Module
Module 3 of 6Lesson 2 of 3~14 min

Identity, accounts and event sources

When user identity is handled badly, journeys split in two and B2B accounts become impossible to count. This lesson prepares a PM to specify, with engineers, how anonymous visitors, signed-in people and customer companies are linked together. It also helps decide which events should be sent from the server rather than from the interface.

Take this lesson: your first course is freeSee the full syllabus

Lesson objective

By the end of this lesson, you will be able to specify when to identify a user, how to link their anonymous and logged-in events, how to attach events to a client account in B2B, and which events to send from the server rather than from the browser or the app.

Topics covered

  • identify call
  • anonymous ID
  • group analytics
  • server-side tracking
  • B2B analytics

Where it fits

Design the tracking plan

Which events should I collect, how should I name them and who should they be attached to, so that the data answers my questions?

Lessons in this module

  1. Events, properties and naming convention
  2. Identity, accounts and event sources (this lesson)
  3. Write the tracking plan and the acceptance criteria

What you will learn in the course

This lesson is part of the course Instrument a product and use data to make decisions

  • Build a product's metrics tree (North Star, input metrics, guardrails) from the decisions to be made, and rule out vanity metrics.
  • Design an implementable tracking plan (events, properties, naming convention, identity and accounts, emitting source, owners).
  • QA and monitor event collection to get reliable data, and know what to do when it is not.
  • Collect usage data in line with the GDPR and the CNIL rules (consent, audience measurement exemption, minimisation, retention period).
  • Analyse a conversion funnel, activation and cohort retention, and interpret the resulting curves correctly.
  • Choose a product analytics tool (PostHog, Amplitude, Mixpanel, GA4, data warehouse) based on needs, constraints and cost.
  • Turn an analysis into a reasoned decision (dashboard, decision memo) while avoiding interpretation traps (vanity, Simpson, correlation, small counts).

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