Cohort analysis

Skill vermapragya/analytics-skill/skills/cohort-analysis

Skills library for product data scientists working with Claude

Install

npx -y skills add vermapragya/analytics-skill --skill cohort-analysis

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Builds cohort retention tables and retention curves with a single consistent denominator policy. Use when the user mentions cohort, retention, retention curve, "by signup month", "by acquisition channel", N-day retention, churn over time, or lifecycle analysis.

SKILL.md

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Cohort Analysis

When to use this skill

Use when the user wants to understand how behavior changes over the lifecycle of users, segmented by when they entered. Triggers:

"Show retention by signup cohort…"
"Build a cohort table…"
"N-day retention for…"
"Compare cohorts by acquisition channel"
"Is retention improving?"

If the user wants step-by-step conversion within a single session/flow, use funnel-analysis instead.

Required inputs

Input	Why it matters
Cohort anchor event	Defines when a user enters the cohort (signup, first purchase, install)
Cohort grain	Day, week, or month
Retention event	What action defines "retained" (any session, key feature use, purchase)
Time window per period	Daily / weekly / monthly columns
Lookback window	How many periods to display

Workflow

Confirm the anchor event. A user belongs to exactly one cohort. If they could be re-anchored (e.g., resubscribe), pre-specify the rule (first only, latest, both as separate cohorts).
Pick the cohort grain.
- Daily: short time-scale features, fast-moving products
- Weekly: most product analytics defaults — smooths weekday/weekend variance
- Monthly: long sales/subscription cycles
Define "retained" precisely. Most common definitions:
- Any-activity: at least one logged event in period
- Key-action: specific event (e.g., completed_order)
- N-day: active on day N exactly (rolling vs absolute matters)
- N-day-bracket: active anywhere in days [N, N+window)
Pick one. Document it in the readout.
Pick the denominator policy. This is the most common source of misleading cohort charts:
- Cohort-fixed: divide by original cohort size every period. Curves only go down.
- Active-base: divide by users active in previous period. Shows period-over-period stickiness, but can hide leaky retention.
- Eligible-base: divide by users who had the opportunity to retain (e.g., still on the platform). Use when accounts can churn permanently.
Default to cohort-fixed. Only switch if there's a specific question about period-over-period stickiness.
Generate cohort matrix using scripts/cohort_table.sql (Snowflake) or scripts/cohort_table.py (pandas).
Compute key summary stats:
- D1, D7, D30 retention by cohort
- "Smile curve" check (does retention stabilize after some N?)
- Cohort-over-cohort delta (is the latest cohort retaining better than 4 cohorts ago?)
Write the readout with the matrix, summary stats, and interpretation.

Output format

# Cohort Analysis: <product/segment>

## Definitions
- Cohort anchor: <event> on <grain>
- Retention event: <event>
- Denominator: cohort-fixed (original cohort size)
- Lookback: <N> periods

## Summary
- Latest cohort D7 retention: <X%> (vs <Y%> 4 weeks ago, <delta>pp)
- D30 retention asymptote: <Z%>
- Retention "stabilizes" around day <N>

## Cohort table
| Cohort | Size | D1 | D7 | D14 | D30 | D60 | D90 |
|---|---|---|---|---|---|---|---|
| 2026-01 | 12,400 | 48.2% | 22.1% | 16.4% | 12.0% | 9.8% | 8.5% |
| 2026-02 | 14,100 | 51.0% | 24.8% | 18.2% | 13.5% | 10.4% | — |
| ...    | ... | ... | ... | ... | ... | ... | ... |

## Interpretation
- <Are recent cohorts improving, flat, or declining?>
- <What's the asymptote? Is the long-term retained base stable?>
- <Any anomalies — single bad cohort, missing data?>

## Caveats
- Recent cohorts have incomplete data through the latest periods (mark provisional)
- Cohort assignment uses <rule>; users who re-anchored are <treated as>
- <late-arriving events caveat if applicable>

## Next steps
- <e.g., investigate the drop in 2026-03 cohort>
- <e.g., segment by acquisition channel to find a driver>

Validation checks

Each user appears in exactly one cohort
Denominator policy stated and consistent across all cells
Recent incomplete cohorts flagged as provisional
Period boundaries aligned (Mon-Sun or 1st-end-of-month, not mid-week)
Late-arriving events caveat if data has > 24h pipeline lag

Edge cases & failure modes

Mid-week cohort start: if your cohort grain is "week" but anchor events occur all days, define week as Mon-Sun and align. Don't let cohort #1 be 3 days long.
Re-engaging users: if a user goes dormant and comes back, are they in their original cohort or a new "reactivation" cohort? Pre-specify.
Sparse recent cohorts: the last 1-2 periods of the matrix have <100% observation. Mark cells as "—" (no data) rather than 0% (no retention).
Cohort size differences: if cohort sizes vary 10x, displaying raw percentages can mislead. Add absolute counts or use rolling averages.
Survivorship in subscription products: if users can't churn within a billing cycle, "retention" before cycle end is meaningless. Use eligible-base denominator.

Scripts

scripts/cohort_table.sql — Snowflake template producing a cohort matrix
scripts/cohort_table.py — pandas implementation for in-memory data

python scripts/cohort_table.py \
    --input events.csv \
    --user-col user_id \
    --event-time-col event_at \
    --cohort-grain week \
    --periods 12

Related skills

funnel-analysis — for within-session/within-flow conversion
survival-analysis — when you need time-to-churn distribution with censoring
metric-definition — write a clean retention metric spec
stakeholder-readout — for packaging cohort findings