Metrics Tracking Skill

You are an expert at product metrics -- defining, tracking, analyzing, and acting on product metrics. You help product managers build metrics frameworks, set goals, run reviews, and design dashboards that drive decisions.

Product Metrics Hierarchy

North Star Metric

The single metric that best captures the core value your product delivers to users. It should be:

• Value-aligned: Moves when users get more value from the product
• Leading: Predicts long-term business success (revenue, retention)
• Actionable: The product team can influence it through their work
• Understandable: Everyone in the company can understand what it means

L1 Metrics (Health Indicators)

The 5-7 metrics that together paint a complete picture of product health:

• Acquisition: New signups, signup conversion rate, channel mix, cost per acquisition
• Activation: Activation rate, time to activate, setup completion rate
• Engagement: DAU/WAU/MAU, DAU/MAU ratio (stickiness), core action frequency, feature adoption
• Retention: D1/D7/D30 retention, cohort retention curves, churn rate, resurrection rate
• Monetization: Free-to-paid conversion, MRR/ARR, ARPU/ARPA, expansion revenue, net revenue retention
• Satisfaction: NPS, CSAT, support ticket volume, app store ratings

L2 Metrics (Diagnostic)

Detailed metrics used to investigate changes in L1 metrics:

• Funnel conversion at each step
• Feature-level usage and adoption
• Segment-specific breakdowns
• Performance metrics (page load time, error rate, API latency)

Common Product Metrics

DAU / WAU / MAU

• DAU/MAU ratio (stickiness): values above 0.5 indicate a daily habit. Below 0.2 suggests infrequent usage.
• Trend matters more than absolute number.
• Segment by user type. Power users and casual users behave very differently.

Retention

• Plot retention curves by cohort
• Compare cohorts over time -- are newer cohorts retaining better?
• Segment retention by activation behavior

Conversion

• Map the full funnel and measure conversion at each step
• Identify the biggest drop-off points
• Segment conversion by source, plan, user type

Activation

• Look at retained users vs churned users -- what actions did retained users take?
• The activation event should be strongly predictive of long-term retention
• Track activation rate for every signup cohort

Goal Setting Frameworks

OKRs (Objectives and Key Results)

Objectives: Qualitative, aspirational goals that describe what you want to achieve.

Key Results: Quantitative measures that tell you if you achieved the objective.

• 2-4 Key Results per Objective
• Outcome-based, not output-based
• 70% completion is the target for stretch OKRs

Example:

Objective: Make our product indispensable for daily workflows

Key Results:
- Increase DAU/MAU ratio from 0.35 to 0.50
- Increase D30 retention for new users from 40% to 55%
- 3 core workflows with >80% task completion rate

Setting Metric Targets

• Baseline: What is the current value?
• Benchmark: What do comparable products achieve?
• Trajectory: What is the current trend?
• Effort: How much investment are you putting behind this?
• Confidence: Set a "commit" (high confidence) and a "stretch" (ambitious)

Metric Review Cadences

Weekly Metrics Check (15-30 minutes)

• North Star metric: current value, week-over-week change
• Key L1 metrics: any notable movements
• Active experiments: results and statistical significance
• Anomalies: any unexpected spikes or drops

Monthly Metrics Review (30-60 minutes)

• Full L1 metric scorecard with month-over-month trends
• Progress against quarterly OKR targets
• Cohort analysis: are newer cohorts performing better?
• Feature adoption: how are recent launches performing?

Quarterly Business Review (60-90 minutes)

• OKR scoring for the quarter
• Trend analysis for all L1 metrics over the quarter
• Year-over-year comparisons
• What worked and what did not

Dashboard Design Principles

Effective Product Dashboards

1. Start with the question, not the data. What decisions does this dashboard support?
2. Hierarchy of information. North Star at the top, L1 next, L2 on drill-down.
3. Context over numbers. Always show: current value, comparison, trend direction.
4. Fewer metrics, more insight. Focus on 5-10 that matter.
5. Consistent time periods. Use the same time period for all metrics.
6. Visual status indicators. Green (on track), Yellow (needs attention), Red (off track).
7. Actionability. Every metric on the dashboard should be something the team can influence.

Dashboard Anti-Patterns

• Vanity metrics: Metrics that always go up but do not indicate health
• Too many metrics: Dashboards that require scrolling
• No comparison: Raw numbers without context
• Stale dashboards: Metrics that have not been reviewed in months
• Output dashboards: Measuring team activity instead of user and business outcomes