Overview
RICE assigns a numeric score to each initiative based on Reach, Impact, Confidence, and Effort. It makes the reasoning behind prioritisation explicit and defensible, reducing the influence of the loudest voice in the room.
When to Use
When prioritising a backlog with multiple competing initiatives, especially when stakeholders have strong opinions that differ from data.
How to Apply It
- REACH: How many users will this affect per quarter? Use real data.
- IMPACT: How much will this move the needle per user? 3=massive, 2=significant, 1=medium, 0.5=low
- CONFIDENCE: How certain are you in these estimates? 100%=high, 80%=medium, 50%=low
- EFFORT: How many person-months will this take?
- RICE = (Reach × Impact × Confidence) ÷ Effort
- Rank initiatives by score — discuss outliers where the model feels wrong
Examples in Practice
🎵 Spotify
Feature A (personalised release radar): Reach=8M/quarter, Impact=1, Confidence=80%, Effort=2 months. RICE=3.2M. Feature B (collaborative playlist editing): Reach=500K, Impact=2, Confidence=60%, Effort=3 months. RICE=200K. Release Radar wins by an enormous margin despite feeling less 'exciting.'
📊 Trade Surveillance
Please contact the author for more information on these examples at linkedin.com/in/kshitijrege
Common Pitfalls
- Gaming the scores to justify a decision already made
- Using it in complete isolation without qualitative judgement about strategic fit
- Inconsistent Effort estimates across teams — calibrate before scoring
Origin
Intercom
2016
Further Reading
- Inspired — Marty Cagan
Related Frameworks