metrics-tree

name

metrics-tree

description

Decomposes high-level North Star metrics into actionable sub-metrics and leading indicators, maps causal relationships between metric levels, and identifies high-impact experiments to move key metrics. Use when setting product North Star metrics, decomposing business metrics into drivers, mapping strategy to measurable outcomes, identifying which metrics to move through experimentation, understanding leading vs lagging indicators, prioritizing metric improvement opportunities, or when user mentions metric tree, metric decomposition, North Star metric, KPI breakdown, metric drivers, or how metrics connect.

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 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 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 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 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 with 2-3 levels
• Complex multi-sided? → Use 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