RICE Scoring
RICE Scoring is a quantitative prioritisation framework developed by Sean McBride at Intercom and published in 2016. It was created to solve a specific and common problem in product management: important decisions about which features, projects, or initiatives to pursue are made based on loudest voice, seniority, or gut feel rather than structured reasoning. RICE provides a simple formula to calculate a priority score for each candidate item by estimating four factors: Reach (how many people will this affect in a given time period — typically expressed as number of users per quarter), Impact (how much will this move the needle for each affected user — scored on a scale such as 3=massive, 2=high, 1=medium, 0.5=low, 0.25=minimal), Confidence (how confident are you in your estimates, expressed as a percentage — 100% for high evidence, 80% for medium, 50% for low), and Effort (how many person-months of work does this require across all team members). The formula is: RICE Score = (Reach × Impact × Confidence) ÷ Effort. The result is a comparable score that surfaces high-reach, high-impact, high-confidence, low-effort items to the top — while penalising speculative big bets that lack evidence. In workshops, RICE creates transparent, defensible prioritisation conversations and reduces political lobbying by anchoring debate to shared estimates.
Facilitation script
- 1
Brief the team on the formula — RICE Score = (Reach × Impact × Confidence) ÷ Effort — and agree the Reach time frame (e.g. users in the next quarter) and the Impact scale before any scoring starts.
10 min - 2
Score Reach item by item: 'How many people will this meaningfully affect in the defined period?' Use analytics, market size estimates, or customer counts where available, and flag items with no data.
15 min - 3
Score Impact: 'For each person reached, how much does this improve their experience or move our key metric?' Push for honest assessment on the agreed scale (3 = massive down to 0.25 = minimal), not inflated advocacy.
15 min - 4
Score Confidence: 100% for estimates validated by data, 80% for medium evidence, 50% for hypotheses. Challenge the evidence behind every high score — this is the model's reality check.
10 min - 5
Score Effort with engineering or delivery leads as total person-months across all roles, using rough buckets (1 week = 0.25, 1 month = 1, 1 quarter = 3) rather than opening a full estimation session.
15 min - 6
Calculate each score with the formula and sort the list descending.
5 min - 7
Sense-check the ranking together: discuss outliers, ask whether the Confidence scores are honest, and surface strategic dependencies the formula can't see.
10 min - 8
Agree the cut: decide how many items from the top of the list enter the next planning cycle, mark them 'committed', and park the rest as backlog.
10 min
Tips
The Confidence factor is the most powerful and most underused part of RICE. Forcing the team to declare low confidence on speculative items naturally drops them in the ranking without political friction.
Reach must be bounded to a time period. Unbounded reach estimates always inflate — everything eventually affects everyone.
Don't let Effort scoring become a full-blown estimation session. RICE works with rough estimates. Precision in effort scoring that takes 30 minutes per item defeats the purpose.
Watch for 'confidence washing' — teams that assign 80% confidence to everything to keep their pet projects competitive. Challenge the evidence behind high confidence scores.
RICE is excellent for comparing items within the same type (all features, or all growth experiments) but should not be used to compare fundamentally different strategic bets against each other.
Common pitfalls
Leaving the Reach time frame unbounded — everything eventually affects everyone, so estimates inflate and the ranking becomes meaningless
'Confidence washing': the team assigns 80% to everything to keep pet projects competitive, which silently removes the model's only reality check
Letting Effort scoring become a full estimation session — 30 minutes of precision per item defeats the purpose of a rough comparative model
Treating the sorted list as the final verdict instead of a starting point; the formula can't see strategic dependencies, and skipping the sense-check ships them into the quarter unexamined
Scoring unlike items against each other — a platform migration and a growth experiment produce scores that look comparable but aren't
Variations
For early-stage product teams with little data, replace Reach with 'Strategic Alignment' (1–5 scale against current strategy) and lower the confidence baseline to 30–50% across the board. For marketing campaign prioritisation, Reach can represent audience size and Impact can represent conversion lift. ICE Scoring is a faster, lighter variant of RICE for teams that want to move quickly without effort estimation.
Where it fits
When to use it
A backlog of 10–30 comparable features, experiments, or initiatives needs a defensible ranking before planning
Roadmap decisions are currently made by loudest voice or seniority and you need to anchor the debate to shared estimates
Quarterly planning with a cross-functional team, where product, engineering, and growth each hold part of the estimate
Usage data or customer research exists to ground Reach and Confidence — the model rewards teams that can evidence their claims
Speculative big bets keep crowding out well-evidenced smaller wins and you want the Confidence factor to correct for that
When not to use it
Comparing fundamentally different strategic bets against each other — RICE only ranks items of the same type fairly
The backlog exceeds 100 items — pre-filter with a lighter pass such as Dot Voting before scoring
An early-stage team with little data — ICE Scoring, or the variant that swaps Reach for Strategic Alignment, fits better than estimating numbers you don't have
You need a decision in the next 15 minutes; agreeing factor definitions and scoring four factors per item takes at least an hour
A single yes/no call on one initiative — RICE produces comparative scores, and a score with nothing to compare against decides nothing
Related methods
Frequently asked questions
How is a RICE score calculated?▾
RICE Score = (Reach × Impact × Confidence) ÷ Effort. Reach is the number of people affected in a set time period (typically users per quarter); Impact is scored per user on a scale of 3 = massive, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal; Confidence is a percentage (100% validated by data, 80% medium evidence, 50% hypothesis); and Effort is total person-months across all roles. A higher score means more value per unit of work.
What does RICE stand for in prioritisation?▾
RICE stands for Reach, Impact, Confidence, and Effort — the four factors you estimate for each backlog item before combining them as (Reach × Impact × Confidence) ÷ Effort. The framework was developed by Sean McBride at Intercom and published in 2016 to replace loudest-voice and gut-feel prioritisation with structured, comparable scores.
What is a good RICE score?▾
There is no absolute 'good' score — RICE scores are comparative and only meaningful within a single list scored against the same Reach time frame, Impact scale, and Effort units. A high score signals high reach, impact, and confidence per person-month of effort, not a guarantee the item is strategically right. Use the ranking to find the top tier, then sense-check outliers before committing.
What is the difference between RICE and ICE scoring?▾
ICE Scoring (Impact, Confidence, Ease) is a faster, lighter variant for teams that want to move quickly: it drops RICE's explicit Reach estimate and person-month Effort estimation in favour of a simple Ease rating. RICE produces a more defensible ranking when usage data exists to ground Reach; ICE suits early-stage teams that lack that data or need a decision in minutes rather than hours.
How many items should you score in a RICE session?▾
Aim for 10–30 items — RICE doesn't scale well past 100 without pre-filtering. With that range, a cross-functional group of 3–15 people typically needs 60–120 minutes: about 10 minutes to agree definitions, 10–15 minutes per factor across the list, and a closing sense-check of the ranking.
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Try it freeMethod descriptions on Workshop Weaver are original content written by our team, based on established facilitation practices. This method was inspired by work from Sean McBride — Intercom.