Event taxonomy is the structure that makes analytics trustworthy. If the names, properties, and ownership are messy, the dashboards will stay messy too.

This page is for teams trying to answer:

✓ What to name✓ What properties matter✓ How to keep it usable

See the builder See the next step

The structure matters before the dashboard does.

Event Taxonomy, Broken Down

01 — NamingClear event names that the team can understand later

02 — PropertiesThe fields that make each event useful for analysis

03 — OwnershipWho maintains the system when the product changes

04 — QAHow the team keeps instrumentation from drifting

If these four layers are weak, the analytics stack becomes harder to trust every month.

WHO THIS IS FOR

Teams with product data already in the stack, but no stable event structure that keeps the analysis honest.

WHAT THIS PAGE COVERS

What event taxonomy is, why it matters, where teams get it wrong, and what good looks like when the system is maintained.

BEST NEXT STEP

Start with the event taxonomy builder if you need the structure, or the PostHog health check if you need a quick audit.

What It Is

Event taxonomy is the naming system that keeps product analytics readable.

It defines what gets tracked, how it is named, which properties are attached, and who owns the structure. When that layer is clean, the rest of analytics gets easier to trust and easier to use.

Good taxonomy is not about tracking everything. It is about tracking the right actions in a way the team can understand months later. That means names that make sense, properties that add context, and a structure that survives product change.

Without that layer, analytics gets noisy fast. Teams end up with the same event represented three different ways, and nobody wants to make decisions from it.

Where Teams Get It Wrong

Most event taxonomies grow by accident.

The structure is usually fine early on, then it starts drifting as more teams add tracking on their own.

Names get added without a rule.

One team tracks `clicked_button`, another tracks `button_clicked`, and the same user action ends up fragmented across the stack.

Properties are missing the context that makes analysis useful.

Events exist, but they do not carry the business context the team needs to segment behavior or compare usage properly.

No one owns the system.

When taxonomy ownership is unclear, every new product change makes the tracking more inconsistent.

The QA process is missing.

Broken instrumentation stays broken long enough that the team stops trusting the data altogether.

What Good Looks Like

Three signs the structure is actually working.

01 — Clear names

The team can read the event and know what happened.

If the name makes sense without a tribal explanation, the taxonomy is in better shape.

02 — Useful context

The properties help the team answer real questions.

Good properties make it easier to compare users, accounts, plans, steps, and behaviors in a way the business can use.

03 — Stable ownership

The structure keeps improving instead of decaying.

Someone owns the rules, the QA, and the update path as the product evolves.

How ProductQuant Approaches It

Design the event layer before you build the dashboard.

A dashboard is only as good as the structure underneath it.

ProductQuant starts with the business question, then designs the event names and properties that answer it, then defines the QA process that keeps the system stable. That order prevents the usual problem where the stack looks complete but does not answer anything useful.

The goal is a clean, maintained system that helps product, growth, and analytics teams work from the same source of truth.

01 — Map