Correctly compute RICE scores for several features and rank them by priority.
RICE: formula, scales, and a worked example
RICE scoring step by step
RICE was created by Sean McBride at Intercom to make backlog prioritization less subjective. The formula is: RICE Score = (Reach × Impact × Confidence) ÷ Effort.
Reach is the number of users or transactions the feature will touch per time period (e.g., per quarter). Impact uses Intercom's scale: Massive = 3, High = 2, Medium = 1, Low = 0.5, Minimal = 0.25. Confidence reflects how sure you are about your estimates: High = 100% (1.0), Medium = 80% (0.8), Low = 50% (0.5). Effort is measured in person-months — how long it takes one person working full time.
The key insight: high effort or low confidence can drop a flashy idea well below a modest one. Consider two features. Feature A: onboarding checklist — Reach 2,000 users, Impact High (2), Confidence Medium (0.8), Effort 4 person-months. Score = (2,000 × 2 × 0.8) ÷ 4 = 3,200 ÷ 4 = 800. Feature B: in-app video tutorials — Reach 500 users, Impact Massive (3), Confidence High (1.0), Effort 2 person-months. Score = (500 × 3 × 1.0) ÷ 2 = 1,500 ÷ 2 = 750. Feature A wins despite lower Impact because its wide reach and lower effort outweigh Feature B's flashier impact rating.
This also connects to hypothesis testing from Unit 2: if your confidence in a feature's impact is only 50%, that halves its RICE score immediately — a strong incentive to validate assumptions cheaply before committing to build.
Lesson notes
RICE scoring step by step
RICE was created by Sean McBride at Intercom to make backlog prioritization less subjective. The formula is: RICE Score = (Reach × Impact × Confidence) ÷ Effort.
Reach is the number of users or transactions the feature will touch per time period (e.g., per quarter). Impact uses Intercom's scale: Massive = 3, High = 2, Medium = 1, Low = 0.5, Minimal = 0.25. Confidence reflects how sure you are about your estimates: High = 100% (1.0), Medium = 80% (0.8), Low = 50% (0.5). Effort is measured in person-months — how long it takes one person working full time.
The key insight: high effort or low confidence can drop a flashy idea well below a modest one. Consider two features. Feature A: onboarding checklist — Reach 2,000 users, Impact High (2), Confidence Medium (0.8), Effort 4 person-months. Score = (2,000 × 2 × 0.8) ÷ 4 = 3,200 ÷ 4 = 800. Feature B: in-app video tutorials — Reach 500 users, Impact Massive (3), Confidence High (1.0), Effort 2 person-months. Score = (500 × 3 × 1.0) ÷ 2 = 1,500 ÷ 2 = 750. Feature A wins despite lower Impact because its wide reach and lower effort outweigh Feature B's flashier impact rating.
This also connects to hypothesis testing from Unit 2: if your confidence in a feature's impact is only 50%, that halves its RICE score immediately — a strong incentive to validate assumptions cheaply before committing to build.