Use when the user wants to analyze retention, cohort behavior, engagement trends, or understand how different user groups perform over time. Triggers on: 'cohort analysis', 'retention analysis', 'user retention', 'cohort retention', 'week 1 retention', 'retention curve'.
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---
name: cohort-analysis
category: research-analysis
description: >
Use when the user wants to analyze retention, cohort behavior, engagement trends, or
understand how different user groups perform over time. Triggers on: 'cohort analysis',
'retention analysis', 'user retention', 'cohort retention', 'week 1 retention', 'retention
curve'.
codex-short-description: "Analyze retention, cohort behavior, and engagement trends over time"
allowed-tools:
- Read
- Grep
- Glob
- WebFetch
- WebSearch
related-skills:
- idea-generate
- clarity-council
loop-eligible: false
compatibility: claude-code codex opencode
---
# Cohort Analysis
You track how groups of users behave over time. The technique's whole value is separating real
change from composition change.
## Define the cohort and the event precisely
Cohort by acquisition date, first-purchase date, or plan — each answers a different question.
"Retained" must be a specific event in a specific window. Analyses that skip this produce
numbers nobody can reconcile against anything else.
## Equal observation windows or the trend is an artifact
A cohort from last month has not had time to reach month three. Comparing incomplete cohorts to
complete ones produces a decline that does not exist — the most common cohort-analysis error.
Truncate to the window every cohort has actually had.
## Look for the composition explanation first
A retention change usually reflects a change in who was acquired, not a change in the product. A
marketing push into a cheaper channel drags the cohort down while nothing about the product
moved. Segment by channel, plan, or geography before concluding anything about product changes.
## Separate the curve's shape from its level
Early drop-off, the plateau it settles to, and whether it plateaus at all are different findings
with different responses. A cohort that never flattens has no retained base regardless of its
month-one number.
## Absolute counts alongside rates
A retention rate improving while the cohort shrinks may be selection, not improvement. Show the
denominator.
## Reporting
State the cohort and event definitions, the observation window, the curve with counts, the
segment breakdown, and whether an observed change is composition or behavior.
> **Host portability:** tool names in this skill follow Claude Code conventions; on other hosts (Codex, opencode) map them by intent — see [PORTABILITY.md](../PORTABILITY.md).
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## Self-Evolve Loop
Journal `~/.ink-and-agency/learnings/cohort-analysis.md` (or workspace-local `.ink-and-agency/` where the sandbox confines writes). Read it first; append what the run taught — [SELF-EVOLVE.md](../SELF-EVOLVE.md).
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