metrics-design

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?)