Module
Module 5 of 6Lesson 3 of 3~14 min

Choose your product analytics tool

Choosing an analytics tool from a demo or a trend gets expensive when you have to switch later. This lesson compares tool families and several common options, including PostHog, Amplitude, Mixpanel and GA4, using a grid of criteria. The PM can then defend a reasoned recommendation to engineering, data and procurement.

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Lesson objective

By the end of this lesson, you will be able to compare the main families of tools (product analytics, web audience measurement, warehouse-native analytics) and four common tools (PostHog, Amplitude, Mixpanel, GA4) with a criteria grid, and recommend a justified choice for a given product.

Topics covered

  • product analytics tool
  • PostHog
  • Amplitude
  • Mixpanel
  • GA4
  • warehouse-native analytics

Where it fits

Analyse usage

Where do users drop off, who comes back, and which tool shows it?

Lessons in this module

  1. Build and read a funnel
  2. Cohorts and retention, knowing whether customers stay
  3. Choose your product analytics tool (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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