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---
name: metrics-tree
description: Use when setting product North Star metrics, decomposing high-level business metrics into actionable sub-metrics and leading indicators, mapping strategy to measurable outcomes, identifying which metrics to move through experimentation, understanding causal relationships between metrics (leading vs lagging), prioritizing metric improvement opportunities, or when user mentions metric tree, metric decomposition, North Star metric, leading indicators, KPI breakdown, metric drivers, or how metrics connect.
---
# Metrics Tree
## Table of Contents
- [Purpose](#purpose)
- [When to Use](#when-to-use)
- [What Is It](#what-is-it)
- [Workflow](#workflow)
- [Common Patterns](#common-patterns)
- [Guardrails](#guardrails)
- [Quick Reference](#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](#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

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{
"name": "Metrics Tree Evaluator",
"description": "Evaluate metrics trees for North Star selection, decomposition quality, causal clarity, and actionability. Assess whether the metrics tree will drive effective decision-making and experimentation.",
"version": "1.0.0",
"criteria": [
{
"name": "North Star Selection",
"description": "Evaluates whether the chosen North Star metric appropriately captures value and business success",
"weight": 1.3,
"scale": {
"1": {
"label": "Poor North Star choice",
"description": "Vanity metric (registered users, pageviews) that doesn't reflect value delivered or business health. Not actionable or measurable."
},
"2": {
"label": "Weak North Star",
"description": "Metric somewhat related to value but indirect or lagging. Example: Revenue for early-stage product (reflects pricing not product-market fit)."
},
"3": {
"label": "Acceptable North Star",
"description": "Metric captures some value but missing key criteria. For example, measures usage but not business model alignment, or actionable but not predictive of revenue."
},
"4": {
"label": "Good North Star",
"description": "Metric captures value delivered to customers, is measurable and actionable, but relationship to business success could be stronger or rationale could be clearer."
},
"5": {
"label": "Excellent North Star",
"description": "Metric perfectly captures value delivered to customers, predicts business success (revenue/retention), is measurable and actionable by teams. Clear rationale provided. Examples: Slack's 'teams sending 100+ messages/week', Airbnb's 'nights booked'."
}
}
},
{
"name": "Decomposition Completeness",
"description": "Evaluates whether North Star is fully decomposed into mutually exclusive, collectively exhaustive drivers",
"weight": 1.2,
"scale": {
"1": {
"label": "No decomposition",
"description": "North Star stated but not broken down into component drivers. No input metrics (L2)."
},
"2": {
"label": "Incomplete decomposition",
"description": "1-2 input metrics identified but major drivers missing. Components overlap (not mutually exclusive) or gaps exist (not collectively exhaustive)."
},
"3": {
"label": "Basic decomposition",
"description": "3-5 input metrics cover major drivers but some gaps or overlaps exist. Mathematical relationship unclear (additive vs multiplicative)."
},
"4": {
"label": "Complete decomposition",
"description": "3-5 input metrics are mutually exclusive and collectively exhaustive. Clear mathematical relationship (e.g., sum or product). Minor gaps acceptable."
},
"5": {
"label": "Rigorous decomposition",
"description": "3-5 input metrics provably decompose North Star with explicit formula. MECE (mutually exclusive, collectively exhaustive). Each input can be owned by a team. Validated with data that components sum/multiply to North Star."
}
}
},
{
"name": "Causal Clarity",
"description": "Evaluates whether causal relationships between metrics are clearly specified and validated",
"weight": 1.2,
"scale": {
"1": {
"label": "No causal reasoning",
"description": "Metrics listed without explaining how they relate to each other or to North Star."
},
"2": {
"label": "Assumed causation",
"description": "Relationships implied but not validated. Possible confusion between correlation and causation. Direction unclear (does A cause B or B cause A?)."
},
"3": {
"label": "Plausible causation",
"description": "Causal relationships stated with reasoning but not validated with data. Direction clear. Lag times not specified."
},
"4": {
"label": "Validated causation",
"description": "Causal relationships supported by correlation data or past experiments. Direction and approximate lag times specified. Some relationships tested."
},
"5": {
"label": "Proven causation",
"description": "Causal relationships validated through experiments or strong observational data (cohort analysis, regression). Effect sizes quantified (e.g., 10% increase in X → 5% increase in Y). Lag times specified. Confounds controlled."
}
}
},
{
"name": "Actionability",
"description": "Evaluates whether metrics can actually be moved by teams through specific actions",
"weight": 1.1,
"scale": {
"1": {
"label": "Not actionable",
"description": "Metrics are outcomes outside team control (market conditions, competitor actions) or too abstract to act on."
},
"2": {
"label": "Weakly actionable",
"description": "Metrics are high-level (e.g., 'engagement') without specific user behaviors identified. Teams unsure what to do."
},
"3": {
"label": "Moderately actionable",
"description": "Some action metrics (L3) identified but not comprehensive. Clear which metrics each team owns but specific actions to move them are vague."
},
"4": {
"label": "Actionable",
"description": "Action metrics (L3) specified as concrete user behaviors for each input metric. Teams know what actions to encourage. Current rates measured."
},
"5": {
"label": "Highly actionable",
"description": "Action metrics are specific, observable behaviors with clear measurement (events tracked). Each input metric has 3-5 actions identified. Teams have explicit experiments to test moving actions. Ownership clear."
}
}
},
{
"name": "Leading Indicator Quality",
"description": "Evaluates whether true leading indicators are identified that predict North Star movement",
"weight": 1.0,
"scale": {
"1": {
"label": "No leading indicators",
"description": "Only lagging indicators provided (same time or after North Star changes)."
},
"2": {
"label": "Weak leading indicators",
"description": "Indicators proposed but timing unclear (do they actually predict?) or correlation weak/untested."
},
"3": {
"label": "Plausible leading indicators",
"description": "2-3 indicators identified that logically should predict North Star. Timing estimates provided but not validated. Correlation not measured."
},
"4": {
"label": "Validated leading indicators",
"description": "2-3 leading indicators with timing specified (e.g., 'predicts 7-day retention') and correlation measured (r > 0.6). Tested on historical data."
},
"5": {
"label": "High-quality leading indicators",
"description": "2-4 leading indicators with proven predictive power (r > 0.7), clear timing (days/weeks ahead), and actionable (teams can move them). Includes propensity models or cohort analysis showing predictive strength."
}
}
},
{
"name": "Prioritization Rigor",
"description": "Evaluates whether experiments and opportunities are prioritized using sound reasoning",
"weight": 1.0,
"scale": {
"1": {
"label": "No prioritization",
"description": "Metrics and experiments listed without ranking or rationale."
},
"2": {
"label": "Subjective prioritization",
"description": "Ranking provided but based on gut feel or opinion without framework or data."
},
"3": {
"label": "Framework-based prioritization",
"description": "ICE or RICE framework applied but scores are estimates without data support. Top 3 experiments identified."
},
"4": {
"label": "Data-informed prioritization",
"description": "ICE/RICE scores based on historical data or analysis. Impact estimates grounded in past experiments or correlations. Top 1-3 experiments have clear hypotheses and success criteria."
},
"5": {
"label": "Rigorous prioritization",
"description": "ICE/RICE scores validated with data. Tradeoffs considered (e.g., impact vs effort, short-term vs long-term). Sensitivity analysis performed (\"what if impact is half?\"). Top experiments have quantified hypotheses, clear metrics, and decision criteria. Portfolio approach if multiple experiments."
}
}
},
{
"name": "Guardrails & Counter-Metrics",
"description": "Evaluates whether risks, tradeoffs, and negative externalities are considered",
"weight": 0.9,
"scale": {
"1": {
"label": "No risk consideration",
"description": "Only positive metrics. No mention of potential downsides, gaming, or tradeoffs."
},
"2": {
"label": "Risks mentioned",
"description": "Potential issues noted but no concrete counter-metrics or guardrails defined."
},
"3": {
"label": "Some guardrails",
"description": "1-2 counter-metrics identified (e.g., quality, satisfaction) but no thresholds set. Tradeoffs acknowledged but not quantified."
},
"4": {
"label": "Clear guardrails",
"description": "2-4 counter-metrics with minimum acceptable thresholds (e.g., NPS must stay ≥40). Gaming risks identified. Monitoring plan included."
},
"5": {
"label": "Comprehensive risk framework",
"description": "Counter-metrics for each major risk (quality, trust, satisfaction, ecosystem health). Guardrail thresholds set based on data or policy. Gaming prevention mechanisms specified. Tradeoff analysis included (e.g., short-term growth vs long-term retention)."
}
}
},
{
"name": "Overall Usefulness",
"description": "Evaluates whether the metrics tree will effectively guide decision-making and experimentation",
"weight": 1.0,
"scale": {
"1": {
"label": "Not useful",
"description": "Missing critical components or so flawed that teams cannot use it for decisions."
},
"2": {
"label": "Limited usefulness",
"description": "Provides some structure but too many gaps, unclear relationships, or impractical to implement."
},
"3": {
"label": "Moderately useful",
"description": "Covers basics (North Star, input metrics, some actions) but lacks depth in actionability or prioritization. Teams can use it with significant additional work."
},
"4": {
"label": "Useful",
"description": "Complete metrics tree with clear structure. Teams can identify what to measure, understand relationships, and select experiments. Minor improvements needed."
},
"5": {
"label": "Highly useful",
"description": "Decision-ready artifact. Teams can immediately use it to align on goals, prioritize experiments, instrument dashboards, and make metric-driven decisions. Well-documented assumptions and data gaps. Review cadence specified."
}
}
}
],
"guidance": {
"by_business_model": {
"saas_subscription": {
"north_star_options": "MRR, WAU/MAU for engaged users, Net Revenue Retention (NRR) for mature",
"key_inputs": "New users, retained users, expansion revenue, churn",
"leading_indicators": "Activation rate, feature adoption, usage frequency, product qualified leads (PQLs)",
"guardrails": "Customer satisfaction (NPS/CSAT), support ticket volume, technical reliability"
},
"marketplace": {
"north_star_options": "GMV, successful transactions, nights booked (supply × demand balanced metric)",
"key_inputs": "Supply-side (active suppliers), demand-side (buyers/searches), match rate/liquidity",
"leading_indicators": "New supplier activation, buyer intent signals, supply utilization rate",
"guardrails": "Supply/demand balance ratio, trust/safety metrics, quality scores"
},
"ecommerce": {
"north_star_options": "Revenue, orders per customer, customer LTV",
"key_inputs": "Traffic, conversion rate, AOV, repeat purchase rate",
"leading_indicators": "Add-to-cart rate, wishlist additions, email engagement, product page depth",
"guardrails": "Return rate, customer satisfaction, shipping time, product quality ratings"
},
"social_content": {
"north_star_options": "Engaged time, content created and consumed, network density (connections per user)",
"key_inputs": "Content creation rate, content consumption, social interactions, retention",
"leading_indicators": "Profile completion, first content post, first social interaction, 7-day activation",
"guardrails": "Content quality, user wellbeing, toxicity/moderation metrics, creator retention"
},
"mobile_app": {
"north_star_options": "DAU (for high-frequency) or WAU (for moderate-frequency), session frequency × duration",
"key_inputs": "New installs, activated users, retained users, resurrected users",
"leading_indicators": "Day 1 retention, tutorial completion, push notification opt-in, first core action",
"guardrails": "App rating, uninstall rate, crash-free rate, user-reported satisfaction"
}
},
"by_stage": {
"pre_pmf": {
"focus": "Finding product-market fit through retention and satisfaction signals",
"north_star": "Week-over-week retention (>40% is strong signal)",
"key_metrics": "Retention curves, NPS, 'very disappointed' score (>40%), organic usage frequency",
"experiments": "Rapid iteration on core value prop, onboarding, early activation"
},
"post_pmf_pre_scale": {
"focus": "Validating unit economics and early growth loops",
"north_star": "New activated users per week or month",
"key_metrics": "LTV/CAC ratio (>3), payback period (<12 months), month-over-month growth (>10%)",
"experiments": "Channel optimization, conversion funnel improvements, early retention tactics"
},
"growth": {
"focus": "Efficient scaling of acquisition, activation, and retention",
"north_star": "Revenue, ARR, or transaction volume",
"key_metrics": "Net revenue retention (>100%), magic number (>0.75), efficient growth",
"experiments": "Systematic A/B testing, multi-channel optimization, retention programs, expansion revenue"
},
"maturity": {
"focus": "Profitability, market share, operational efficiency",
"north_star": "Free cash flow, EBITDA, or market share",
"key_metrics": "Operating margin (>20%), customer concentration, competitive position",
"experiments": "Operational efficiency, new market expansion, product line extension, M&A"
}
}
},
"common_failure_modes": {
"vanity_north_star": "Chose metric that looks good but doesn't reflect value (total registered users, app downloads). Fix: Select metric tied to usage and business model.",
"incomplete_decomposition": "Input metrics don't fully explain North Star. Missing key drivers. Fix: Validate that inputs sum/multiply to North Star mathematically.",
"correlation_not_causation": "Assumed causation without validation. Metrics move together but one doesn't cause the other. Fix: Run experiments or use causal inference methods.",
"not_actionable": "Metrics are outcomes without clear actions. Teams don't know what to do. Fix: Add action metrics (L3) as specific user behaviors.",
"no_leading_indicators": "Only lagging metrics that react slowly. Can't make proactive decisions. Fix: Find early signals through cohort analysis or propensity modeling.",
"ignoring_tradeoffs": "Optimizing one metric hurts another. No guardrails set. Fix: Add counter-metrics with minimum thresholds.",
"gaming_risk": "Metric can be easily gamed without delivering real value. Fix: Add quality signals and combination metrics.",
"no_prioritization": "Too many metrics to focus on. No clear experiments. Fix: Use ICE/RICE framework to rank top 1-3 experiments."
},
"excellence_indicators": [
"North Star clearly captures value delivered to customers and predicts business success with explicit rationale",
"Decomposition is provably MECE (mutually exclusive, collectively exhaustive) with mathematical formula",
"Causal relationships validated through experiments or strong observational data with effect sizes quantified",
"Each input metric has 3-5 specific action metrics (observable user behaviors) with measurement defined",
"2-4 leading indicators identified with proven predictive power (r > 0.7) and clear timing",
"Top 1-3 experiments prioritized using data-informed ICE/RICE scores with quantified hypotheses",
"Counter-metrics and guardrails defined for major risks (quality, gaming, ecosystem health) with thresholds",
"Assumptions documented, data gaps identified, review cadence specified",
"Metrics tree diagram clearly shows relationships and hierarchy",
"Decision-ready artifact that teams can immediately use for alignment and experimentation"
],
"evaluation_notes": {
"scoring": "Calculate weighted average across all criteria. Minimum passing score: 3.0 (basic quality). Production-ready target: 3.5+. Excellence threshold: 4.2+.",
"context": "Adjust expectations based on business stage, data availability, and complexity. Early-stage with limited data may score 3.0-3.5 and be acceptable. Growth-stage with resources should target 4.0+.",
"iteration": "Low scores indicate specific improvement areas. Prioritize fixing North Star selection and causal clarity first (highest weights), then improve actionability and prioritization. Revalidate after changes."
}
}

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# Metrics Tree Methodology
**When to use this methodology:** You've used [template.md](template.md) and need advanced techniques for:
- Multi-sided marketplaces or platforms
- Complex metric interdependencies and feedback loops
- Counter-metrics and guardrail systems
- Network effects and viral growth
- Preventing metric gaming
- Seasonal adjustment and cohort aging effects
- Portfolio approach for different business stages
**If your metrics tree is straightforward:** Use [template.md](template.md) directly. This methodology is for complex metric systems.
---
## Table of Contents
1. [Multi-Sided Marketplace Metrics](#1-multi-sided-marketplace-metrics)
2. [Counter-Metrics & Guardrails](#2-counter-metrics--guardrails)
3. [Network Effects & Viral Loops](#3-network-effects--viral-loops)
4. [Preventing Metric Gaming](#4-preventing-metric-gaming)
5. [Advanced Leading Indicators](#5-advanced-leading-indicators)
6. [Metric Interdependencies](#6-metric-interdependencies)
7. [Business Stage Metrics](#7-business-stage-metrics)
---
## 1. Multi-Sided Marketplace Metrics
### Challenge
Marketplaces have supply-side and demand-side that must be balanced. Optimizing one side can hurt the other.
### Solution: Dual Tree Approach
**Step 1: Identify constraint**
- **Supply-constrained**: More demand than supply → Focus on supply-side metrics
- **Demand-constrained**: More supply than demand → Focus on demand-side metrics
- **Balanced**: Need both → Monitor ratio/balance metrics
**Step 2: Create separate trees**
**Supply-Side Tree:**
```
North Star: Active Suppliers (providing inventory)
├─ New supplier activation
├─ Retained suppliers (ongoing activity)
└─ Supplier quality/performance
```
**Demand-Side Tree:**
```
North Star: Successful Transactions
├─ New customer acquisition
├─ Repeat customer rate
└─ Customer satisfaction
```
**Step 3: Define balance metrics**
- **Liquidity ratio**: Supply utilization rate (% of inventory sold)
- **Match rate**: % of searches resulting in transaction
- **Wait time**: Time from demand signal to fulfillment
**Example (Uber):**
- Supply NS: Active drivers with >10 hours/week
- Demand NS: Completed rides
- Balance metric: Average wait time <5 minutes, driver utilization >60%
### Multi-Sided Decomposition Template
```
Marketplace GMV = (Supply × Utilization) × (Demand × Conversion) × Average Transaction
Where:
- Supply: Available inventory/capacity
- Utilization: % of supply that gets used
- Demand: Potential buyers/searches
- Conversion: % of demand that transacts
- Average Transaction: $ per transaction
```
---
## 2. Counter-Metrics & Guardrails
### Problem
Optimizing primary metrics can create negative externalities (quality drops, trust declines, user experience suffers).
### Solution: Balanced Scorecard with Guardrails
**Framework:**
1. **Primary metric** (North Star): What you're optimizing
2. **Counter-metrics**: What could be harmed
3. **Guardrail thresholds**: Minimum acceptable levels
**Example (Content Platform):**
```
Primary: Content Views (maximize)
Counter-metrics with guardrails:
- Content quality score: Must stay ≥7/10 (current: 7.8)
- User satisfaction (NPS): Must stay ≥40 (current: 52)
- Creator retention: Must stay ≥70% (current: 75%)
- Time to harmful content takedown: Must be ≤2 hours (current: 1.5h)
Rule: If any guardrail is breached, pause optimization of primary metric
```
### Common Counter-Metric Patterns
| Primary Metric | Potential Harm | Counter-Metric |
|----------------|----------------|----------------|
| Pageviews | Clickbait, low quality | Time on page, bounce rate |
| Engagement time | Addictive dark patterns | User-reported wellbeing, voluntary sessions |
| Viral growth | Spam | Unsubscribe rate, report rate |
| Conversion rate | Aggressive upsells | Customer satisfaction, refund rate |
| Speed to market | Technical debt | Bug rate, system reliability |
### How to Set Guardrails
1. **Historical baseline**: Look at metric over past 6-12 months, set floor at 10th percentile
2. **Competitive benchmark**: Set floor at industry average
3. **User feedback**: Survey users on acceptable minimum
4. **Regulatory**: Compliance thresholds
---
## 3. Network Effects & Viral Loops
### Measuring Network Effects
**Network effect**: Product value increases as more users join.
**Metrics to track:**
- **Network density**: Connections per user (higher = stronger network)
- **Cross-side interactions**: User A's action benefits User B
- **Viral coefficient (K)**: New users generated per existing user
- K > 1: Exponential growth (viral)
- K < 1: Sub-viral (need paid acquisition)
**Decomposition:**
```
New Users = Existing Users × Invitation Rate × Invitation Acceptance Rate
Example:
100,000 users × 2 invites/user × 50% accept = 100,000 new users (K=1.0)
```
### Viral Loop Metrics Tree
**North Star:** Viral Coefficient (K)
**Decomposition:**
```
K = (Invitations Sent / User) × (Acceptance Rate) × (Activation Rate)
Input metrics:
├─ Invitations per user
│ ├─ % users who send ≥1 invite
│ ├─ Average invites per sender
│ └─ Invitation prompts shown
├─ Invite acceptance rate
│ ├─ Invite message quality
│ ├─ Social proof (sender credibility)
│ └─ Landing page conversion
└─ New user activation rate
├─ Onboarding completion
├─ Value realization time
└─ Early engagement actions
```
### Network Density Metrics
**Measure connectedness:**
- Average connections per user
- % of users with ≥N connections
- Clustering coefficient (friends-of-friends)
- Active daily/weekly connections
**Threshold effects:**
- Users with 7+ friends have 10x retention (identify critical mass)
- Teams with 10+ members have 5x engagement (team size threshold)
---
## 4. Preventing Metric Gaming
### Problem
Teams optimize for the letter of the metric, not the spirit, creating perverse incentives.
### Gaming Detection Framework
**Step 1: Anticipate gaming**
For each metric, ask: "How could someone game this?"
**Example metric: Time on site**
- Gaming: Auto-play videos, infinite scroll, fake engagement
- Intent: User finds value, willingly spends time
**Step 2: Add quality signals**
Distinguish genuine value from gaming:
```
Time on site (primary)
+ Quality signals (guards against gaming):
- Active engagement (clicks, scrolls, interactions) vs passive
- Return visits (indicates genuine interest)
- Completion rate (finished content vs bounced)
- User satisfaction rating
- Organic shares (not prompted)
```
**Step 3: Test for gaming**
- Spot check: Sample user sessions, review for patterns
- Anomaly detection: Flag outliers (10x normal behavior)
- User feedback: "Was this session valuable to you?"
### Gaming Prevention Patterns
**Pattern 1: Combination metrics**
Don't measure single metric; require multiple signals:
```
❌ Single: Pageviews
✓ Combined: Pageviews + Time on page >30s + Low bounce rate
```
**Pattern 2: User-reported value**
Add subjective quality check:
```
Primary: Feature adoption rate
+ Counter: "Did this feature help you?" (must be >80% yes)
```
**Pattern 3: Long-term outcome binding**
Tie short-term to long-term:
```
Primary: New user signups
+ Bound to: 30-day retention (signups only count if user retained)
```
**Pattern 4: Peer comparison**
Normalize by cohort or segment:
```
Primary: Sales closed
+ Normalized: Sales closed / Sales qualified leads (prevents cherry-picking easy wins)
```
---
## 5. Advanced Leading Indicators
### Technique 1: Propensity Scoring
**Predict future behavior from early signals.**
**Method:**
1. Collect historical data: New users + their 30-day outcomes
2. Identify features: Day 1 behaviors (actions, time spent, features used)
3. Build model: Logistic regression or decision tree predicting 30-day retention
4. Score new users: Probability of retention based on day 1 behavior
5. Threshold: Users with >70% propensity score are "likely retained"
**Example (SaaS):**
```
30-day retention model (R² = 0.78):
Retention = 0.1 + 0.35×(invited teammate) + 0.25×(completed 3 workflows) + 0.20×(time in app >20min)
Leading indicator: % of users with propensity score >0.7
Current: 45% → Target: 60% (predicts 15% retention increase)
```
### Technique 2: Cohort Behavior Clustering
**Find archetypes that predict outcomes.**
**Method:**
1. Segment users by first-week behavior patterns
2. Measure long-term outcomes per segment
3. Identify high-value archetypes
**Example:**
```
Archetypes (first week):
- "Power user": 5+ days active, 20+ actions → 85% retain
- "Social": Invites 2+ people, comments 3+ times → 75% retain
- "Explorer": Views 10+ pages, low actions → 40% retain
- "Passive": <3 days active, <5 actions → 15% retain
Leading indicator: % of new users becoming "Power" or "Social" archetypes
Target: Move 30% → 45% into high-value archetypes
```
### Technique 3: Inflection Point Analysis
**Find tipping points where behavior changes sharply.**
**Method:**
1. Plot outcome (retention) vs candidate metric (actions taken)
2. Find where curve steepens (inflection point)
3. Set that as leading indicator threshold
**Example:**
```
Retention by messages sent (first week):
- 0-2 messages: 20% retention (slow growth)
- 3-9 messages: 45% retention (moderate growth)
- 10+ messages: 80% retention (sharp jump)
Inflection point: 10 messages
Leading indicator: % of users hitting 10+ messages in first week
```
---
## 6. Metric Interdependencies
### Problem
Metrics aren't independent; changing one affects others in complex ways.
### Solution: Causal Diagram
**Step 1: Map relationships**
Draw arrows showing how metrics affect each other:
```
[Acquisition] → [Active Users] → [Engagement] → [Retention]
↓ ↑
[Activation] ----------------------------------------
```
**Step 2: Identify feedback loops**
- **Positive loop** (reinforcing): A → B → A (exponential)
Example: More users → more network value → more users
- **Negative loop** (balancing): A → B → ¬A (equilibrium)
Example: More supply → lower prices → less supply
**Step 3: Predict second-order effects**
If you increase metric X by 10%:
- Direct effect: Y increases 5%
- Indirect effect: Y affects Z, which feeds back to X
- Net effect: May be amplified or dampened
**Example (Marketplace):**
```
Increase driver supply +10%:
→ Wait time decreases -15%
→ Rider satisfaction increases +8%
→ Rider demand increases +5%
→ Driver earnings decrease -3% (more competition)
→ Driver churn increases +2%
→ Net driver supply increase: +10% -2% = +8%
```
### Modeling Tradeoffs
**Technique: Regression or experiments**
```
Run A/B test increasing X
Measure all related metrics
Calculate elasticities:
- If X increases 1%, Y changes by [elasticity]%
Build tradeoff matrix
```
**Tradeoff Matrix Example:**
| If increase by 10% | Acquisition | Activation | Retention | Revenue |
|--------------------|-------------|------------|-----------|---------|
| **Acquisition** | +10% | -2% | -1% | +6% |
| **Activation** | 0% | +10% | +5% | +12% |
| **Retention** | 0% | +3% | +10% | +15% |
**Interpretation:** Investing in retention has best ROI (15% revenue lift vs 6% from acquisition).
---
## 7. Business Stage Metrics
### Problem
Optimal metrics change as business matures. Early-stage metrics differ from growth or maturity stages.
### Stage-Specific North Stars
**Pre-Product/Market Fit (PMF):**
- **Focus**: Finding PMF, not scaling
- **North Star**: Retention (evidence of value)
- **Key metrics**:
- Week 1 → Week 2 retention (>40% = promising)
- NPS or "very disappointed" survey (>40% = good signal)
- Organic usage frequency (weekly+ = habit-forming)
**Post-PMF, Pre-Scale:**
- **Focus**: Unit economics and growth
- **North Star**: New activated users per week (acquisition + activation)
- **Key metrics**:
- LTV/CAC ratio (target >3:1)
- Payback period (target <12 months)
- Month-over-month growth rate (target >10%)
**Growth Stage:**
- **Focus**: Efficient scaling
- **North Star**: Revenue or gross profit
- **Key metrics**:
- Net revenue retention (target >100%)
- Magic number (ARR growth / S&M spend, target >0.75)
- Burn multiple (cash burned / ARR added, target <1.5)
**Maturity Stage:**
- **Focus**: Profitability and market share
- **North Star**: Free cash flow or EBITDA
- **Key metrics**:
- Operating margin (target >20%)
- Market share / competitive position
- Customer lifetime value
### Transition Triggers
**When to change North Star:**
```
PMF → Growth: When retention >40%, NPS >40, organic growth observed
Growth → Maturity: When growth rate <20% for 2+ quarters, market share >30%
```
**Migration approach:**
1. Track both old and new North Star for 2 quarters
2. Align teams on new metric
3. Deprecate old metric
4. Update dashboards and incentives
---
## Quick Decision Trees
### "Should I use counter-metrics?"
```
Is primary metric easy to game or has quality risk?
├─ YES → Add counter-metrics with guardrails
└─ NO → Is metric clearly aligned with user value?
├─ YES → Primary metric sufficient, monitor only
└─ NO → Redesign metric to better capture value
```
### "Do I have network effects?"
```
Does value increase as more users join?
├─ YES → Track network density, K-factor, measure at different scales
└─ NO → Does one user's action benefit others?
├─ YES → Measure cross-user interactions, content creation/consumption
└─ NO → Standard metrics tree (no network effects)
```
### "Should I segment my metrics tree?"
```
Do different user segments have different behavior patterns?
├─ YES → Do segments have different value to business?
├─ YES → Create separate trees per segment, track segment mix
└─ NO → Single tree, annotate with segment breakdowns
└─ NO → Are there supply/demand sides?
├─ YES → Dual trees (Section 1)
└─ NO → Single unified tree
```
---
## Summary: Advanced Technique Selector
| Scenario | Use This Technique | Section |
|----------|-------------------|---------|
| **Multi-sided marketplace** | Dual tree + balance metrics | 1 |
| **Risk of negative externalities** | Counter-metrics + guardrails | 2 |
| **Viral or network product** | K-factor + network density | 3 |
| **Metric gaming risk** | Quality signals + combination metrics | 4 |
| **Need better prediction** | Propensity scoring + archetypes | 5 |
| **Complex interdependencies** | Causal diagram + elasticities | 6 |
| **Changing business stage** | Stage-appropriate North Star | 7 |

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# Metrics Tree Template
## How to Use This Template
Follow this structure to create a metrics tree for your product or business:
1. Start with North Star metric definition
2. Apply appropriate decomposition method
3. Map action metrics for each input
4. Identify leading indicators
5. Prioritize experiments using ICE framework
6. Output to `metrics-tree.md`
---
## Part 1: North Star Metric
### Define Your North Star
**North Star Metric:** [Name of metric]
**Definition:** [Precise definition including time window]
Example: "Number of unique users who complete at least one transaction per week"
**Rationale:** [Why this metric?]
- ✓ Captures value delivered to customers: [how]
- ✓ Reflects business model: [revenue connection]
- ✓ Measurable and trackable: [data source]
- ✓ Actionable by teams: [who can influence]
**Current Value:** [Number] as of [Date]
**Target:** [Goal] by [Date]
### North Star Selection Checklist
- [ ] **Customer value**: Does it measure value delivered to customers?
- [ ] **Business correlation**: Does it predict revenue/business success?
- [ ] **Actionable**: Can teams influence it through their work?
- [ ] **Measurable**: Do we have reliable data?
- [ ] **Not vanity**: Does it reflect actual usage/value, not just interest?
- [ ] **Time-bounded**: Does it have a clear time window (daily/weekly/monthly)?
---
## Part 2: Metric Decomposition
Choose the decomposition method that best fits your North Star:
### Method 1: Additive Decomposition
**Use when:** North Star is sum of independent segments
**Formula:**
```
North Star = Component A + Component B + Component C + ...
```
**Template:**
```
[North Star] =
+ [New users/customers]
+ [Retained users/customers]
+ [Resurrected users/customers]
+ [Other segment]
```
**Example (SaaS WAU):**
```
Weekly Active Users =
+ New activated users this week (30%)
+ Retained from previous week (60%)
+ Resurrected (inactive→active) (10%)
```
### Method 2: Multiplicative Decomposition
**Use when:** North Star is product of rates/factors
**Formula:**
```
North Star = Factor A × Factor B × Factor C × ...
```
**Template:**
```
[North Star] =
[Total addressable users/visits]
× [Conversion rate at step 1]
× [Conversion rate at step 2]
× [Value per conversion]
```
**Example (E-commerce Revenue):**
```
Monthly Revenue =
Monthly site visitors
× Purchase conversion rate (3%)
× Average order value ($75)
```
### Method 3: Funnel Decomposition
**Use when:** North Star is end of sequential conversion process
**Formula:**
```
North Star = Top of funnel → Step 1 → Step 2 → ... → Final conversion
```
**Template:**
```
[North Star] =
[Total entries]
× [Step 1 conversion %]
× [Step 2 conversion %]
× [Final conversion %]
```
**Example (Paid SaaS Customers):**
```
New paid customers/month =
Free signups
× Activation rate (complete onboarding) (40%)
× Trial start rate (25%)
× Trial→Paid conversion rate (20%)
Math: 1000 signups × 0.4 × 0.25 × 0.2 = 20 paid customers
```
### Method 4: Cohort Decomposition
**Use when:** Retention is key driver, need to separate acquisition from retention
**Formula:**
```
North Star = Σ (Cohort Size_t × Retention Rate_t,n) for all cohorts
```
**Template:**
```
[North Star today] =
[Users from Month 0] × [Month 0 retention rate]
+ [Users from Month 1] × [Month 1 retention rate]
+ ...
+ [Users from Month N] × [Month N retention rate]
```
**Example (Subscription Service MAU):**
```
March Active Users =
Jan signups (500) × Month 2 retention (50%) = 250
+ Feb signups (600) × Month 1 retention (70%) = 420
+ Mar signups (700) × Month 0 retention (100%) = 700
= 1,370 MAU
```
---
## Part 3: Input Metrics (L2)
For each component in your decomposition, define as input metric:
### Input Metric Template
**Input Metric 1:** [Name]
- **Definition:** [Precise definition]
- **Current value:** [Number]
- **Target:** [Goal]
- **Owner:** [Team/person]
- **Relationship to North Star:** [How it affects NS, with estimated coefficient]
Example: "Increasing activation rate by 10% → 5% increase in WAU"
**Input Metric 2:** [Name]
[Repeat for 3-5 input metrics]
### Validation Questions
- [ ] Are all input metrics **mutually exclusive**? (No double-counting)
- [ ] Do they **collectively exhaust** the North Star? (Nothing missing)
- [ ] Can each be **owned by a single team**?
- [ ] Is each **measurable** with existing/planned instrumentation?
- [ ] Are they all **at same level of abstraction**?
---
## Part 4: Action Metrics (L3)
For each input metric, identify specific user behaviors that drive it:
### Action Metrics Template
**For Input Metric: [Name of L2 metric]**
**Action 1:** [Specific user behavior]
- **Measurement:** [How to track it]
- **Frequency:** [How often it happens]
- **Impact:** [Estimated effect on input metric]
- **Current rate:** [% of users doing this]
**Action 2:** [Another behavior]
[Repeat for 3-5 actions per input]
**Example (For input metric "Retained Users"):**
**Action 1:** User completes core workflow
- Measurement: Track "workflow_completed" event
- Frequency: 5x per week average for active users
- Impact: Users with 3+ completions have 80% retention vs 20% baseline
- Current rate: 45% of users complete workflow at least once
**Action 2:** User invites teammate
- Measurement: "invite_sent" event with "invite_accepted" event
- Frequency: 1.2 invites per user on average
- Impact: Users who invite have 90% retention vs 40% baseline
- Current rate: 20% of users send at least one invite
---
## Part 5: Leading Indicators
Identify early signals that predict North Star movement:
### Leading Indicator Template
**Leading Indicator 1:** [Metric name]
- **Definition:** [What it measures]
- **Timing:** [How far in advance it predicts] Example: "Predicts week 4 retention"
- **Correlation:** [Strength of relationship] Example: "r=0.75 with 30-day retention"
- **Actionability:** [How teams can move it]
- **Current value:** [Number]
**Example:**
**Leading Indicator: Day 1 Activation Rate**
- Definition: % of new users who complete 3 key actions on first day
- Timing: Predicts 7-day and 30-day retention (measured day 1, predicts weeks ahead)
- Correlation: r=0.82 with 30-day retention. Users with Day 1 activation have 70% retention vs 15% without
- Actionability: Improve onboarding flow, reduce time-to-value, send activation nudges
- Current value: 35%
### How to Find Leading Indicators
**Method 1: Cohort analysis**
- Segment users by early behavior (first day, first week)
- Measure long-term outcomes (retention, LTV)
- Find behaviors that predict positive outcomes
**Method 2: Correlation analysis**
- List all early-funnel metrics
- Calculate correlation with North Star or key inputs
- Select metrics with r > 0.6 and actionable
**Method 3: High-performer analysis**
- Identify users in top 20% for North Star metric
- Look at their first week/month behavior
- Find patterns that distinguish them from average users
---
## Part 6: Experiment Prioritization
Use ICE framework to prioritize which metrics to improve:
### ICE Scoring Template
**Impact (1-10):** How much will improving this metric affect North Star?
- 10 = Direct, large effect (e.g., 10% improvement → 8% NS increase)
- 5 = Moderate effect (e.g., 10% improvement → 3% NS increase)
- 1 = Small effect (e.g., 10% improvement → 0.5% NS increase)
**Confidence (1-10):** How certain are we about the relationship?
- 10 = Proven causal relationship with data
- 5 = Correlated, plausible causation
- 1 = Hypothesis, no data yet
**Ease (1-10):** How easy is it to move this metric?
- 10 = Simple change, 1-2 weeks
- 5 = Moderate effort, 1-2 months
- 1 = Major project, 6+ months
**ICE Score = (Impact + Confidence + Ease) / 3**
### Prioritization Table
| Metric/Experiment | Impact | Confidence | Ease | ICE Score | Rank |
|-------------------|--------|------------|------|-----------|------|
| [Experiment 1] | [1-10] | [1-10] | [1-10] | [Avg] | [#] |
| [Experiment 2] | [1-10] | [1-10] | [1-10] | [Avg] | [#] |
| [Experiment 3] | [1-10] | [1-10] | [1-10] | [Avg] | [#] |
### Top 3 Experiments
**Experiment 1:** [Name - highest ICE score]
- **Hypothesis:** [What we believe will happen]
- **Metric to move:** [Target metric]
- **Expected impact:** [Quantified prediction]
- **Timeline:** [Duration]
- **Success criteria:** [How we'll know it worked]
**Experiment 2:** [Second highest]
[Repeat structure]
**Experiment 3:** [Third highest]
[Repeat structure]
---
## Part 7: Metric Relationships Diagram
Create visual representation of your metrics tree:
### ASCII Tree Format
```
North Star: [Metric Name] = [Current Value]
│
├─ Input Metric 1: [Name] = [Value]
│ ├─ Action 1.1: [Behavior] = [Rate]
│ ├─ Action 1.2: [Behavior] = [Rate]
│ └─ Action 1.3: [Behavior] = [Rate]
│
├─ Input Metric 2: [Name] = [Value]
│ ├─ Action 2.1: [Behavior] = [Rate]
│ ├─ Action 2.2: [Behavior] = [Rate]
│ └─ Action 2.3: [Behavior] = [Rate]
│
└─ Input Metric 3: [Name] = [Value]
├─ Action 3.1: [Behavior] = [Rate]
├─ Action 3.2: [Behavior] = [Rate]
└─ Action 3.3: [Behavior] = [Rate]
Leading Indicators:
→ [Indicator 1]: Predicts [what] by [timing]
→ [Indicator 2]: Predicts [what] by [timing]
```
### Example (Complete Tree)
```
North Star: Weekly Active Users = 10,000
│
├─ New Activated Users = 3,000/week (30%)
│ ├─ Complete onboarding: 40% of signups
│ ├─ Connect data source: 25% of signups
│ └─ Invite teammate: 20% of signups
│
├─ Retained Users = 6,000/week (60%)
│ ├─ Use core feature 3+ times: 45% of users
│ ├─ Create content: 30% of users
│ └─ Engage with team: 25% of users
│
└─ Resurrected Users = 1,000/week (10%)
├─ Receive reactivation email: 50% open rate
├─ See new feature announcement: 30% click rate
└─ Get @mentioned by teammate: 40% return rate
Leading Indicators:
→ Day 1 activation rate (35%): Predicts 30-day retention
→ 3 key actions in first session (22%): Predicts weekly usage
```
---
## Output Format
Create `metrics-tree.md` with this structure:
```markdown
# Metrics Tree: [Product/Business Name]
**Date:** [YYYY-MM-DD]
**Owner:** [Team/Person]
**Review Frequency:** [Weekly/Monthly]
## North Star Metric
**Metric:** [Name]
**Current:** [Value] as of [Date]
**Target:** [Goal] by [Date]
**Rationale:** [Why this metric]
## Decomposition Method
[Additive/Multiplicative/Funnel/Cohort]
**Formula:**
[Mathematical relationship]
## Input Metrics (L2)
### 1. [Input Metric Name]
- **Current:** [Value]
- **Target:** [Goal]
- **Owner:** [Team]
- **Impact:** [Effect on NS]
#### Actions (L3):
1. [Action 1]: [Current rate]
2. [Action 2]: [Current rate]
3. [Action 3]: [Current rate]
[Repeat for all input metrics]
## Leading Indicators
1. **[Indicator 1]:** [Definition]
- Timing: [When it predicts]
- Correlation: [Strength]
- Current: [Value]
2. **[Indicator 2]:** [Definition]
[Repeat structure]
## Prioritized Experiments
### Experiment 1: [Name] (ICE: [Score])
- **Hypothesis:** [What we believe]
- **Metric:** [Target]
- **Expected Impact:** [Quantified]
- **Timeline:** [Duration]
- **Success Criteria:** [Threshold]
[Repeat for top 3 experiments]
## Metrics Tree Diagram
[Include ASCII or visual diagram]
## Notes
- [Assumptions made]
- [Data gaps or limitations]
- [Next review date]
```
---
## Quick Examples by Business Model
### SaaS Example (Slack-style)
**North Star:** Teams sending 100+ messages per week
**Decomposition (Additive):**
```
Active Teams = New Active Teams + Retained Active Teams + Resurrected Teams
```
**Input Metrics:**
- New active teams: Complete onboarding + hit 100 messages in week 1
- Retained active teams: Hit 100 messages this week and last week
- Resurrected teams: Hit 100 messages this week but not last 4 weeks
**Leading Indicators:**
- 10 members invited in first day (predicts team activation)
- 50 messages sent in first week (predicts long-term retention)
### E-commerce Example
**North Star:** Monthly Revenue
**Decomposition (Multiplicative):**
```
Revenue = Visitors × Purchase Rate × Average Order Value
```
**Input Metrics:**
- Monthly unique visitors (owned by Marketing)
- Purchase conversion rate (owned by Product)
- Average order value (owned by Merchandising)
**Leading Indicators:**
- Add-to-cart rate (predicts purchase)
- Product page views per session (predicts purchase intent)
### Marketplace Example (Airbnb-style)
**North Star:** Nights Booked
**Decomposition (Multi-sided):**
```
Nights Booked = (Active Listings × Availability Rate) × (Searches × Booking Rate)
```
**Input Metrics:**
- Active host supply: Listings with ≥1 available night
- Guest demand: Unique searches
- Match rate: Searches resulting in booking
**Leading Indicators:**
- Host completes first listing (predicts long-term hosting)
- Guest saves listings (predicts future booking)