RICE vs ICE Scoring

Compare how RICE and ICE handle reach, impact, confidence, and delivery effort.

Last updated: 2026-03-01

Overview

RICE

Data-Driven

Created by Intercom, RICE is a quantitative framework that scores features by Reach, Impact, Confidence, and Effort. Read the original Intercom method.

Best for teams with data on user reach and a need for rigorous, defensible prioritization.

ICE

Fast & Simple

Developed by Sean Ellis, ICE is a lightweight framework that scores by Impact, Confidence, and Ease.

Best for growth teams, startups, and rapid experiment prioritization.

Formula comparison

RICE

RICE = (Reach x Impact x Confidence%) / Effort

Reach = users or events affected in a shared period. Impact = 0.25 to 3. Confidence = a decimal, such as 0.8 for 80%. Effort = person-months.

ICE

ICE = Impact x Confidence x Ease

All three components scored on a 1-10 scale. Simple multiplication gives the final score.

Side-by-side comparison

Criteria	RICE	ICE
Components	Reach, Impact, Confidence, Effort	Impact, Confidence, Ease
Created By	Sean McBride at Intercom	Sean Ellis (GrowthHackers)
Scoring Scale	Mixed (numbers, percentages, person-months)	Simple 1-10 for all components
Data Requirements	Needs comparable Reach estimates	Needs evidence and shared rubrics for three scores
Scoring effort	Estimate reach and delivery effort as well as impact	Score three factors using a shared rubric
Best For	Feature prioritization, roadmap planning	Growth experiments, quick triage
Team Size	Any size with comparable reach estimates	Any size, especially small teams
Objectivity	Depends on the quality of estimates	Depends on evidence and scoring consistency
Handles Large Backlogs	Moderate (slower but thorough)	Excellent (fast scoring)
Learning Curve	Moderate	Low

When to use each

Choose RICE when

You have analytics data to estimate user reach
Stakeholders need data-backed justification
You are planning a quarterly or annual roadmap
Your team can agree on reach, impact, and effort estimates
Features have significantly different user bases

Choose ICE when

You need to quickly score many ideas or experiments
You are in early stage without much analytics data
Running growth experiments or A/B tests
Your team is small and needs minimal process
You are triaging a large backlog as a first pass

Pros and cons

RICE

Pros

Makes reach estimates explicit
Easier to defend in stakeholder meetings
Accounts for scope of impact (not just intensity)

Cons

Requires analytics data for Reach estimates
Slower to score each item
Mixed scales can be confusing for new users

ICE

Pros

Easy to learn and use
Fast scoring enables rapid iteration
Consistent 1-10 scale across all dimensions

Cons

Relies on judgment and can be biased
Does not account for how many users benefit
Harder to defend to data-oriented stakeholders

Try the related tools

Use your own inputs to explore the calculations and compare results.

RICE Scoring Calculator ICE Scoring Calculator

Frequently asked questions

What is the main difference between RICE and ICE scoring?

RICE multiplies Reach, Impact, and Confidence, then divides by Effort. ICE multiplies Impact, Confidence, and Ease in this toolkit. ICE has no separate Reach factor, and its Ease rating is not a literal reciprocal of person-months.

Can I use both together?

Yes. You can use ICE for initial screening, then RICE for candidates where differences in reach matter. Check that the first screening step does not discard ideas whose value ICE misses.

Which framework is better for startups?

ICE can be useful for quick experiment triage. RICE fits when you can estimate reach and effort consistently. Choose based on the decision and available evidence, rather than company age or team size.

How do I prevent score inflation?

Define each score level and have people score independently before discussing disagreements. For RICE, use actual reach estimates over the same time period. Compare past estimates with outcomes to improve future scoring.

Back to calculators