metrics-tree

What this skill does

# Metrics Tree

## Workflow

Copy this checklist and track your progress:

```
Metrics Tree Progress:
- [ ] Step 1: Define North Star metric
- [ ] Step 2: Identify input metrics (L2)
- [ ] Step 3: Map action metrics (L3)
- [ ] Step 4: Select leading indicators
- [ ] Step 5: Prioritize and experiment
- [ ] Step 6: Validate and refine
```

**Step 1: Define North Star metric**

Ask user for context if not provided:
- **Product/business**: What are we measuring?
- **Current metrics**: Any existing key metrics?
- **Goals**: What does success look like?

Choose North Star using criteria:
- Captures value delivered to customers
- Reflects business model (how you make money)
- Measurable and trackable
- Actionable (teams can influence it)
- Not a vanity metric

See [Common Patterns](#common-patterns) for North Star examples by type.

**Step 2: Identify input metrics (L2)**

Decompose North Star into 3-5 direct drivers:
- What directly causes North Star to increase?
- Use addition or multiplication decomposition
- Ensure components are mutually exclusive where possible
- Each input should be controllable by a team

See [resources/template.md](resources/template.md) for decomposition frameworks.

**Step 3: Map action metrics (L3)**

For each input metric, identify specific user behaviors:
- What actions drive this input?
- Focus on measurable, observable behaviors
- Limit to 3-5 actions per input
- Actions should be within user control

If complex, see [resources/methodology.md](resources/methodology.md) for multi-level hierarchies.

**Step 4: Select leading indicators**

Identify early signals that predict North Star movement:
- Which metrics change before North Star changes?
- Look for early-funnel behaviors (onboarding, activation)
- Find patterns in high-retention cohorts
- Test correlation with future North Star values

**Step 5: Prioritize and experiment**

Rank opportunities by:
- **Impact**: How much will moving this metric affect North Star?
- **Confidence**: How certain are we about the relationship?
- **Ease**: How hard is it to move this metric?

Select 1-3 experiments to test highest-priority hypotheses.

See [resources/evaluators/rubric_metrics_tree.json](resources/evaluators/rubric_metrics_tree.json) for quality criteria.

**Step 6: Validate and refine**

Verify metric relationships:
- Check correlation strength between metrics
- Validate causal direction (does A cause B or vice versa?)
- Test leading indicator timing (how early does it predict?)
- Refine based on data and experiments

## Common Patterns

**North Star Metrics by Business Model:**

**Subscription/SaaS:**
- Monthly Recurring Revenue (MRR)
- Weekly Active Users (WAU)
- Net Revenue Retention (NRR)
- Paid user growth

**Marketplace:**
- Gross Merchandise Value (GMV)
- Successful transactions
- Completed bookings
- Platform take rate × volume

**E-commerce:**
- Revenue per visitor
- Order frequency × AOV
- Customer lifetime value (LTV)

**Social/Content:**
- Time spent on platform
- Content created/consumed
- Engaged users (not just active)
- Network density

**Decomposition Patterns:**

**Additive Decomposition:**
```
North Star = Component A + Component B + Component C

Example: WAU = New Users + Retained Users + Resurrected Users
```
- Use when: Components are independent segments
- Benefit: Teams can own individual components

**Multiplicative Decomposition:**
```
North Star = Factor A × Factor B × Factor C

Example: Revenue = Users × Conversion Rate × Average Order Value
```
- Use when: Components multiply together
- Benefit: Shows leverage points clearly

**Funnel Decomposition:**
```
North Star = Step 1 → Step 2 → Step 3 → Final Conversion

Example: Paid Users = Signups × Activation × Trial Start × Trial Convert
```
- Use when: Sequential conversion process
- Benefit: Identifies bottlenecks

**Cohort Decomposition:**
```
North Star = Σ (Cohort Size × Retention Rate) across all cohorts

Example: MAU = Sum of retained users from each signup cohort
```
- Use when: Retention is key driver
- Benefit: Separates acquisition from retention

## Guardrails

**Avoid Vanity Metrics:**
- ❌ Total registered users (doesn't reflect value)
- ❌ Page views (doesn't indicate engagement)
- ❌ App downloads (doesn't mean active usage)
- ✓ Active users, engagement time, completed transactions

**Ensure Causal Clarity:**
- Don't confuse correlation with causation
- Test whether A causes B or B causes A
- Consider confounding variables
- Validate relationships with experiments

**Limit Tree Depth:**
- Keep to 3-4 levels max (North Star → L2 → L3 → L4)
- Too deep = analysis paralysis
- Too shallow = not actionable
- Focus on highest-leverage levels

**Balance Leading and Lagging:**
- Need both for complete picture
- Leading indicators for early action
- Lagging indicators for validation
- Don't optimize leading indicators that hurt lagging ones

**Avoid Gaming:**
- Consider unintended consequences
- What behaviors might teams game?
- Add guardrail metrics (quality, trust, safety)
- Balance growth with retention/satisfaction

## Quick Reference

**Resources:**
- `resources/template.md` - Metrics tree structure with decomposition frameworks
- `resources/methodology.md` - Advanced techniques for complex metric systems
- `resources/evaluators/rubric_metrics_tree.json` - Quality criteria for metric trees

**Output:**
- File: `metrics-tree.md` in current directory
- Contains: North Star definition, input metrics (L2), action metrics (L3), leading indicators, prioritized experiments, metric relationships diagram

**Success Criteria:**
- North Star clearly defined with rationale
- 3-5 input metrics that fully decompose North Star
- Action metrics are specific, measurable behaviors
- Leading indicators identified with timing estimates
- Top 1-3 experiments prioritized with ICE scores
- Validated against rubric (score ≥ 3.5)

**Quick Decision Framework:**
- **Simple product?** → Use [template.md](resources/template.md) with 2-3 levels
- **Complex multi-sided?** → Use [methodology.md](resources/methodology.md) for separate trees per side
- **Unsure about North Star?** → Review common patterns above, test with "captures value + predicts revenue" criteria
- **Too many metrics?** → Limit to 3-5 per level, focus on highest impact

**Common Mistakes:**
1. **Choosing wrong North Star**: Pick vanity metric or one team can't influence
2. **Too many levels**: Analysis paralysis, lose actionability
3. **Weak causal links**: Metrics correlated but not causally related
4. **Ignoring tradeoffs**: Optimizing one metric hurts another
5. **No experiments**: Build tree but don't test hypotheses