KPI Dashboard Design & Metric Tree
Designs a focused KPI framework with a metric tree linking a north-star goal to leading and operational drivers.
Prompt
ROLE: You are a business-intelligence and operations consultant who designs KPI systems that drive behavior, not vanity dashboards. CONTEXT: The team/business is [NAME, FUNCTION]. The overarching goal we're trying to move is [NORTH-STAR / PRIMARY OUTCOME]. The data we can realistically capture: [AVAILABLE METRICS / SYSTEMS]. The audience for the dashboard: [WHO USES IT AND HOW OFTEN]. TASK: 1. Define one north-star metric that best represents value created, and explain why it's the right top-line. 2. Build a metric tree: decompose the north-star into 3-5 lagging outcome metrics, then into the leading/input metrics that teams can actually influence weekly. 3. For each KPI, specify: definition (no ambiguity), formula, target/benchmark, owner, and review cadence. 4. Distinguish leading from lagging indicators and explain how the leading ones predict the lagging ones. 5. Identify the 'guardrail' metrics that must not degrade while we chase the goal (e.g., quality, cost, burnout). 6. Recommend the minimal dashboard layout: what's on the executive view vs. the operational view. OUTPUT FORMAT: - North-star metric + rationale - Metric tree (visualized as nested bullets) - KPI definition table (KPI | Formula | Target | Owner | Cadence | Leading/Lagging) - Guardrail metrics - Dashboard layout recommendation CONSTRAINTS: Fewer metrics, better — resist a wall of numbers. Every KPI must be actionable by someone; cut metrics nobody owns. Define formulas precisely so two people compute them identically. Flag any metric that is easily gamed and how to counter it. Note where the needed data isn't yet captured.
How to use this prompt
- 1
Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.
- 2
Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.
- 3
Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.
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Build on this prompt
Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.
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