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