The leader track π€ Β· for C-level, managers & curious minds Β· 6 x 45 min Β· no code to write
Thinking mode: why a top-line number names an outcome not a cause, the difference between leading drivers you steer by and lagging outcomes you report, how to read a drop off a tree in thirty seconds, and how to run the weekly business review and monthly summit as decomposition rituals - each session ends with questions to ask your data team.
Why a number needs a tree
A top-line metric is an outcome, not a cause. The driver-tree mindset: break every number into the levers that move it, each with an owner.
π¦ Leader 2 Β· easyLeading vs lagging
Lagging outcomes report late; leading drivers warn early. Which numbers you steer by and which you only report - and why the difference matters.
πͺ Leader 3 Β· easyDecompose and read a drop
The three decomposition patterns and the diagnosis walk: given a fall, trace it down the tree to the single driver that moved.
πΊοΈ Leader 4 Β· mediumThe six domains side by side
One driver tree per business model - ecommerce, marketing, branding, traffic, B2B SaaS, B2G - so you read any team's top line the same way.
π Leader 5 Β· mediumThe weekly business performance review
Run the Amazon-style weekly review as a tree-reading ritual: glance at the drivers, spot what moved, route the question before the meeting ends.
ποΈ Leader 6 Β· mediumThe monthly leadership summit review
The monthly zoom-out: trees across the business on one page, Goodhart's law when a driver becomes a target, and metric governance.
The builder track π οΈ Β· for practitioners Β· 10 sessions x 45 min (b10: 60) Β· live driver-tree simulator + DuckDB SQL
One skill, ten sessions: take any top-line metric, break it into a driver tree, and diagnose a drop in minutes. Every page runs a live driver-tree simulator - edit a leaf and the top line recomputes; press "Simulate a drop" and the guilty driver's path lights coral so you practise tracing a fall back to its cause. Then real DuckDB via WebAssembly segments the guilty driver with live SQL to find the culprit slice. You start with a GMV tree (b1), learn the three decomposition patterns (b2) and the leading-indicator diagnosis playbook (b3), then apply all of it to six domains one at a time (b4-b9) before a capstone where a metric drops and you diagnose it end to end (b10). The engine loads once from a CDN (about 8 MB, cached); everything after that is offline.
Your first driver tree
Build GMV = Traffic x Conversion x AOV live, edit a leaf and watch the top line move, then simulate a drop and read the coral path back to the cause.
π§© Builder 2 Β· easyThe three decomposition patterns
Multiply, add, and the flow bridge. Decide which pattern any metric follows, and build a tree in each shape in the simulator.
π Builder 3 Β· easyLeading indicators and the diagnosis playbook
Push a tree deep enough that the leaves warn early, then run the repeatable playbook that takes a drop to a root cause.
π Builder 4 Β· mediumEcommerce deep dive
Decompose GMV into its real drivers, simulate a drop, then segment the guilty driver with live SQL to find which channel fell.
π£ Builder 5 Β· mediumMarketing deep dive
Build the funnel tree - spend to impressions to clicks to leads to CAC - and find exactly where the funnel leaks when performance dips.
β¨ Builder 6 Β· mediumBranding deep dive
Decompose the softer metrics: the brand funnel from awareness to preference, and share of search as a leading driver of demand.
π Builder 7 Β· mediumInternet and traffic deep dive
Ad revenue and the DAU bridge: decompose an audience business into the drivers of reach, engagement, and monetisation.
π³ Builder 8 Β· mediumB2B SaaS deep dive
The ARR bridge - new, expansion, contraction, churn - plus pipeline coverage as the leading driver of the number next quarter.
ποΈ Builder 9 Β· mediumB2G deep dive
Decompose government bookings and the tension every public-sector tree carries: mission outcomes versus money, both on the same page.
π Builder 10 Β· hands-on Β· 60 minCapstone: diagnose the drop
A metric drops with no explanation. Build the tree, simulate, segment, and write the weekly-business-review narrative that names the cause.
Metric decomposition is the reading skill under every dashboard. It pairs with learn-business-intelligence-with-phoebe (the dashboards these trees live in), sits on top of learn-data-warehouse-with-phoebe (where the numbers come from), and goes deeper on one domain in learn-marketing-attribution-with-phoebe.
Built from Lean Analytics (Croll & Yoskovitz), DuPont driver-tree analysis, the Amplitude North Star Playbook, and the Amazon Weekly Business Review - frameworks applied live in the simulator, not just described.
The knowledge map π§
Both tracks at a glance - hover a session to spotlight its concepts, click any node to jump in.