RICE Scoring Template
A ready-to-use RICE scoring template for feature prioritization. Score your backlog by Reach, Impact, Confidence, and Effort — and let the numbers drive roadmap decisions. Free to copy, download, and use. No signup required.
# RICE Scoring Template **Product / Feature Area:** [Name] **Scoring Date:** [Date] **Scored By:** [PM Name] **Quarter / Cycle:** [Q2 2026] --- ## RICE Formula > **RICE Score = (Reach × Impact × Confidence) ÷ Effort** | Factor | Definition | Scale | |---|---|---| | **Reach** | How many users/customers will this affect per quarter? | Number (e.g., 500 users/quarter) | | **Impact** | How much will it move the metric per user? | 3 = massive, 2 = high, 1 = medium, 0.5 = low, 0.25 = minimal | | **Confidence** | How sure are you in your estimates? | 100% = high, 80% = medium, 50% = low | | **Effort** | Total person-months across all team members | 0.5 = 1 week, 1 = 1 month, 2 = 2 months, etc. | --- ## Feature Scoring Table | Feature / Initiative | Reach | Impact | Confidence | Effort | **RICE Score** | Priority | |---|---|---|---|---|---|---| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | **RICE Score formula:** =(Reach * Impact * Confidence%) / Effort --- ## Worked Example | Feature | Reach | Impact | Confidence | Effort | RICE Score | |---|---|---|---|---|---| | Bulk CSV export | 800 | 1 | 80% | 0.5 | **(800 × 1 × 0.8) / 0.5 = 1,280** | | SSO / SAML login | 200 | 3 | 80% | 2 | **(200 × 3 × 0.8) / 2 = 240** | | Slack notifications | 1,200 | 0.5 | 100% | 0.5 | **(1,200 × 0.5 × 1.0) / 0.5 = 1,200** | | AI summary widget | 400 | 2 | 50% | 3 | **(400 × 2 × 0.5) / 3 = 133** | **Ranked:** Bulk CSV export (1,280) → Slack notifications (1,200) → SSO (240) → AI widget (133) --- ## Notes / Assumptions For each feature, document the key assumptions behind your estimates: | Feature | Key Assumptions | |---|---| | | | | | | --- ## Decision Log After scoring, record which items you're committing to and why: | Decision | Rationale | |---|---| | Shipping [Feature X] in Q2 | RICE = 1,280; low effort, high reach. Unblocks enterprise sales. | | Deferring [Feature Y] | RICE = 133; low confidence in impact estimate. Revisit after user research. | | Descoping [Feature Z] | RICE below threshold; stakeholder request, not user need. | --- ## RICE Threshold > Set a threshold score and stick to it. Items below the threshold go to the backlog unless a strategic override is documented. **This cycle's threshold:** ___ Items above threshold: ___ Items below threshold: ___
How to use this RICE Scoring template
Gather your candidate features
List every initiative you're considering for the quarter — new features, improvements, bugs, and tech debt. Include everything before scoring anything. Incomplete lists produce biased rankings.
Score each feature by factor
Fill in Reach (user count), Impact (1–3 scale), Confidence (%), and Effort (person-months). Be consistent: always estimate Reach over the same time horizon (usually per quarter). Score independently before discussing as a team.
Calculate and rank
Apply the formula: (Reach × Impact × Confidence%) ÷ Effort. Sort descending. The top of the list is your starting roadmap. Don't adjust scores retroactively to justify a predetermined answer.
Document assumptions and overrides
Every score is only as good as its assumptions. Write down what you assumed for Reach and Impact. If you override a low-scoring item for strategic reasons, document why — it creates a paper trail and forces honest tradeoffs.
Want a RICE Scoring grounded in your actual customer data?
PMRead ingests your customer interviews, feedback, and Slack threads — and generates PRDs backed by real evidence, not guesses.
Frequently asked questions
What's the difference between RICE and ICE scoring?
ICE (Impact × Confidence × Ease) skips the Reach factor, treating every feature as if it affects the same number of users. RICE adds Reach explicitly, which makes it better for products with variable audience sizes. For growth experiments where all features target the full user base, ICE is simpler and equally valid.
How do I estimate Reach accurately?
Reach = number of users affected per quarter if the feature ships. Use analytics to find the relevant cohort (e.g., 'users who upload files' = 800/quarter). If you don't have data, use 50% confidence. Never use 'all users' unless every active user genuinely hits the feature.
Should engineering estimate Effort or should PM do it?
Engineering estimates Effort. PMs estimate Reach, Impact, and Confidence. Mixing these responsibilities creates systematically biased scores — PMs tend to underestimate effort when it's their pet feature, and over-estimate impact on low-confidence bets.
When should I NOT use RICE scoring?
RICE doesn't work well for: (1) qualitative bets that are hard to quantify (brand, morale, technical foundation), (2) items with strong regulatory or compliance drivers, (3) very early-stage products where you have < 100 users and most estimates are noise. Use it when you have enough data to make the numbers meaningful.
Other free templates