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Daily management

KPI tree

KPI tree is licensed CC BY 4.0. Attribution: Katafacts (katafacts.com).

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1 · What it is

What it is

A KPI tree is what a daily or weekly huddle actually tracks: goals, broken into driver metrics that genuinely move them, each with a specific target and how often it's reviewed. State your goals, and a real tree gets drafted — but only with targets the input actually supports, never an invented number to fill the field.

2 · When to use it

When to use it — and when not to

Use it when

  • You want a huddle board or daily standup to track something real, not a vague sense of 'how are we doing.'
  • You have goals but haven't broken them into the specific metrics that actually drive them.
  • You want to know honestly which goals still have no real, checkable target.

Not when

  • You don't have real goals yet, just a general sense you should 'track more metrics' — sharpen the goals first.
  • The driver metric would just restate the goal — a real driver metric is something specific enough to move independently.
  • You'd be forcing a target onto a metric with no real baseline yet — leave it open and set the target once you have one.

3 · How to fill it in

How to fill it in

Goals → driver metrics → targets
List each goal. Driver metrics and targets are drafted beneath each one.
Coverage
Computed for you: total goals, driver metrics, and targets, and which goals still have no target — nothing to enter here.
Narrative
Drafted from the tree and any coverage gaps.

4 · What good looks like

What good looks like

The example below tracks the same renewal-risk support process running through this catalogue's kaizen charter, sustainment audit, work instruction, and ILUO matrix examples — one driver metric even references the ILUO matrix's own coverage number directly. The third goal is honestly left with a driver metric but no target yet.

Same example, as a downloadable xlsx workbook.

Download .xlsx

KPI tree

Renewal-risk support — huddle KPI tree

Support — renewal-risk queue

Priya Nair · 2026-09-28

Goals → driver metrics → targets

Keep renewal-risk accounts from stalling in support

Median first-response time for renewal-risk tickets

  • < 4 hoursDaily in the huddle

Reopened-ticket rate for renewal-risk tickets

  • < 5%Weekly in the huddle

Grow the share of renewal-risk tickets handled by a fully trained responder

Percent of renewal-risk tickets handled by someone at U or O level

  • ≥ 80%Weekly, against the ILUO training matrix

Reduce onboarding-driven support loadNo target yet

Number of setup-related tickets in the first 14 days after signup

    Coverage

    Goals

    3

    Driver metrics

    4

    Targets

    3

    No target yet: Reduce onboarding-driven support load

    Narrative

    Two of three goals have real targets tracked on a set cadence — renewal-risk response time and reopened-ticket rate are reviewed daily and weekly, and unsupervised-coverage on renewal-risk tickets ties directly back to the ILUO training matrix. The third goal, reducing onboarding-driven support load, has a driver metric named but no target yet — worth setting one once a real baseline is established.

    5 · Common mistakes

    Common mistakes

    • Writing a driver metric that's really just the goal restated in metric language.

      A real driver metric can move independently and gives the team something specific to act on — a restated goal gives them nothing to actually do differently.

    • Inventing a target to fill the field when there's no real baseline yet.

      A made-up target looks like rigor but isn't — it's better to leave the gap visible and set a real target once the data exists.

    • Building the tree once and never revisiting it as goals change.

      A KPI tree is a living huddle artifact, not a one-time analysis — re-run it as goals shift or targets get hit.

    6 · What it connects to

    What it connects to

    upstream

    • ILUO training matrix

      A driver metric can directly track something the ILUO matrix already measures, like coverage — the worked example does exactly this.

    downstream

      7 · Where AI helps

      Where AI helps

      Judgement — stays yours

      • Deciding whether a drafted target is realistic given real team capacity
      • Deciding which coverage gap to close first

      Analysis — AI helps

      • Drafting driver metrics and, where supportable, real targets from a stated goal
      • Drafting the narrative from the tree and any coverage gaps

      Drudgery — automated

      • Counting goals, driver metrics, and targets
      • Identifying which goals still have no target
      • Exporting to xlsx in the house format

      9 · Rate this kata

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