| name | metrics-design |
| description | Design complete metrics frameworks with outcome-intermediate-leading hierarchy, activation funnels, and engagement dimensions. Use when defining how to measure product success. |
Metrics Design Skill
Purpose
Provide structured guidance for designing complete, measurable metrics frameworks. Adapts to the PM's preferred hierarchy from their profile.
Metrics Hierarchy
Default: Outcome → Intermediate → Leading
Outcome Metrics measure business impact:
- Revenue, retention, expansion
- Measured over longer periods (quarterly, annually)
- What the business ultimately cares about
Intermediate Metrics measure product outcomes:
- Self-service rate, task completion rate, satisfaction score
- Bridge between user behavior and business impact
- Measured over medium periods (monthly, quarterly)
Leading Metrics measure user behavior:
- Feature adoption, session frequency, engagement depth
- Early signals that predict intermediate and outcome metrics
- Measured continuously (daily, weekly)
Alternative Hierarchies
North Star → L1 → L2:
- North Star: Single metric that captures core value delivered
- L1: Direct drivers of the North Star
- L2: Inputs to L1 metrics
Business → Product → Feature:
- Business: Revenue and retention metrics
- Product: User outcome metrics
- Feature: Usage and adoption metrics
Activation Funnel
Default: Setup → Aha → Habit
| Stage | Definition | How to Measure |
|---|
| Setup | User has completed initial configuration and can use the product | % of users who complete setup; time to complete |
| Aha | User has experienced the core value for the first time | % of users who perform the key value action; time to Aha |
| Habit | User has developed a repeated pattern of value extraction | % of users who perform the value action [X] times in [Y] period |
Defining Good Thresholds
- Setup completion should be > 80% (if lower, onboarding is broken)
- Aha rate should be > 50% of those who set up (if lower, value prop is unclear)
- Habit rate should be > 30% of those who reached Aha (if lower, value doesn't sustain)
- Time to Aha should be < 1 week for most B2B products
Engagement Dimensions
Default: Breadth, Depth, Intensity
| Dimension | What It Measures | Example |
|---|
| Breadth | How many features are used | Avg features used per week |
| Depth | How frequently features are used | Avg actions per week |
| Intensity | How much time/effort is invested | Avg session duration |
User Segmentation
| Segment | Default Definition | Implications |
|---|
| Casual | 1-2 sessions per week | At risk of churn, need engagement |
| Core | 3-4 sessions per week | Healthy engagement, focus on deepening |
| Power | 5+ sessions per week | Champions, leverage for expansion |
Tradeoff Metrics
Every optimization has a cost. Always define what might get worse:
| If Optimizing | Watch For Decline In |
|---|
| Adoption rate | Quality of users adopted |
| Engagement depth | Breadth of feature usage |
| Self-service rate | Satisfaction with complex cases |
| Speed to market | Code quality, tech debt |
| Revenue per user | Total addressable users |
Anti-Patterns
- Metrics without baselines (can't measure improvement)
- Outcome metrics only (no early warning system)
- Vanity metrics (total users, page views without context)
- Too many metrics (if everything is measured, nothing is prioritized)
- Metrics without owners (who acts on this data?)
- Metrics without thresholds (what's good vs bad?)
