Guide
What is product analytics?
Product analytics is the measurement of what users actually do inside software — how many are active, how often they return, which features they reach and where they stop — as distinct from web analytics, which measures how they arrived.
Active users: DAU, WAU and MAU
Daily, weekly and monthly active users count distinct people who did something meaningful in a period. The definition of "something meaningful" is yours to set, and setting it badly is the most common way these numbers become useless. The DAU-to-MAU ratio is a rough measure of how habitual your product is.
Events and features
An event is one thing a user did. A feature is a named group of events. Adoption is the share of active users who triggered a given feature in a period — and distinct users, not event count, is the number that means something.
Why engineers should care
Product analytics answers questions engineering asks constantly and usually guesses at: whether anyone uses the feature about to be rewritten, whether the bug in an obscure code path is worth the sprint, and whether last release changed behaviour or only opinion.
Where ErrorDock fits
ErrorDock collects product analytics through the same SDK as errors, so feature usage can be read beside errors and latency when deciding what to fix.
Related
- Understand the people behind the events. Connect active users, sessions and product events to reliability, so usage and failures become part of the same engineering conversation.
- What is feature analytics? Feature analytics measures adoption of individual features — including the ones nobody uses but everybody still maintains.
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