RICE Scoring

A score that ranks features by how many users they help, how much they help, how sure you are, and how hard they are to build.

When to use this

You have several features to compare and you want a defensible ordering. You have rough usage data (analytics, support tickets, sales call counts) so Reach isn't pure guesswork. You're aligning a team or a stakeholder group on what ships next quarter.

Limits

For a short list, a direct discussion may be enough. You're evaluating a strategic bet where Reach is unknowable. Your team disagrees on what "Impact" even means -- fix the goal first, then score.

Inputs

Reach: How many users (or accounts, or sessions) will encounter this in a fixed period. Pick monthly or quarterly and stick with it.
Impact: How much it moves the needle per user. A 0.25 / 0.5 / 1 / 2 / 3 scale. 3 represents massive impact and 0.25 minimal impact on your chosen goal.
Confidence: How sure you are the Reach and Impact estimates are right. A percentage, usually 50% / 80% / 100%.
Effort: Total work across design, eng, QA, docs, and launch. Pick a unit (person-weeks or person-months) and stick with it.

The math

Score = (Reach x Impact x Confidence) / Effort

Reach times Impact gives a relative estimate of total impact. Multiply by Confidence to discount estimates that could be wrong. Divide by Effort to get benefit per unit of work. Use higher scores as a starting point for priority discussions.

A worked example

Say you're a PM at a B2B SaaS with 8,000 monthly active users. You're scoring three features for the next quarter. Effort is measured in person-weeks.

Feature A: Bulk export to CSV. Reach: 5,000 users expected to use it during the quarter. Impact: 2 (high -- removes a real friction point that shows up in calls). Confidence: 80% (10 customer interviews, clear demand). Effort: 4 person-weeks.

Score = (5,000 x 2 x 0.8) / 4 = 2,000.

Feature B: Custom dashboard widgets. Reach: 1,200 users expected to configure it during the quarter. Impact: 3 (transforms the workflow for that segment). Confidence: 50% (we think they want it, no test yet). Effort: 12 person-weeks.

Score = (1,200 x 3 x 0.5) / 12 = 150.

Feature C: Onboarding tooltips refresh. Reach: 2,000 new users per quarter. Impact: 0.5 (small lift on activation). Confidence: 80% (we've A/B tested similar). Effort: 2 person-weeks.

Score = (2,000 x 0.5 x 0.8) / 2 = 400.

Ranking: A (2,000), C (400), B (150). A ranks first under these estimates. B combines lower reach, lower confidence, and higher effort; its low score is not caused by confidence alone. Review dependencies and strategic fit before choosing the order.

In PM Toolkit

The calculator scores and ranks multiple features in one table. Edit estimates to see how the ranking changes and use the result to discuss tradeoffs.

Common mistakes

Treating Effort as certain. Test a range when estimates are uncertain.
Using different meanings of Impact across features. Choose a shared goal and scale.
Comparing scores from different periods or units. Keep Reach periods and Effort units consistent.
Assigning high Confidence without evidence. Record what supports the estimate and what remains untested.