest = 100) leaves the order unchanged here but changes the numbers: the checklist becomes 50 x 2 x 0.8 / 4 = 20.
Formulas and scoring rules
- RICE score
score = reach x impact x (confidence / 100) / effort- Normalised reach (optional)
reach' = reach / largest reach x 100- Share of total
share = score / sum of all scores- Ranking
standard competition ranking on the unrounded score (1, 2, 2, 4)Scores shown to 1 decimal; the CSV keeps 4.
Limitations: what the result does not prove
- RICE ranks items on the estimates you give it. It cannot tell you whether the estimates are right, and optimistic effort estimates are the most common distortion.
- It ignores dependencies, deadlines, strategic bets and risk reduction, which is why many teams treat the ranking as a starting point for discussion rather than an answer.
- Reach must use one unit and period for every item; mixing monthly users with yearly transactions makes scores meaningless.
- Items that enable other work (platform changes, tech debt) often score poorly on direct reach and need to be judged separately.
Privacy: where your data goes
Everything you paste, type or drop is processed in this browser tab. It is not uploaded, logged, stored or sent to analytics. Session recording and tag-manager scripts are switched off on this page.
Standards and sources
Frequently asked questions
What is the RICE prioritization formula?
Score equals reach times impact times confidence, divided by effort. With reach 500, impact 2, confidence 80% and effort 4 person-months, the score is 500 x 2 x 0.8 / 4 = 200.
What scale should I use for RICE impact?
Intercom's original scale is 3 for massive, 2 for high, 1 for medium, 0.5 for low and 0.25 for minimal. Keeping to it matters more than the exact numbers, because every item must be scored on the same scale.
How is RICE different from ICE?
RICE separates reach from impact and divides by effort, so it favours cheap work that touches many people. ICE uses three subjective scores of equal weight and is quicker for early experiments where reach is unknown.
Why normalise reach in RICE?
When one item reaches millions and others thousands, raw reach dominates the score. Normalising to 0-100 keeps the ranking identical but makes the scores easier to read and compare between backlogs.
Can I import my backlog from a spreadsheet?
Yes. Export a CSV with the columns name, reach, impact, confidence and effort, then drop it on the import box or paste it. A header row is ignored, and the CSV this tool exports re-imports unchanged.
What does a confidence of 50% mean in RICE?
That the reach or impact estimate is little better than a guess. Intercom suggests treating anything under 50% as a moonshot. Low confidence cuts the score, which is the framework nudging you to validate first.
Last reviewed by the A2Z.Tools team against the sources listed above.
