What is cohort analysis used for?

It groups users into cohorts (e.g., by signup date or channel) and tracks retention and behavior over time to reveal lifecycle patterns and churn risks.

What inputs do I need?

A dataset with at least a signup/date column and an event/activation column; you specify these when running the scripts.

What outputs does this produce?

Retention tables by cohort, visualizations (cohort heatmaps, retention charts), and exportable reports to support decision making.

cohort-analysis

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npx machina-cli add skill guia-matthieu/clawfu-skills/cohort-analysis --openclaw

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SKILL.md

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Cohort Analysis

Analyze retention and behavior patterns by grouping users into cohorts - understand how different customer groups behave over time.

When to Use This Skill

Retention tracking - Measure how users stick around over time
Acquisition analysis - Compare cohorts from different channels
Product changes - Measure impact on user behavior
Churn prediction - Identify at-risk cohorts
LTV estimation - Project customer lifetime value

What Claude Does vs What You Decide

Claude Does	You Decide
Structures analysis frameworks	Metric definitions
Identifies patterns in data	Business interpretation
Creates visualization templates	Dashboard design
Suggests optimization areas	Action priorities
Calculates statistical measures	Decision thresholds

Dependencies

pip install pandas plotly click

Commands

Retention Analysis

python scripts/main.py retention data.csv --date-col signup --event-col purchase
python scripts/main.py retention data.csv --date-col signup --periods week

Visualize Cohorts

python scripts/main.py visualize cohorts.csv --output retention_chart.html

Export Report

python scripts/main.py report data.csv --date-col signup --event-col active --output report.html

Examples

Example 1: Analyze User Retention

python scripts/main.py retention users.csv --date-col signup_date --event-col last_active

# Output:
# Cohort Retention Analysis
# ──────────────────────────────────
# Cohort     Users    M1     M2     M3     M4
# Jan 2024   1,234    65%    48%    42%    38%
# Feb 2024   1,456    62%    45%    41%    --
# Mar 2024   1,321    68%    52%    --     --
# Apr 2024   1,567    64%    --     --     --
#
# Avg Retention: 65% → 48% → 42% → 38%
# Best Cohort: Mar 2024 (68% M1)

Example 2: Generate Visual Report

python scripts/main.py report transactions.csv \
  --date-col signup \
  --event-col purchase_date \
  --output retention_report.html

# Generates interactive HTML with:
# - Retention heatmap
# - Cohort size chart
# - Trend analysis

Cohort Table Format

Cohort	Size	Period 0	Period 1	Period 2	Period 3
2024-01	1234	100%	65%	48%	42%
2024-02	1456	100%	62%	45%	-
2024-03	1321	100%	68%	-	-

Skill Boundaries

What This Skill Does Well

Structuring data analysis
Identifying patterns and trends
Creating visualization frameworks
Calculating statistical measures

What This Skill Cannot Do

Access your actual data
Replace statistical expertise
Make business decisions
Guarantee prediction accuracy

Related Skills

ab-test-stats - Test retention experiments
funnel-analyzer - Analyze conversion funnels

Skill Metadata

Mode: centaur

category: analytics
subcategory: retention
dependencies: [pandas, plotly]
difficulty: intermediate
time_saved: 4+ hours/week

Source

git clone https://github.com/guia-matthieu/clawfu-skills/blob/main/skills/analytics/cohort-analysis/SKILL.mdView on GitHub

Overview

Cohort analysis groups users by shared attributes (like signup date or channel) to reveal how retention and behavior evolve over time. This helps you measure retention, understand lifecycle patterns, compare cohorts across acquisitions, and flag churn risks for proactive optimization.

How This Skill Works

Define cohorts (e.g., by signup_date) and compute period-based retention (Period 0, 1, 2, etc.) for each cohort using pandas, producing retention tables and visualizations. Use the provided CLI commands to run retention analysis, visualize cohorts, and export a report for sharing with stakeholders.

When to Use It

Retention tracking
Acquisition analysis
Product changes
Churn prediction
LTV estimation

Quick Start

Step 1: Retention analysis - python scripts/main.py retention data.csv --date-col signup --event-col purchase
Step 2: Visualize cohorts - python scripts/main.py visualize cohorts.csv --output retention_chart.html
Step 3: Export report - python scripts/main.py report data.csv --date-col signup --event-col active --output report.html

Best Practices

Define cohorts using meaningful attributes (e.g., signup date and channel) so comparisons are actionable
Use consistent time buckets (week or month) and a fixed baseline for all cohorts
Ensure inputs include required date and event columns and align column names
Clean data before analysis (deduplicate records, fix missing dates, normalize formats)
Compare multiple cohorts over the same horizon to identify drivers and trends

Example Use Cases

An e-commerce app tracks retention by signup month to identify onboarding or channel effects on long-term engagement
A SaaS product compares cohorts from organic vs paid channels to optimize acquisition spend
A product team measures the impact of a feature release on subsequent engagement across cohorts
Churn risk is detected by cohorts showing rapidly declining retention curves
LTV is estimated by projecting revenue from observed cohort retention patterns

Frequently Asked Questions

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