Product analytics
Product analytics is the collection and analysis of data about how people use a product: which actions they take, in what order, how often and whether they come back. It relies on events, such as “invoice sent”, carrying properties that describe their context, sent from the app or the server to an analytics tool or a data warehouse. Funnels, retention cohorts and segment comparisons are its core analyses.
Why it matters for a PM
Product analytics lets a PM check whether a feature is used as intended, where users drop off and which behaviors predict long-term retention. Its value depends entirely on the instrumentation, which is why PMs own the tracking plan: the list of events and properties derived from the questions the team needs to answer, with clear names and acceptance criteria.
Example
A PM wants to know whether a new onboarding checklist helps. The tracking plan adds events for each checklist step and for the first project created. A funnel then shows where new users stop, and a retention cohort compares users who completed the checklist with those who skipped it.
Key points
- Start from decisions and questions, then define the events, not the other way around.
- A tracking plan documents each event's name, trigger, properties and owner.
- Identity matters: linking anonymous visits to accounts, and users to companies in B2B products.
- Data quality needs checks at each release and monitoring afterward.
- Collection has to respect consent and data minimization rules such as the GDPR.
Common mistakes
- Tracking everything “just in case”, which produces noisy, unusable data.
- Naming events inconsistently across platforms, so the same action is counted twice or not at all.
- Reading a correlation in a dashboard as proof that a feature caused an outcome.
Go further with Module
The courses and lessons that cover this concept: