A methodology for systematically determining feature priorities using the RICE framework. Used for 'RICE analysis,' 'feature prioritization,' 'backlog prioritization,' 'feature scoring,' and 'prioritization frameworks' when deciding on product feature priorities. Note: Automatic Jira/Linear ticket creation and real-time dashboard building are outside the scope of this skill.
A skill that enhances the feature prioritization decisions of strategist and prd-writer.
RICE Score = (Reach x Impact x Confidence) / Effort
Definition: The number of users/events this feature will affect within a given period
Measurement criteria:
- Number of affected users per quarter
- Number of events triggered per month
- Number of customer requests
Examples:
Search improvement: 10,000/quarter (80% of all users)
Dark mode: 3,000/quarter (survey respondents)
CSV export: 500/quarter (enterprise only)
Note: Estimates should be data-driven (GA, surveys, CS tickets)
Definition: The magnitude of impact on individual users
Scoring scale (Intercom method):
3 = Massive — Major improvement in conversion/retention
2 = High — Meaningful improvement
1 = Medium — Moderate improvement
0.5 = Low — Minor improvement
0.25 = Minimal — Barely noticeable
Evaluation criteria:
- Impact on core metrics (conversion, retention, NPS)
- Severity of user pain
- Contribution to differentiation
Definition: Level of confidence in the Reach and Impact estimates
Scoring scale:
100% = High — Data-driven (A/B tests, quantitative analysis)
80% = Medium — Qualitative research (interviews, surveys)
50% = Low — Intuition, limited data
20% = Moonshot — Pure hypothesis
Note: If Confidence is 50% or below, conduct a validation experiment first
Definition: Person-months required for implementation
Calculation method:
1. Engineering: Development + Code Review + QA
2. Design: Wireframes + UI + Usability Testing
3. Other: PM coordination, documentation, launch
Unit: person-month (minimum 0.5)
Examples:
Search improvement: 2 PM (Backend 1, Frontend 0.5, QA 0.5)
Dark mode: 1.5 PM
CSV export: 0.5 PM
| Feature | Reach | Impact | Confidence | Effort | Score |
|---------|-------|--------|------------|--------|-------|
| Search improvement | 10000 | 2 | 80% | 2 | 8000 |
| Dark mode | 3000 | 1 | 80% | 1.5 | 1600 |
| CSV export | 500 | 2 | 100% | 0.5 | 2000 |
| AI recommendations | 8000 | 3 | 50% | 4 | 3000 |
Priority: Search > AI Recommendations > CSV > Dark Mode
Adjusted RICE = RICE Score x Strategic Weight
Strategic Weight:
1.5: Directly tied to a core strategic initiative
1.0: Indirectly related
0.7: Maintenance work unrelated to strategy
→ Corrects for items that are strategically important but have low RICE scores
ICE = Impact x Confidence x Ease
Impact: 1-10
Confidence: 1-10
Ease: 1-10 (inverse of Effort)
Use case: Quick decisions, small teams
Must have — Cannot launch without it
Should have — Important but workaround exists
Could have — Nice to have but not essential
Won't have — Excluded from this release
Use case: Scope decisions, stakeholder communication
High Impact
│
│ Quick Wins │ Strategic
│ (High priority)│ (Planned investment)
│────────────────┼────────────────
│ Fill-ins │ Avoid
│ (When time │ (Deprioritize)
│ permits) │
└──────────────────────────── High Effort
## Feature Priority Analysis
### RICE Scoreboard
| # | Feature | R | I | C | E | Score | Rank |
|---|---------|---|---|---|---|-------|------|
### Roadmap Integration
- Now (This Sprint): [Feature 1, 2]
- Next (Next Quarter): [Feature 3, 4]
- Later (Future): [Feature 5, 6]
### Key Assumptions and Risks
- [Assumption 1]: Validation method
- [Risk 1]: Mitigation plan