RICE prioritization
Definition
RICE prioritization is a scoring model that ranks product backlog items by calculating a score based on four factors — Reach, Impact, Confidence, and Effort — to give product teams a structured, data-driven method for prioritizing features against each other.
RICE was created by Sean McBride at Intercom and published in 2016. It addresses a fundamental problem in product prioritization: when every feature request comes from someone important and every justification sounds compelling, you need a shared scoring model to cut through the politics.
The RICE formula:
RICE Score = (Reach × Impact × Confidence) / Effort
Reach: How many users will this feature affect in a given time period (e.g., per quarter)? Estimate from real data when possible: active users, customer segment size, or survey reach. Example: 500 users per quarter.
Impact: How much will this feature improve the metric you care about for each user it reaches? Score on a scale: 3 = massive impact, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal.
Confidence: How confident are you in your estimates? Express as a percentage: 100% = high confidence with data, 80% = medium confidence, 50% = low confidence (mostly a guess).
Effort: How many person-months of work will this require? Include design, engineering, and QA.
Example calculation: Feature: Onboarding checklist. Reach: 800 users/quarter. Impact: 2 (high). Confidence: 80%. Effort: 0.5 person-months. RICE = (800 × 2 × 0.8) / 0.5 = 2,560
Higher RICE scores indicate higher priority. Compare scores across features to produce an ordered backlog.
RICE limitations: RICE is only as good as the estimates going in. Teams that game the confidence score or estimate reach optimistically will get misleading results. Use RICE as a thinking tool, not a decision-making machine. The conversation the scoring forces — "what's our confidence really?" — is often more valuable than the final number.
Examples
- PM scores 15 feature requests using RICE — the top three aren't the ones leadership expected, which triggers a useful strategy conversation
- Feature with huge reach but tiny confidence score ends up below a small-reach, high-confidence feature after RICE scoring
- Team discovers their confidence scores are all at 80% — agrees they're not being honest about uncertainty
- RICE calculation worksheet filled out on a whiteboard in a product planning session
- Intercom used RICE internally to prioritize engineering time — it became their public playbook
Snap a rice prioritization. Ship its actions.
BoardSnap turns any whiteboard — including this one — into a summary and action plan.