Metrics dashboard skill
Define and design a product metrics dashboard with key metrics, data sources, visualization types, and alert thresholds.
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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
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
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] 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
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
- The Ultimate List of Product Metrics
- The North Star Framework 101
- The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs
- AARRR (Pirate) Metrics: The 5-Stage Framework for Growth
- The Google HEART Framework: Your Guide to Measuring User-Centric Success
- Funnel Analysis 101: How to Track and Optimize Your User Journey
- Are You Tracking the Right Metrics?
- Continuous Product Discovery Masterclass (CPDM) (video course)
| 1 | |
| 2 | name metrics-dashboard |
| 3 | 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." |
| 4 | |
| 5 | |
| 6 | ## Product Metrics Dashboard |
| 7 | |
| 8 | Design a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds. |
| 9 | |
| 10 | ### Context |
| 11 | |
| 12 | You are designing a metrics dashboard for **$ARGUMENTS**. |
| 13 | |
| 14 | If the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first. |
| 15 | |
| 16 | ### Domain Context |
| 17 | |
| 18 | **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. |
| 19 | |
| 20 | **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." |
| 21 | |
| 22 | **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). |
| 23 | |
| 24 | **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. |
| 25 | |
| 26 | For case studies and more detail: [Are You Tracking the Right Metrics?] by Ben Yoskovitz |
| 27 | |
| 28 | ### Instructions |
| 29 | |
| 30 | **Identify the metrics framework** — organize metrics into layers: |
| 31 | |
| 32 | **North Star Metric**: The single metric that best captures core value delivery |
| 33 | |
| 34 | **Input Metrics** (3-5): The levers that drive the North Star |
| 35 | |
| 36 | **Health Metrics**: Guardrails that ensure overall product health |
| 37 | |
| 38 | **Business Metrics**: Revenue, cost, and unit economics |
| 39 | |
| 40 | **For each metric, define**: |
| 41 | |
| 42 | | Metric | Definition | Data Source | Visualization | Target | Alert Threshold | |
| 43 | |---|---|---|---|---|---| |
| 44 | | [Name] | [Exact calculation: numerator/denominator, time window] | [Where the data comes from] | [Line chart / Bar / Number / Funnel] | [Goal value] | [When to trigger an alert] | |
| 45 | |
| 46 | **Design the dashboard layout**: |
| 47 | |
| 48 | |
| 49 | ┌─────────────────────────────────────────────┐ |
| 50 | │ NORTH STAR: [Metric] — [Current Value] │ |
| 51 | │ Trend: [↑/↓ X% vs last period] │ |
| 52 | ├──────────────────┬──────────────────────────┤ |
| 53 | │ Input Metric 1 │ Input Metric 2 │ |
| 54 | │ [Sparkline] │ [Sparkline] │ |
| 55 | ├──────────────────┼──────────────────────────┤ |
| 56 | │ Input Metric 3 │ Input Metric 4 │ |
| 57 | │ [Sparkline] │ [Sparkline] │ |
| 58 | ├──────────────────┴──────────────────────────┤ |
| 59 | │ HEALTH: [Latency] [Error Rate] [NPS] │ |
| 60 | ├─────────────────────────────────────────────┤ |
| 61 | │ BUSINESS: [MRR] [CAC] [LTV] [Churn] │ |
| 62 | └─────────────────────────────────────────────┘ |
| 63 | |
| 64 | |
| 65 | **Set review cadence**: |
| 66 | **Daily**: Operational health (errors, latency, critical flows) |
| 67 | **Weekly**: Input metrics and engagement trends |
| 68 | **Monthly**: North Star, business metrics, OKR progress |
| 69 | **Quarterly**: Strategic review and metric recalibration |
| 70 | |
| 71 | **Define alerts**: |
| 72 | What thresholds trigger investigation? |
| 73 | Who gets alerted and through what channel? |
| 74 | What's the expected response time? |
| 75 | |
| 76 | **Recommend tools** based on the user's context: |
| 77 | Amplitude, Mixpanel, PostHog for product analytics |
| 78 | Looker, Metabase, Mode for SQL-based dashboards |
| 79 | Datadog, Grafana for operational health |
| 80 | |
| 81 | Think step by step. Save the dashboard specification as a markdown document. |
| 82 | |
| 83 | |
| 84 | |
| 85 | ### Further Reading |
| 86 | |
| 87 | [The Ultimate List of Product Metrics] |
| 88 | [The North Star Framework 101] |
| 89 | [The Product Analytics Playbook: AARRR, HEART, Cohorts & Funnels for PMs] |
| 90 | [AARRR (Pirate) Metrics: The 5-Stage Framework for Growth] |
| 91 | [The Google HEART Framework: Your Guide to Measuring User-Centric Success] |
| 92 | [Funnel Analysis 101: How to Track and Optimize Your User Journey] |
| 93 | [Are You Tracking the Right Metrics?] |
| 94 | [Continuous Product Discovery Masterclass (CPDM)] (video course) |
| 95 |
Discussion
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