Read retention curves and adoption trends by cohort and decide what is real signal vs noise. Use when analyzing retention by cohort, studying feature adoption over time, investigating churn, or comparing cohorts — to reach a defensible PM judgement, not just a chart.
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What the file says
> **Provenance.** Vendored from `phuryn/pm-skills@cohort-analysis` (MIT, Paweł Huryn / Product Compass — github.com/phuryn/pm-skills). Adapted into great-pm 2026-05-29 with attribution; reframed 2026-06-17 from a data-engineering recipe into a PM interpretation playbook. Host agent: analytics-analyst.
# Cohort Analysis — interpretation playbook
A cohort is a group of users bucketed by a shared starting event (signup
month, first-purchase week, feature-launch exposure) and then tracked over
the same elapsed time. The chart is the easy part. **The skill is the
judgement: deciding what is a real difference between cohorts, what is
noise, and what is an artefact of how you drew the cohort.** This skill is a
decision playbook, not a pandas recipe — you produce an insight and a
recommendation, not a script (generate code only if the human explicitly
asks).
## 1. Define the cohort — and freeze the definition
Before any number, pin down three things and write them down:
- **Grouping event** — what makes someone a member? (signup week, first
paid transaction, first exposure to feature X). One event, stated once.
…
The line at the top of this page pins the label git:20260721.86de099, which is what most people want. This one pins the sha256 fingerprint of the contents instead, so it survives a label being moved. Either way the exact version is written to mdr.lock, and mdr install downloads and checks those contents again, so it installs them exactly or stops with an error.
GET https://markdownregistry.com/api/v1/artifacts/art_ewkihf2vz2noo44n
GET https://markdownregistry.com/api/v1/resolve?ref=vandanaajaydubey111/great-pm/cohort-analysis
GET https://markdownregistry.com/api/v1/blob/1c0b3e1ee428dbd19aa944532fb5a3547be0df43bea87998f32c651bf75a3dbb
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