Metrics Tree

Table of Contents

• Purpose
• When to Use
• What Is It
• Workflow
• Common Patterns
• Guardrails
• Quick Reference

Purpose

Decompose high-level "North Star" metrics into actionable sub-metrics, identify leading indicators, understand causal relationships, and select high-impact experiments to move metrics.

When to Use

Use metrics-tree when you need to:

Define Strategy:

• Setting a North Star metric for product/business
• Aligning teams around single most important metric
• Clarifying what success looks like quantitatively
• Connecting strategic goals to measurable outcomes

Understand Metrics:

• Decomposing complex metrics into component drivers
• Identifying what actually moves a high-level metric
• Understanding causal relationships between metrics
• Distinguishing leading vs lagging indicators
• Mapping metric interdependencies

Prioritize Actions:

• Deciding which sub-metrics to focus on
• Identifying highest-leverage improvement opportunities
• Selecting experiments that will move North Star
• Allocating resources across metric improvement efforts
• Understanding tradeoffs between metric drivers

Diagnose Issues:

• Investigating why a metric is declining
• Finding root causes of metric changes
• Identifying bottlenecks in metric funnels
• Troubleshooting unexpected metric behavior

What Is It

A metrics tree decomposes a North Star metric (the single most important product/business metric) into its component drivers, creating a hierarchy of related metrics with clear causal relationships.

Key Concepts:

North Star Metric: Single metric that best captures core value delivered to customers and predicts long-term business success. Examples:

• Airbnb: Nights booked
• Netflix: Hours watched
• Slack: Messages sent by teams
• Uber: Rides completed
• Stripe: Payment volume

Metric Levels:

1. North Star (top): Ultimate measure of success
2. Input Metrics (L2): Direct drivers of North Star (what you can control)
3. Action Metrics (L3): Specific user behaviors that drive inputs
4. Output Metrics (L4): Results of actions (often leading indicators)

Leading vs Lagging:

• Leading indicators: Predict future North Star movement (early signals)
• Lagging indicators: Measure past performance (delayed feedback)

Quick Example:

North Star: Weekly Active Users (WAU)

Input Metrics (L2):
├─ New User Acquisition
├─ Retained Users (week-over-week)
└─ Resurrected Users (inactive → active)

Action Metrics (L3) for Retention:
├─ Users completing onboarding
├─ Users creating content
├─ Users engaging with others
└─ Users receiving notifications

Leading Indicators:
- Day 1 activation rate (predicts 7-day retention)
- 3 key actions in first session (predicts long-term engagement)

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

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