RICE Prioritisation Skill

Required Inputs

• 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

Process

1. For each initiative provided, gather or estimate R, I, C, E values
2. Flag where estimates are weak and note what data would improve them
3. Calculate RICE score for each
4. Rank highest to lowest
5. Flag any "quick wins" (high RICE score, low effort) and "moonshots" (high impact, high effort)
6. Note dependencies between items that affect sequencing
7. 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]

Recommended Sequence

Quick Wins (high score, low effort)

Data Gaps to Address

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