| name | metrics-dashboard |
| description | Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds. Use when creating a metrics dashboard, defining KPIs, setting up product analytics, or building a data monitoring plan. |
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? by Ben Yoskovitz
Instructions
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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
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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] |
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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] │
└─────────────────────────────────────────────┘
-
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
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Define alerts:
- What thresholds trigger investigation?
- Who gets alerted and through what channel?
- What's the expected response time?
-
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
