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Glossary
Prioritization

What is ICE Score?

An ICE Score is a mathematical formula used by product and growth teams to rank features, experiments, or marketing tactics. It multiplies three factors—Impact, Confidence, and Ease—each typically rated on a scale of 1 to 10. The resulting total helps teams quickly identify which initiatives offer the most value relative to the estimated effort and level of uncertainty.

When to use it

Teams should use ICE scoring when they need to make rapid decisions across a large volume of ideas, particularly for growth experiments or internal improvements. It is ideal for situations where hard data for more complex frameworks like RICE isn't yet available. This method solves the problem of subjective decision-making by forcing a standardized evaluation of effort and potential success.

Example

A SaaS team is prioritizing four growth experiments for Q3. For a new 'referral program' feature, they estimate an Impact of 8 (high growth potential), a Confidence of 5 (never tried before), and an Ease of 4 (requires significant engineering). The ICE score is 160 (8 x 5 x 4). They compare this to a 'one-click checkout' update with an Impact of 6, Confidence of 9, and Ease of 8, resulting in a score of 432, making the checkout update the clear priority.

How Planet Roadmap helps with ICE Score

Planet Roadmap features a Table view with custom fields that allows teams to input numeric values for Impact, Confidence, and Ease to instantly calculate and sort their product backlog by priority.

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FAQ

How is ICE different from RICE?
While both use Impact, Confidence, and Ease, RICE adds 'Reach' as a fourth factor to account for how many users will be affected by a change. ICE is generally faster to implement and better suited for growth experiments or internal tasks where reach is relatively constant across all options.
What is the best way to determine the Confidence score?
Confidence should be based on evidence rather than gut feel. A score of 10 represents a completed pilot or direct data from customers, while a 1 represents a total guess; using a tiered internal scale based on existing research or technical prototypes helps keep these scores objective across the team.

Related terms

RICE ScoreMoSCoW MethodKano ModelWeighted ScoringValue vs Effort MatrixProduct BacklogInitiativeProduct Strategy

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