Module
Module 2 of 6Lesson 2 of 2~16 min

From decisions to metrics, and a definition of activation

Starting from the decision at hand, rather than from the data you happen to have, changes what your team measures and how it reads the results. This lesson helps you set aside flattering numbers that guide no choice and write a definition of activation that the data can confirm or refute. Useful before any onboarding trade-off.

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

Lesson objective

By the end of this lesson, you will be able to start from a decision to be made to write the questions and metrics that inform it, tell an actionable metric from a vanity metric, and propose a definition of activation that can be checked against the data.

Topics covered

  • vanity metrics
  • actionable metrics
  • activation definition
  • decision question
  • aha moment

Where it fits

Choose what to measure

Out of everything I could measure, what actually helps the team decide?

Lessons in this module

  1. North Star, input metrics and guardrails
  2. From decisions to metrics, and a definition of activation (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).

Related courses