Product Analytics Department System
A product analytics team measuring product usage and performance and turning numbers into documented decisions.
From metric to decision: measurement, analysis, hypotheses, and experiments — decisions built on data, not impressions.
No credit card required — your project is set up with the full system in under a minute
This is the system board as you'll receive it: workflow columns and starter cards showing the first steps
How does work flow in this system?
The product’s core metrics are defined as cards in “Metric”: adoption, retention, conversion — each with a definition, data source, and owner. Readings are updated periodically in “Measurement”, and any unexplained movement moves to “Analysis”, where the analyst breaks it down: which user segment? Which step in the journey? When did it start? A mature analysis produces a “Hypothesis” in falsifiable form — changing X will raise Y because Z — tested in “Experiment” with scope, duration, and success criteria set in advance. The outcome is ruled on in “Decision”: adopt or drop; then rolls into “Documentation”, so whoever comes later knows what we tried and what we learned.
The Forum is the data council: metric dashboards are published and debated periodically, and hypotheses are presented before testing for peer review — is the measurement sound? Is there confounding? — so experiments grow stronger before engineering effort is spent on them. Adopt-or-drop decisions are documented with their numbers in permanent topics, keeping the company’s analytical record alive instead of reports read once and buried.
The product analyst owns the card’s journey from “Measurement” to “Experiment”: updating readings, breaking down deviations, and designing experiments. The product manager receives “Decision” and converts it into the roadmap, requesting new analyses with specific questions. The data engineer keeps the pipelines and definitions sound so no decision is built on a broken number. The golden rule: nothing enters “Experiment” without written success criteria before it starts.
Who does what?
The operational roles in this system and each role's responsibility in daily work — assign them to your team as-is or adapt them to your reality.
Product Analyst
Updates metric readings periodically, breaks down deviations in “Analysis”, and designs hypotheses and experiments with pre-set success criteria.
Product Manager
Asks the analytical questions that matter, receives results, and turns “Decision” into roadmap priorities.
Data Engineer
Keeps data sources and metric definitions sound, fixing any outage before it contaminates the readings.
What's prepared for you from day one?
System units
- Tasks — A kanban board with workflow columns and execution cards
- Forum — Documented discussions in organized sections — decisions and knowledge that never get lost
Forum sections (4)
- Metrics CouncilPublishing and debating periodic readings of core metrics — what moved, why, and what we do about it.
- Hypotheses Under ReviewPresenting hypotheses before experimenting, for collective review of method and measurement before effort is spent.
- Experiment ResultsAn archive of completed experiments, successful and failed, with their numbers — so no forgotten experiment is repeated.
- Definitions & MethodologiesThe metric dictionary: each metric’s definition, source, and calculation method — one language for numbers.
«Working Rules for This System» — Pinned in the forum
1) Every metric has a written definition, a data source, and an owner — no vague metrics on the board. 2) Nothing enters “Experiment” without a written hypothesis and success criteria set before it starts. 3) Experiment results are documented in the Forum whether they succeeded or failed, and closed with an explicit decision. 4) Periodic readings are updated on schedule, and any suspect number is flagged until the data engineer fixes it. 5) Major decisions cite their supporting numbers — an opinion without data does not enter “Decision”.
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