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A single measure is a fact. The Metric Tree captures how measures relate, so a top-line metric — ARR, labor cost %, contribution margin — decomposes into the driver metrics that explain it. It’s the layer of the World Model that turns “what happened” into “why,” and it’s built entirely from your semantic model definitions — there’s no separate file to maintain.

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How measures compose

Measures connect through two kinds of edges.

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Component edges

When one measure’s expression references another with {{view.measure}}, that reference is an edge. arr depends on net_mrr, which depends on total_mrr and churned_mrr — a tree the engine reads straight from the formulas. The operator in front of each reference (+, -, *, /) tells it whether the relationship is additive or multiplicative.

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Driver edges

Component edges are arithmetic. Driver edges — declared with drivers: — encode a business relationship: which metrics push a target up or down, how strongly, and how confident you are. They can be purely qualitative, or carry a quantitative coefficient and functional form.
A driver’s form sets the shape of the relationship: linear (constant marginal effect), log-log (elasticity — percent for percent), log-linear (diminishing returns), or linear-log (accelerating returns).

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Promotion up the hierarchy

Because the entity graph defines a parent: hierarchy (sale → store → company → market), a measure defined once at the sale grain automatically promotes upward: markets.net_sales is derived by rolling up sales.net_sales, never redefined. The tree holds not just how metrics compose, but at every level of the business they apply.

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Explore it in the IDE

The Metric Tree is a tab in the Semantic Layer IDE. It renders your measures as an interactive graph, laid out top-down:
  • Composite nodes (the metrics built from others) are highlighted; driver nodes are tinted amber; leaf inputs are muted.
  • Component edges are drawn solid; driver edges are dashed and animated, fainter when confidence is low.
  • Click any measure to select it — the graph dims unrelated edges and a side panel shows the measure’s definition and its ranked drivers, sorted by influence.

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What you can do with it

Because the tree knows how every metric is built, it powers four operations over your metrics:

Sensitivity

Rank the drivers of any measure by how much they move it — the effective coefficient compounded along each path.

Predict

What-if propagation: change a driver by some delta and see the estimated impact ripple up to the top-line metric.

Explain (RCA)

Decompose a metric’s period-over-period change across its drivers and the segments that concentrate it — with warnings for Simpson’s paradox and offsetting effects. Anomalies surface in the Insights Inbox with an AI-authored root cause. Available today.

Opportunity

Size the gap between each segment and its benchmark, and propagate the top opportunity through the tree. On the roadmap.
For example, an explain on labor_cost_pct for a location might read:

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Next steps

Measures

Define the measures — and their drivers — that the tree composes

Entities & relationships

The hierarchy measures promote across

World Model

How the metric tree fits with the semantic model and entity graph