Module
Module 3 of 6Lesson 3 of 3~16 min

Write the tracking plan and the acceptance criteria

A written tracking plan becomes the shared contract of the product, engineering and data teams, and prevents orphan events. This lesson covers what it must contain to be implementable and kept up to date, and how to add testable acceptance criteria to tickets. The PM ends up with data they can actually rely on.

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

Lesson objective

By the end of this lesson, you will be able to write a complete tracking plan (one row per event with the question served, trigger, properties, source, identity, owner and status) and write acceptance criteria in tickets that developers and testers can check.

Topics covered

  • tracking plan
  • acceptance criteria
  • tracking specification
  • event governance

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
  3. Write the tracking plan and the acceptance criteria (this lesson)

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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