--- id: sm-metrics-tracking name: "metrics-tracking" url: https://skills.yangsir.net/skill/sm-metrics-tracking author: anthropics domain: data-analysis tags: ["metrics", "data-analytics", "kpis", "business-intelligence", "performance-monitoring"] install_count: 279 rating: 4.10 (36 reviews) github: https://github.com/anthropics/knowledge-work-plugins --- # metrics-tracking > 专注于产品指标的定义、追踪、分析和行动，帮助产品经理构建指标框架。 **Stats**: 279 installs · 4.1/5 (36 reviews) ## Before / After 对比 ### 产品指标定义与数据驱动决策效率对比 | Metric | Before | After | Change | |---|---|---|---| | - | - | - | - | | - | - | - | - | | - | - | - | - | ## Readme # metrics-tracking # 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 and why it matters **Examples by product type**: - Collaboration tool: Weekly active teams with 3+ members contributing - Marketplace: Weekly transactions completed - SaaS platform: Weekly active users completing core workflow - Content platform: Weekly engaged reading/viewing time - Developer tool: Weekly deployments using the tool ### L1 Metrics (Health Indicators) The 5-7 metrics that together paint a complete picture of product health. These map to the key stages of the user lifecycle: **Acquisition**: Are new users finding the product? - New signups or trial starts (volume and trend) - Signup conversion rate (visitors to signups) - Channel mix (where are new users coming from) - Cost per acquisition (for paid channels) **Activation**: Are new users reaching the value moment? - Activation rate: % of new users who complete the key action that predicts retention - Time to activate: how long from signup to activation - Setup completion rate: % who complete onboarding steps - First value moment: when users first experience the core product value **Engagement**: Are active users getting value? - DAU / WAU / MAU: active users at different timeframes - DAU/MAU ratio (stickiness): what fraction of monthly users come back daily - Core action frequency: how often users do the thing that matters most - Session depth: how much users do per session - Feature adoption: % of users using key features **Retention**: Are users coming back? - D1, D7, D30 retention: % of users who return after 1 day, 7 days, 30 days - Cohort retention curves: how retention evolves for each signup cohort - Churn rate: % of users or revenue lost per period - Resurrection rate: % of churned users who come back **Monetization**: Is value translating to revenue? - Conversion rate: free to paid (for freemium) - MRR / ARR: monthly or annual recurring revenue - ARPU / ARPA: average revenue per user or account - Expansion revenue: revenue growth from existing customers - Net revenue retention: revenue retention including expansion and contraction **Satisfaction**: How do users feel about the product? - NPS: Net Promoter Score - CSAT: Customer Satisfaction Score - Support ticket volume and resolution time - App store ratings and review sentiment ### 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 (by plan, company size, geography, user role) - Performance metrics (page load time, error rate, API latency) - Content-specific engagement (which features, pages, or content types drive engagement) ## Common Product Metrics ### DAU / WAU / MAU **What they measure**: Unique users who perform a qualifying action in a day, week, or month. **Key decisions**: - What counts as "active"? A login? A page view? A core action? Define this carefully — different definitions tell different stories. - Which timeframe matters most? DAU for daily-use products (messaging, email). WAU for weekly-use products (project management). MAU for less frequent products (tax software, travel booking). **How to use them**: - DAU/MAU ratio (stickiness): values above 0.5 indicate a daily habit. Below 0.2 suggests infrequent usage. - Trend matters more than absolute number. Is active usage growing, flat, or declining? - Segment by user type. Power users and casual users behave very differently. ### Retention **What it measures**: Of users who started in period X, what % are still active in period Y? **Common retention timeframes**: - D1 (next day): Was the first experience good enough to come back? - D7 (one week): Did the user establish a habit? - D30 (one month): Is the user retained long-term? - D90 (three months): Is this a durable user? **How to use retention**: - Plot retention curves by cohort. Look for: initial drop-off (activation problem), steady decline (engagement problem), or flattening (good — you have a stable retained base). - Compare cohorts over time. Are newer cohorts retaining better than older ones? That means product improvements are working. - Segment retention by activation behavior. Users who completed onboarding vs those who did not. Users who used feature X vs those who did not. ### Conversion **What it measures**: % of users who move from one stage to the next. **Common conversion funnels**: - Visitor to signup - Signup to activation (key value moment) - Free to paid (trial conversion) - Trial to paid subscription - Monthly to annual plan **How to use conversion**: - Map the full funnel and measure conversion at each step - Identify the biggest drop-off points — these are your highest-leverage improvement opportunities - Segment conversion by source, plan, user type. Different segments convert very differently. - Track conversion over time. Is it improving as you iterate on the experience? ### Activation **What it measures**: % of new users who reach the moment where they first experience the product's core value. **Defining activation**: - Look at retained users vs churned users. What actions did retained users take that churned users did not? - The activation event should be strongly predictive of long-term retention - It should be achievable within the first session or first few days - Examples: created first project, invited a teammate, completed first workflow, connected an integration **How to use activation**: - Track activation rate for every signup cohort - Measure time to activate — faster is almost always better - Build onboarding flows that guide users to the activation moment - A/B test activation flows and measure impact on retention, not just activation rate ## Goal Setting Frameworks ### OKRs (Objectives and Key Results) **Objectives**: Qualitative, aspirational goals that describe what you want to achieve. - Inspiring and memorable - Time-bound (quarterly or annually) - Directional, not metric-specific **Key Results**: Quantitative measures that tell you if you achieved the objective. - Specific and measurable - Time-bound with a clear target - Outcome-based, not output-based - 2-4 Key Results per Objective **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 ``` ### OKR Best Practices - Set OKRs that are ambitious but achievable. 70% completion is the target for stretch OKRs. - Key Results should measure outcomes (user behavior, business results), not outputs (features shipped, tasks completed). - Do not have too many OKRs. 2-3 objectives with 2-4 KRs each is plenty. - OKRs should be uncomfortable. If you are confident you will hit all of them, they are not ambitious enough. - Review OKRs at mid-period. Adjust effort allocation if some KRs are clearly off track. - Grade OKRs honestly at end of period. 0.0-0.3 = missed, 0.4-0.6 = progress, 0.7-1.0 = achieved. ### Setting Metric Targets - **Baseline**: What is the current value? You need a reliable baseline before setting a target. - **Benchmark**: What do comparable products achieve? Industry benchmarks provide context. - **Trajectory**: What is the current trend? If the metric is already improving at 5% per month, a 6% target is not ambitious. - **Effort**: How much investment are you putting behind this? Bigger bets warrant more ambitious targets. - **Confidence**: How confident are you in hitting the target? Set a "commit" (high confidence) and a "stretch" (ambitious). ## Metric Review Cadences ### Weekly Metrics Check **Purpose**: Catch issues quickly, monitor experiments, stay in touch with product health. **Duration**: 15-30 minutes. **Attendees**: Product manager, maybe engineering lead. **What to review**: - 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 - Alerts: anything that triggered a monitoring alert **Action**: If something looks off, investigate. Otherwise, note it and move on. ### Monthly Metrics Review **Purpose**: Deeper analysis of trends, progress against goals, strategic implications. **Duration**: 30-60 minutes. **Attendees**: Product team, key stakeholders. **What to review**: - 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? - Segment analysis: any divergence between user segments? **Action**: Identify 1-3 areas to investigate or invest in. Update priorities if metrics reveal new information. ### Quarterly Business Review **Purpose**: Strategic assessment of product performance, goal-setting for next quarter. **Duration**: 60-90 minutes. **Attendees**: Product, engineering, design, leadership. **What to review**: - OKR scoring for the quarter - Trend analysis for all L1 metrics over the quarter - Year-over-year comparisons - Competitive context: market changes and competitor movements - What worked and what did not **Action**: Set OKRs for next quarter. Adjust product strategy based on what the data shows. ## Dashboard Design Principles ### Effective Product Dashboards A good dashboard answers the question "How is the product doing?" at a glance. **Principles**: - **Start with the question, not the data**. What decisions does this dashboard support? Design backwards from the decision. - **Hierarchy of information**. The most important metric should be the most visually prominent. North Star at the top, L1 metrics next, L2 metrics available on drill-down. - **Context over numbers**. A number without context is meaningless. Always show: current value, comparison (previous period, target, benchmark), trend direction. - **Fewer metrics, more insight**. A dashboard with 50 metrics helps no one. Focus on 5-10 that matter. Put everything else in a detailed report. - **Consistent time periods**. Use the same time period for all metrics on a dashboard. Mixing daily and monthly metrics creates confusion. - **Visual status indicators**. Use color to indicate health at a glance: Green: on track or improving - Yellow: needs attention or flat - Red: off track or declining - **Actionability**. Every metric on the dashboard should be something the team can influence. If you cannot act on it, it does not belong on the product dashboard. ### Dashboard Layout **Top row**: North Star metric with trend line and target. **Second row**: L1 metrics scorecard — current value, change, target, status for each key metric. **Third row**: Key funnels or conversion metrics — visual funnel showing drop-off at each stage. **Fourth row**: Recent experiments and launches — active A/B tests, recent feature launches with early metrics. **Bottom / drill-down**: L2 metrics, segment breakdowns, and detailed time series for investigation. ### Dashboard Anti-Patterns - **Vanity metrics**: Metrics that always go up but do not indicate health (total signups ever, total page views) - **Too many metrics**: Dashboards that require scrolling to see. If it does not fit on one screen, cut metrics. - **No comparison**: Raw numbers without context (current value with no previous period or target) - **Stale dashboards**: Metrics that have not been updated or reviewed in months - **Output dashboards**: Measuring team activity (tickets closed, PRs merged) instead of user and business outcomes - **One dashboard for all audiences**: Executives, PMs, and engineers need different views. One size does not fit all. ### Alerting Set alerts for metrics that require immediate attention: - **Threshold alerts**: Metric drops below or rises above a critical threshold (error rate > 1%, conversion < 5%) - **Trend alerts**: Metric shows sustained decline over multiple days/weeks - **Anomaly alerts**: Metric deviates significantly from expected range **Alert hygiene**: - Every alert should be actionable. If you cannot do anything about it, do not alert on it. - Review and tune alerts regularly. Too many false positives and people ignore all alerts. - Define an owner for each alert. Who responds when it fires? - Set appropriate severity levels. Not everything is P0. Weekly Installs268Repository[anthropics/know…-plugins](https://github.com/anthropics/knowledge-work-plugins)GitHub Stars9.9KFirst SeenJan 31, 2026Security Audits[Gen Agent Trust HubPass](/anthropics/knowledge-work-plugins/metrics-tracking/security/agent-trust-hub)[SocketPass](/anthropics/knowledge-work-plugins/metrics-tracking/security/socket)[SnykPass](/anthropics/knowledge-work-plugins/metrics-tracking/security/snyk)Installed onclaude-code211opencode206codex200gemini-cli193github-copilot185cursor170 --- *Source: https://skills.yangsir.net/skill/sm-metrics-tracking* *Markdown mirror: https://skills.yangsir.net/api/skill/sm-metrics-tracking/markdown*