PlaybookPrompts

Build a KPI tree from a top-level business goal

Data & Analysis kpismetricsstrategy

Before you build a dashboard, you need to know which metrics actually connect to the goal you care about. This prompt constructs a structured KPI tree that shows drivers, lag and lead indicators, and data-source requirements.

Prompt
You are a strategic analytics consultant. I need to build a KPI tree that connects a top-level business goal to the specific metrics my team should track and own.

Business goal: {{BUSINESS_GOAL}}
Business model context (e.g., SaaS, marketplace, e-commerce, media): {{BUSINESS_MODEL}}
Teams that will use this KPI tree: {{TEAMS}}
Time horizon for the goal: {{TIME_HORIZON}}

Follow these steps:
1. Restate the top-level goal as a single, measurable North Star metric with a clear unit (e.g., monthly recurring revenue in USD, weekly active users).
2. Decompose the North Star into 3–5 Level 2 driver metrics. For each, explain the causal link to the North Star in one sentence.
3. For each Level 2 driver, list 2–3 Level 3 input metrics (the levers teams can actually influence week to week).
4. For each metric at all levels, note: (a) whether it is a lead or lag indicator, (b) the likely data source, and (c) the team responsible.
5. Flag any driver metric that is important but likely difficult to measure with standard tooling, and suggest a proxy.
6. Identify one metric at each level that is commonly tracked but is actually a vanity metric for this specific goal — and explain why.

Format the output as an indented outline, not a prose paragraph.
Variables to fill in
  • {{BUSINESS_GOAL}}
  • {{BUSINESS_MODEL}}
  • {{TEAMS}}
  • {{TIME_HORIZON}}

How to use this prompt

  1. Copy the prompt above (Copy button on the top-right).
  2. Replace each {{VAR}} with your own value. Variables: {{BUSINESS_GOAL}}{{BUSINESS_MODEL}}{{TEAMS}}{{TIME_HORIZON}}.
  3. Paste it into one of the recommended tools below.
  4. Iterate: tighten constraints in the prompt if the output is generic.

Why this prompt is structured this way

The prompt is split into explicit steps because LLMs do better when the path is named, not implied. Each variable forces specificity at the input layer — vague inputs get vague outputs.

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