What problem does it solve?
Teams often hard-code a guessed "aha moment" event as their activation metric without evidence it predicts retention. This Skill replaces that guess with a validated activation definition — a combination of early actions and count thresholds chosen by balancing reach against retention lift — and models it as a durable per-user activated flag plus an activation-rate metric.
Core Features & Use Cases
- Candidate discovery and validation: List early actions from the event taxonomy, then measure retention lift for each candidate, combination, and count threshold before committing to a definition.
- PostHog HogQL recipes: SQL views that flag per-user activation within N days of the first-ever event and roll up activation rate by signup cohort, with guards against false signup dates and right-censored retention windows.
- dbt modeling: A
dim_activation_criteria table holding the definition as versioned data plus fct_user_activation with per-user flags, activated_at timestamps, and schema tests.
- Use Case: A B2B SaaS team suspects "created a project" is their activation event. The Skill validates it against week-4 retention lift, finds "ran ≥3 queries within 7 days" predicts retention better, and models that definition as an account-level activated flag.
Quick Start
Ask the assistant to find and validate which early user actions predict retention, then build an activation-rate model for your PostHog warehouse or dbt project.
