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ICE Scoring Template for Prioritization
Score and rank features with the ICE framework. Rate each on Impact, Confidence, and Ease to calculate a composite score for data-driven prioritization.
IPBy IdeaPlan Editorial · Methodology
Updated 2026-03-05
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Frequently Asked Questions
How is ICE different from RICE?+
RICE adds a Reach dimension that estimates how many users a feature affects in a given time period. ICE skips Reach in favor of simplicity. RICE is better when you have reliable usage data. ICE works well when you need a fast, intuitive score without detailed analytics.
What scale should I use for ICE scoring?+
The standard scale is 1 to 10 for each dimension. Some teams prefer 1 to 5 for simplicity, but the 1-10 scale gives more granularity and makes it easier to differentiate between similar features.
How do I handle disagreements on scores?+
Have each team member score independently first, then discuss items where scores diverge by more than 3 points. The discussion is often more valuable than the final number. Use the average of individual scores as the starting point.
When should I NOT use ICE scoring?+
ICE is less useful when you have detailed quantitative data on reach and usage. In those cases, [RICE scoring](/frameworks/rice-framework) gives you a more precise result. ICE also struggles with large strategic bets that do not fit neatly into a 1-10 scale.
How often should I re-score my backlog?+
Re-score at least once per quarter, or whenever you get significant new data. Features with low Confidence scores should be re-evaluated as soon as you run experiments or user research that changes your assumptions.
Related Tools
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MoSCoW Prioritizer
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