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