| name | metrics-tracking |
| description | Define, track, and analyze product metrics with frameworks for goal setting and dashboard design |
| type | reference |
| version | 1.0.0 |
| category | business |
| last_updated | "2026-02-03T00:00:00.000Z" |
| source | https://github.com/anthropics/knowledge-work-plugins |
| related_skills | ["feature-spec","stakeholder-comms"] |
| capabilities | [] |
| requires | [] |
| see_also | [] |
| tags | [] |
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
