Metric Tree Builder Skill

A north-star metric you can't decompose is a number you can't move. This skill breaks it into the multiplicative/additive drivers beneath it, down to metrics a team can actually act on — and points at the highest-leverage levers.

Working from a brief

Given a top metric and a rough business model, build the full tree anyway, inferring the standard driver structure for that model and marking assumptions. Never stop at one level; push down to input metrics someone owns.

Required Inputs

Ask for (if not already provided):

• The north-star / top metric (e.g. weekly active revenue, MRR, GMV, activated users)
• Business model (subscription, marketplace, ads, transactional, freemium)
• Where the team can act (which teams own which surfaces)
• Current pain (the metric is flat / dropping — optional, focuses the tree)

Output Format

1. The decomposition

Express the top metric as an equation of its drivers, e.g.: Revenue = New customers × Avg first order + Retained customers × Repeat rate × AOV Then break each driver down a level or two, until you reach input metrics a team can directly influence (e.g. signup conversion, activation rate, email open→click, time-to-value).

Show it as an indented tree or a table:

Level	Metric	Driven by	Owner / lever
0	North star	—
1	Driver	sub-inputs
2	Input metric	actions	team

2. Relationships

Note where drivers are multiplicative (a small % gain compounds) vs additive, and any that trade off against each other.

3. Highest-leverage levers

The 2–3 input metrics where a realistic improvement moves the north star most — and why (sensitivity × how movable it is).