RICE Score Calculator

RICE is a prioritization framework developed by Intercom that scores features on four factors: Reach (how many users will this affect per quarter?), Impact (how much will it affect each user?), Confidence (how sure are you about your estimates?), and Effort (how many person-weeks will this take?). The formula — Reach × Impact × Confidence ÷ Effort — produces a single score that represents the ROI of building each feature. Higher scores mean higher priority.

Reach is the number of users or customers who will be affected by this feature per quarter. Use real data: check your analytics for how many users visit the relevant page, ask your support team how many tickets mention this need, or look at vote counts on your feedback board. If you're guessing, lower your Confidence score to compensate. Common ranges: 100 (niche feature), 1,000 (moderate), 10,000+ (affects most users).

Impact measures how much the feature will affect each user who encounters it. The scale: 3 (massive — transforms their workflow), 2 (high — significantly improves their experience), 1 (medium — noticeable improvement), 0.5 (low — minor improvement), 0.25 (minimal — barely noticeable). When in doubt, use 1 (medium). Reserve 3 for features that fundamentally change how users work with your product.

Use RICE when you have 10+ features competing for resources and need an objective way to compare them. RICE works best for teams that can estimate reach and effort with reasonable accuracy. For earlier-stage products where data is scarce, simpler frameworks like Impact vs. Effort (2×2 matrix) or MoSCoW might be better. For teams focused on outcomes rather than features, try an opportunity solution tree instead.

RICE scores are relative, not absolute — what matters is that Feature A scores higher than Feature B, not the exact number. Rough estimates are fine. The Confidence factor exists specifically to account for uncertainty: if you're guessing at Reach, set Confidence to 50%. The framework is most valuable for separating obviously high-impact work from obviously low-impact work. Don't spend hours perfecting scores for features that are clearly in the middle.

No. RICE is a structured way to evaluate features, but the inputs (especially Reach and Impact) should be informed by real user data. The best source? A feature voting board where users tell you what they want. Vote counts are a direct measure of Reach, and the intensity of user comments informs Impact. RICE + user voting data is the most powerful combination for prioritization.