What you will learn
- How event taxonomy design shapes every later analysis
- How funnels, retention, and cohorts answer different questions
- The build order from tracking to analysis to experimentation
- Why experiment analysis needs the same rigour as the experiment itself
What this project is
Product teams live on event data: signups, activations, purchases, churn. But event data collected without design becomes a swamp of inconsistent names, missing properties, and duplicate events, and analysts spend their time cleaning instead of answering. The platform's first job is therefore taxonomy: what events exist, what they mean, and what properties each carries.
Analysis follows from taxonomy. Funnels show where users drop off, retention shows who stays, cohorts show whether newer users behave differently, and experiment analysis shows whether a change caused a difference or merely coincided with one. Each needs clean events and stated definitions to be believed.
Core features
- Event taxonomy management with naming rules, properties, and ownership per event.
- SDKs and tracking validation that reject malformed events before they pollute data.
- Funnel, retention, and cohort analysis built on governed event definitions.
- Experiment analysis with assignment records, guardrail metrics, and significance handling.
- User journey views tracing paths through events for individuals and segments.
- Data quality checks on event volume, property completeness, and naming compliance.
How it is built
- Taxonomy first. Event names, properties, and owners for the core user journey, agreed with product teams.
- Tracking second. SDKs, validation, and quality checks on live events.
- Core analysis third. Funnels and retention on governed events.
- Experimentation fourth, once event quality and assignment records are reliable.
- Advanced analysis last. Journeys, predictions, and segment-level views.
Technology considerations
Event volume is high and bursty, so ingestion must handle spikes without losing events, and storage must support both raw event queries and fast aggregates. Tracking validation should run at ingestion and in SDKs, since bad events are cheapest to stop at the source. Experiment assignment needs a durable record linking users to variants, or analysis cannot be reproduced. Costs scale with event volume; confirm with a quote.
- High-throughput event ingestion with durable buffering.
- Taxonomy registry with validation at ingestion.
- Aggregate stores for funnels, retention, and cohorts.
- Experiment assignment records kept for reproducible analysis.
Challenges to plan for
- Taxonomy drift. Teams rename events informally, breaking history, so changes need versioning and migration rules.
- Duplicate and missing events. SDK bugs and ad blockers distort counts, so quality checks must flag anomalies.
- Experiment peeking. Checking results repeatedly inflates false positives, so analysis needs stated stopping rules.
- Privacy. Event data identifies behaviour, so consent, retention, and access controls belong in the design.
A worked example
A mobile app team cannot tell whether its new onboarding flow helps or hurts, because event names differ between the old and new flows and nobody documented either. The team rebuilds its taxonomy: agreed event names for onboarding steps, with properties for device, entry point, and experiment variant, owned by the product team and validated at ingestion so malformed events are rejected, not silently stored.
With governed events, funnels show exactly where users drop off in each onboarding variant, retention curves show whether completers stay longer, and experiment analysis, backed by durable assignment records, shows the new flow lifting activation by a real margin rather than a seasonal coincidence. When a developer later renames an event informally, ingestion validation flags it the same day, and the change goes through taxonomy versioning with a migration plan instead of breaking history. The team's roadmap arguments now cite funnel and retention evidence instead of opinions.
The app team's next experiment, a redesigned paywall, runs on the same governed foundation, and the difference shows in the analysis meeting. Assignment records prove the variant groups were balanced, funnel data shows where each variant loses users, and retention curves show whether paying users stay or churn after the first month. The team ships the variant that lifts retained revenue, not the one that merely lifts initial purchases, and the decision record cites the experiment analysis rather than anyone's intuition about the design.
Who it is for, and its limits
This fits product teams making roadmap and growth decisions from user behaviour, and companies running frequent experiments.
It does not fit teams with low traffic, where qualitative research and simple analytics suffice. It also does not replace product judgment; it informs it with evidence.
Frequently asked questions
What is event taxonomy?
The agreed set of event names, properties, and meanings for a product, owned and versioned so analysis stays consistent over time.
How do funnels and retention differ?
Funnels show steps toward one goal and where users drop off. Retention shows who returns over time after first use.
Why does experiment analysis need assignment records?
Because without knowing who saw which variant and when, results cannot be reproduced or trusted.
How are tracking errors caught?
Through ingestion validation, volume and completeness checks, and alerts when an event's shape or volume changes suddenly.
When is product analytics overkill?
With very low traffic or a single simple funnel, basic analytics plus user interviews usually answer the questions faster.
Why does event taxonomy come before analysis?
Because analysis built on inconsistent event names measures nothing reliably. Clean, owned, versioned events are what make funnels, retention, and experiments trustworthy.