Product analytics
Contents
Product analytics answers what people actually do in your product. Build trends, funnels, retention, paths, stickiness, and lifecycle insights on the events you already send to PostHog, then save them to dashboards, share them, and get alerted when they move.
Because it runs on the same events, persons, and properties as the rest of PostHog, every number you look at is one click away from the session replay behind it, the feature flag that gated it, or the experiment that changed it. Those same insights feed Self-driving, which watches your saved insights for regressions and files a report when one starts slipping.
Where you can use it
You build and explore insights in the PostHog web app. The other surfaces let you query the same data, act on it, and pull it into your own tools.
PostHog Web
Build insights, assemble dashboards, share them, and set alerts on the numbers that matter.
Build insights →PostHog MCP
Run trends, funnels, retention, and SQL queries from any MCP client or AI editor.
Run queries →PostHog Desktop Beta
Read the Self-driving reports about metric regressions and decide what to do about them.
Review regressions →Where its data comes from
Product analytics runs on the events your product sends to PostHog and the persons and properties attached to them. Get those right and every insight downstream gets easier.
Events
Custom events you capture from any SDK, each with the properties you attach to them.
Capture events →Autocapture
Pageviews, clicks, and form submissions captured for you, so you have data before you instrument.
Set up autocapture →Persons and identity
Link anonymous and logged-in activity to one person so funnels and retention count real users.
Identify users →Person and group properties
Plan, role, company, and anything else you set – the dimensions you break every insight down by.
Set person properties →How product analytics works with Self-driving
Your product analytics data is a signal source for Self-driving. A scout watches your saved funnel, retention, lifecycle, stickiness, and paths insights for rate regressions – a conversion step that started dropping, retention that flattened – and turns what it finds into a report.
The scout stops at the report. A conversion drop can be a bug, a pricing change, a bad release, or seasonality, so deciding what it means is a judgment call rather than a one-line code change – it doesn't open a pull request on its own. You build and explore insights in the PostHog web app and read the reports in the Self-driving inbox. See the Self-driving docs for the full picture.