prioritization-frameworks

# Prioritization Frameworks

Quantitative and qualitative frameworks for ranking features, initiatives, and backlog items.

## RICE Framework

Developed by Intercom, RICE provides a data-driven score for comparing features.

### Formula

```
RICE Score = (Reach × Impact × Confidence) / Effort
```

### Factors

| Factor | Definition | Scale |
|--------|------------|-------|
| **Reach** | Users/customers affected per quarter | Actual number |
| **Impact** | Effect on individual user | 0.25 (minimal) to 3 (massive) |
| **Confidence** | How sure are you? | 0.5 (low) to 1.0 (high) |
| **Effort** | Person-months required | Actual estimate |

### Impact Scale

| Score | Level | Description |
|-------|-------|-------------|
| 3 | Massive | Fundamental improvement |
| 2 | High | Significant improvement |
| 1 | Medium | Noticeable improvement |
| 0.5 | Low | Minor improvement |
| 0.25 | Minimal | Barely noticeable |

### Confidence Scale

| Score | Level | Evidence |
|-------|-------|----------|
| 1.0 | High | Strong data, validated |
| 0.8 | Medium | Some data, reasonable assumptions |
| 0.5 | Low | Gut feeling, little data |

### Example Calculation

```markdown
Feature: Smart search with AI suggestions

Reach: 50,000 users/quarter (active searchers)
Impact: 2 (high - significantly better results)
Confidence: 0.8 (tested in prototype)
Effort: 3 person-months

RICE = (50,000 × 2 × 0.8) / 3 = 26,667
```

### RICE Template

```markdown
| Feature | Reach | Impact | Confidence | Effort | RICE Score |
|---------|-------|--------|------------|--------|------------|
| Feature A | 10,000 | 2 | 0.8 | 2 | 8,000 |
| Feature B | 50,000 | 1 | 1.0 | 4 | 12,500 |
| Feature C | 5,000 | 3 | 0.5 | 1 | 7,500 |
```

## ICE Framework

Simpler than RICE, ICE is ideal for fast prioritization.

### Formula

```
ICE Score = Impact × Confidence × Ease
```

### Factors (All 1-10 Scale)

| Factor | Question |
|--------|----------|
| **Impact** | How much will this move the metric? |
| **Confidence** |