Land a PM Job
Playbook

The Product Analytics Playbook

How PMs actually use product analytics — instrumenting, monitoring, diagnosing regressions, and running experiments.

Instrumentation Checklist

Events every product should have

  • user_created + user_activated
  • session_started + session_ended
  • key_action_completed (per your core loop)
  • purchase_started + purchase_completed
  • error_seen + error_recovered

Dashboards Every PM Should Own

Core set

  • Top-line KPI: your team's primary metric
  • Funnel: acquisition → activation → retention → revenue
  • Cohort retention: D1/D7/D30 by signup week
  • Segmentation: usage by user tier / geography / device

Diagnosing Regressions

5-step method

  • 1. Confirm the drop is real (not measurement bug)
  • 2. Segment by geo/device/cohort — is drop everywhere or specific?
  • 3. Check for internal changes (releases, config, experiments)
  • 4. Check for external factors (holidays, competitor, PR)
  • 5. Form hypothesis + run isolated test

Experimentation Basics

Rules to live by

  • Compute sample size before launching (not after)
  • Set primary metric and stop-loss metrics
  • Run for at least one full weekly cycle
  • Beware novelty effects — measure after 2 weeks too
  • Always analyze by segment, not just aggregate

Master These with a Coach

The cohort drills every framework and template with live feedback from FAANG PMs.