# Scoring Framework

Hybrid prioritization combining RICE (Intercom), WSJF (SAFe), and Kano classification, grounded in Multi-Criteria Decision Analysis (MCDA) principles.

## Mathematical Foundation

This framework extends standard prioritization models with MCDA best practices:

- **Normalization**: Logarithmic normalization for score scales (handles non-linear value perception)
- **Weighting**: Customizable weights with validation requirements
- **Trade-offs**: Explicit handling through Value/Cost ratio
- **Uncertainty**: Confidence factor adjusts for estimation risk
- **Sensitivity**: Weight variations tested for robustness

**Documentation**: See [Multi-Metric Evaluation Methodology](https://claude-night-market/plugins/abstract/skills/skills-eval/modules/multi-metric-evaluation-methodology.md) for theoretical foundations.

## The Formula

```
Feature Score = (Value Score / Cost Score) * Confidence

Where:
  Value Score = weighted_avg(Reach, Impact, Business Value, Time Criticality)
  Cost Score = weighted_avg(Effort, Risk, Complexity)
  Confidence = 0.0 to 1.0 (how certain are we about estimates?)
```

### Validation Requirements

Before using this framework:

```yaml
validation:
  weights:
    - Document weight derivation method (AHP, expert judgment, empirical)
    - Verify weights sum to 1.0 within each category (value, cost)
    - Test sensitivity to ±20% weight variations
    - Flag critical weights that significantly change rankings

  normalization:
    - Method: "logarithmic" (handles non-linear perception)
    - Rationale: "Diminishing returns on raw scores"
    - Scale_invariance: "Not required (absolute scale used)"

  uncertainty:
    - Confidence < 0.5: Require research before commitment
    - Document basis for confidence assessment
    - Consider worst-case scenario for low-confidence items
```

## Value Factors

### Reach (R)

**Question:** How many users/use-cases does this affect?

| Score | Meaning | Example |
|-------|---------|---------|
| 1 | Very few (<5%) | Niche admin feature |
| 2 | Some (5-15%) | Power user feature |
| 3 | Moderate (15-35%) | Common workflow |
| 5 | Many (35-60%) | Core user journey |
| 8 | Most (60-85%) | Essential feature |
| 13 | Nearly all (>85%) | Universal need |

### Impact (I)

**Question:** How much does this improve the user experience?

| Score | Meaning | Kano Category |
|-------|---------|---------------|
| 1 | Minimal improvement | Basic (expected) |
| 2 | Slight improvement | Basic |
| 3 | Noticeable improvement | Performance |
| 5 | Significant improvement | Performance |
| 8 | Major improvement | Performance |
| 13 | Transformative | Delighter |

### Business Value (BV)

**Question:** How does this contribute to business goals/OKRs?

| Score | Meaning | OKR Alignment |
|-------|---------|---------------|
| 1 | Tangential | No direct OKR connection |
| 2 | Supporting | Supports an initiative |
| 3 | Contributing | Contributes to Key Result |
| 5 | Advancing | Directly advances Key Result |
| 8 | Critical | Required for Key Result |
| 13 | Strategic | Core to company Objective |

### Time Criticality (TC)

**Question:** What's the cost of delay?

| Score | Meaning | Urgency |
|-------|---------|---------|
| 1 | Can wait indefinitely | Nice to have |
| 2 | Can wait 6+ months | Low urgency |
| 3 | Should do this quarter | Moderate urgency |
| 5 | Should do this month | High urgency |
| 8 | Should do this sprint | Very high urgency |
| 13 | Must do immediately | Blocking/critical |

## Cost Factors

### Effort (E)

**Question:** How much work is this?

| Score | Meaning | Time Estimate |
|-------|---------|---------------|
| 1 | Trivial | < 1 day |
| 2 | Small | 1-3 days |
| 3 | Moderate | 3-5 days |
| 5 | Large | 1-2 weeks |
| 8 | Very large | 2-4 weeks |
| 13 | Huge | > 1 month |

### Risk (Rk)

**Question:** What could go wrong?

| Score | Meaning | Risk Level |
|-------|---------|------------|
| 1 | Very low risk | Well-understood, no dependencies |
| 2 | Low risk | Minor unknowns |
| 3 | Moderate risk | Some unknowns or dependencies |
| 5 | High risk | Significant unknowns |
| 8 | Very high risk | Many unknowns, critical dependencies |
| 13 | Extreme risk | Uncharted territory |

### Complexity (Cx)

**Question:** How hard is this to build correctly?

| Score | Meaning | Complexity Level |
|-------|---------|------------------|
| 1 | Simple | Single component, clear requirements |
| 2 | Straightforward | Few components, clear interfaces |
| 3 | Moderate | Multiple components, some edge cases |
| 5 | Complex | Cross-cutting concerns, many edge cases |
| 8 | Very complex | Architectural changes, distributed state |
| 13 | Extremely complex | Novel algorithms, fundamental changes |

## Confidence Scoring

Rate your confidence in the estimates:

| Confidence | Meaning | When to Use |
|------------|---------|-------------|
| 0.9-1.0 | High | Clear requirements, similar past work |
| 0.7-0.9 | Moderate | Some unknowns, reasonable estimates |
| 0.5-0.7 | Low | Many unknowns, rough estimates |
| 0.3-0.5 | Very low | Mostly guessing |
| < 0.3 | Speculative | Requires spike/research first |

**Guardrail:** Features with confidence < 0.5 should be flagged for research before commitment.

## Kano Classification

After scoring, classify the feature:

### Basic (Must-Have)

- Users expect this; absence causes dissatisfaction
- Doesn't increase satisfaction when present
- **Action:** validate these exist before anything else

### Performance (Linear)

- More is better; satisfaction scales with quality
- Competitive differentiator
- **Action:** Optimize based on ROI

### Delighters (Wow Factors)

- Unexpected features that create joy
- Absence doesn't hurt; presence delights
- **Action:** Build after basics and key performers

### Indifferent

- Users don't care either way
- **Action:** Deprioritize or cut

### Reverse

- Feature that some users actively dislike
- **Action:** Make optional or remove

## Calculation Example

```yaml
Feature: Auto-save drafts

# Value Factors
Reach: 8          # Most users write drafts
Impact: 5         # Significant UX improvement
Business Value: 3 # Supports retention KR
Time Criticality: 3 # Should do this quarter

Value Score = (8 + 5 + 3 + 3) / 4 = 4.75

# Cost Factors
Effort: 3         # 3-5 days
Risk: 2           # Low risk, understood problem
Complexity: 3     # Moderate, needs state management

Cost Score = (3 + 2 + 3) / 3 = 2.67

# Confidence
Confidence: 0.8   # Similar features built before

# Final Score
Feature Score = (4.75 / 2.67) * 0.8 = 1.42

# Classification
Kano: Performance (more saving = better UX)
Priority: Medium (1.42 is between 1.5-2.5 threshold)
```

## Interpreting Scores

| Score Range | Priority | Recommendation |
|-------------|----------|----------------|
| > 2.5 | High | Schedule for next sprint |
| 1.5 - 2.5 | Medium | Add to roadmap, plan timing |
| 1.0 - 1.5 | Low | Backlog, revisit quarterly |
| < 1.0 | Very Low | Defer indefinitely or reject |

## Custom Weights

Default weights can be customized in `.feature-review.yaml`:

```yaml
weights:
  value:
    reach: 0.25           # Equal weighting
    impact: 0.30          # Slightly favor user impact
    business_value: 0.25  # Equal to reach
    time_criticality: 0.20 # Slightly less weight
  cost:
    effort: 0.40          # Effort matters most
    risk: 0.30            # Risk is significant
    complexity: 0.30      # Complexity matters

# REQUIRED: Document weight derivation
derivation:
  method: "expert_judgment"  # or "AHP" or "empirical"
  experts: 3
  date: "2025-01-07"
  rationale: "Impact weighted slightly higher based on user feedback"
```

**Guardrails**:
- Weights within each category must sum to 1.0
- Document how weights were derived (not arbitrary)
- Run sensitivity analysis before finalizing
- Flag weights that cause ranking instability

## Comparison with Pure RICE

| Aspect | RICE | Feature Review |
|--------|------|----------------|
| Value factors | Reach, Impact | Reach, Impact, BV, TC |
| Cost factors | Effort only | Effort, Risk, Complexity |
| Time sensitivity | Not explicit | Time Criticality factor |
| Business alignment | Not explicit | Business Value factor |
| Uncertainty | Confidence | Confidence |
| Classification | None | Kano model |
| **MCDA Compliance** | Basic | **Full** (normalization, weighting, sensitivity) |

Feature Review extends RICE with WSJF's time criticality and business value, plus Kano classification for strategic context, all grounded in MCDA best practices.

## Sensitivity Analysis

Before committing to prioritization, test robustness:

```python
def priority_sensitivity_analysis(features, weights, variation=0.20):
    """
    Tests if rankings are stable to weight variations.

    Args:
        features: List of features with scores
        weights: Current weight configuration
        variation: Test ±20% changes

    Returns:
        Dict with sensitivity metrics
    """
    base_ranking = rank_features(features, weights)
    sensitivity = {}

    for category in ["value", "cost"]:
        for factor in weights[category].keys():
            # Test weight increase
            weights_plus = adjust_weight(weights, factor, +variation)
            ranking_plus = rank_features(features, weights_plus)
            correlation_plus = spearman_correlation(base_ranking, ranking_plus)

            # Test weight decrease
            weights_minus = adjust_weight(weights, factor, -variation)
            ranking_minus = rank_features(features, weights_minus)
            correlation_minus = spearman_correlation(base_ranking, ranking_minus)

            sensitivity[factor] = {
                "avg_correlation": (correlation_plus + correlation_minus) / 2,
                "sensitive": correlation_plus < 0.8 or correlation_minus < 0.8
            }

    return sensitivity
```

**Interpretation**:
- Correlation > 0.9: Ranking stable to this weight variation
- Correlation 0.8-0.9: Moderately sensitive
- Correlation < 0.8: Highly sensitive, weight is critical