RICE Prioritisation Skill
Apply consistent, criteria-based RICE scoring to a list of features or initiatives to produce an objective prioritisation ranking.
Required Inputs
Ask the user for these if not provided:
- List of initiatives or features to score (names and brief descriptions)
- Reach estimates (users affected per quarter — from analytics if available)
- Impact estimates (use the standard scale below)
- Effort estimates (person-months — from engineering if available)
- Quarter or planning period
RICE Definitions (adapt to your context)
- Reach: Number of users affected per quarter (use actual DAU/MAU data where available)
- Impact: Effect on your primary metric — use scale: 3=massive, 2=high, 1=medium, 0.5=low, 0.25=minimal
- Confidence: How certain are we about R and I estimates? 100%=high, 80%=medium, 50%=low
- Effort: Person-months required across all functions
RICE Formula
RICE Score = (Reach × Impact × Confidence) / Effort
Process
- For each initiative provided, gather or estimate R, I, C, E values
- Flag where estimates are weak and note what data would improve them
- Calculate RICE score for each
- Rank highest to lowest
- Flag any "quick wins" (high RICE score, low effort) and "moonshots" (high impact, high effort)
- Note dependencies between items that affect sequencing
- Validate — Cross-check: if the top-ranked item surprises the team, investigate whether an estimate is inflated. RICE is a tool, not a verdict.
Output Structure
RICE Prioritisation: [Backlog/Quarter]
| Initiative | Reach | Impact | Confidence | Effort | RICE Score | Notes |
|---|---|---|---|---|---|---|
| [name] | [n] | [score] | [%] | [months] | [score] | [flags] |
Recommended Sequence
[Top 5 initiatives with rationale]
Quick Wins (high score, low effort)
[Items to pick up alongside bigger bets]
Data Gaps to Address
[What information would most improve scoring accuracy]
Quality Checks
- Every initiative has all four RICE components estimated (even roughly)
- Confidence is 50% for anything without data backing (not 100% as a default)
- Quick wins and moonshots are explicitly called out
- Dependencies that affect sequencing are noted
- Any surprising ranking is investigated before accepting it
Anti-Patterns
- Do not default to 100% confidence on estimates that lack supporting data — this inflates scores and misleads planning
- Do not treat RICE scores as a final decision — a ranking that surprises the team must be investigated before it is accepted
- Do not omit effort estimates from engineering — PM-only effort estimates are frequently optimistic and skew results
- Do not forget to note dependencies that would change the sequencing even if RICE scores suggest otherwise
- Do not score every initiative at the same impact level — if everything is "high impact," the framework produces no useful signal