metrics-dashboard
This Claude Code skill creates a structured framework for designing product metrics dashboards by organizing metrics into layers (North Star Metric, Input Metrics, Health Metrics, Business Metrics) and defining each metric's calculation, data source, visualization type, target, and alert threshold. Use it when establishing KPIs for a new product, auditing existing analytics to identify actionable metrics, setting up data monitoring systems, or aligning teams around customer-centric measurement frameworks.
git clone --depth 1 https://github.com/phuryn/pm-skills /tmp/metrics-dashboard && cp -r /tmp/metrics-dashboard/pm-product-discovery/skills/metrics-dashboard ~/.claude/skills/metrics-dashboardSKILL.md
## Product Metrics Dashboard Design a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds. ### Context You are designing a metrics dashboard for **$ARGUMENTS**. If the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first. ### Domain Context **Metrics vs KPIs vs NSM**: Metrics = all measurable things. KPIs = a few key quantitative metrics tracked over a longer period. North Star Metric = a single customer-centric KPI that is a leading indicator of business success. **4 criteria for a good metric** (Ben Yoskovitz, *Lean Analytics*): (1) Understandable — creates a common language. (2) Comparative — over time, not a snapshot. (3) Ratio or Rate — more revealing than whole numbers. (4) Behavior-changing — the Golden Rule: "If a metric won't change how you behave, it's a bad metric." **8 metric types**: Vanity vs Actionable (only actionable metrics change behavior), Qualitative vs Quantitative (WHAT vs WHY — you need both; never stop talking to customers), Exploratory vs Reporting (explore data to uncover unexpected insights), Lagging vs Leading (leading indicators enable faster learning cycles, e.g. customer complaints predict churn). **5 action steps**: (1) Audit metrics against the 4 good-metric criteria. (2) Update dashboards — ensure all key metrics are good ones. (3) Identify vanity metrics — be careful how you use them. (4) Classify leading vs lagging indicators. (5) Pick one problem and dig deep into the data. For case studies and more detail: [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics) by Ben Yoskovitz ### Instructions 1. **Identify the metrics framework** — organize metrics into layers: **North Star Metric**: The single metric that best captures core value delivery **Input Metrics** (3-5): The levers that drive the North Star **Health Metrics**: Guardrails that ensure overall product health **Business Metrics**: Revenue, cost, and unit economics 2. **For each metric, define**: | Metric | Definition | Data Source | Visualization | Target | Alert Threshold | |---|---|---|---|---|---| | [Name] | [Exact calculation: numerator/denominator, time window] | [Where the data comes from] | [Line chart / Bar / Number / Funnel] | [Goal value] | [When to trigger an alert] | 3. **Design the dashboard layout**: ``` ┌─────────────────────────────────────────────┐ │ NORTH STAR: [Metric] — [Current Value] │ │ Trend: [↑/↓ X% vs last period] │ ├──────────────────┬──────────────────────────┤ │ Input Metric 1 │ Input Metric 2 │ │ [Sparkline] │ [Sparkline] │ ├──────────────────┼──────────────────────────┤ │ Input Metric 3 │ Input Metric 4 │ │ [Sparkline] │ [Sparkline] │ ├──────────────────┴──────────────────────────┤ │ HEALTH: [Latency] [Error Rate] [NPS] │ ├─────────────────────────────────────────────┤ │ BUSINESS: [MRR] [CAC] [LTV] [Churn] │ └─────────────────────────────────────────────┘ ``` 4. **Set review cadence**: - **Daily**: Operational health (errors, latency, critical flows) - **Weekly**: Input metrics and engagement trends - **Monthly**: North Star, business metrics, OKR progress - **Quarterly**: Strategic review and metric recalibration 5. **Define alerts**: - What thresholds trigger investigation? - Who gets alerted and through what channel? - What's the expected response time? 6. **Recommend tools** based on the user's context: - Amplitude, Mixpanel, PostHog for product analytics - Looker, Metabase, Mode for SQL-based dashboards - Datadog, Grafana for operational health Think step by step. Save the dashboard specification as a markdown document. --- ### Further Reading - [The Ultimate List of Product Metrics](https://www.productcompass.pm/p/the-ultimate-list-of-product-metrics) - [The North Star Framework 101](https://www.productcompass.pm/p/the-north-star-framework-101) - [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs](https://www.productcompass.pm/p/the-product-analytics-playbook-aarrr) - [AARRR (Pirate) Metrics: The 5-Stage Framework for Growth](https://www.productcompass.pm/p/aarrr-pirate-metrics) - [The Google HEART Framework: Your Guide to Measuring User-Centric Success](https://www.productcompass.pm/p/the-google-heart-framework) - [Funnel Analysis 101: How to Track and Optimize Your User Journey](https://www.productcompass.pm/p/funnel-analysis) - [Are You Tracking the Right Metrics?](https://www.productcompass.pm/p/are-you-tracking-the-right-metrics) - [Continuous Product Discovery Masterclass (CPDM)](https://www.productcompass.pm/p/cpdm) (video course)
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