Design a product metrics dashboard with clearly defined measures, data sources, charts, targets, and alert thresholds. The skill produces a Markdown specification that also covers dashboard layout, review schedules, and responsibilities for responding to alerts.
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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.
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Design a comprehensive product metrics dashboard with the right metrics, visualizations, and alert thresholds.
You are designing a metrics dashboard for $ARGUMENTS.
If the user provides files (existing dashboards, analytics data, OKRs, or strategy docs), read them first.
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
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:
Define alerts:
Recommend tools based on the user's context:
Think step by step. Save the dashboard specification as a markdown document.
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Analyze A/B test results to decide whether to launch a change, extend testing, stop, or investigate further. Check sample size, statistical significance, uncertainty ranges, and effects on other important metrics, then summarize the recommendation and next steps.
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Organize customer feature requests into themes and rank the top three against product goals, customer value, effort, and risk. For each recommendation, explain the underlying need, possible alternatives, uncertain assumptions, and simple ways to test them.
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Compare growth options across existing and new products and markets using the Ansoff Matrix, a four-part planning framework. Assess opportunities, resources, competition, and risks, then develop market launch plans and a phased roadmap for priority options.
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Choose the first customer segment for a product launch by comparing urgent needs, willingness to pay, achievable market share, and referral potential. Get scored recommendations, a priority segment, a 90-day acquisition plan, and expansion options.
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Plan low-effort experiments to check assumptions about an existing product before building a feature. Compare prototypes, A/B tests, and other methods, with a clear assumption, procedure, metric, and success threshold for each experiment.
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Turn a new product idea into a testable hypothesis and two or three simple experiments. Define target customers, expected actions, measurement methods, and success thresholds to assess demand through behavior such as sign-ups, pre-orders, or paid commitments.
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