cohort-analysis

1# Cohort Analysis & Retention Explorer
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3## Purpose
4Analyze user engagement and retention patterns by cohort to identify trends in user behavior, feature adoption, and long-term engagement. Combine quantitative insights with qualitative research recommendations.
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6## How It Works
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8### Step 1: Read and Validate Your Data
9- Accept CSV, Excel, or JSON data files with user cohort information
10- Verify data structure: cohort identifier, time periods, engagement metrics
11- Check for missing values and data quality issues
12- Summarize key statistics (cohort sizes, date ranges, metrics available)
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14### Step 2: Generate Quantitative Analysis
15- Calculate cohort retention rates and engagement trends
16- Identify retention curves, drop-off patterns, and anomalies
17- Compute feature adoption rates across cohorts
18- Calculate month-over-month or period-over-period changes
19- Generate Python analysis scripts using pandas and numpy if requested
20 
21### Step 3: Create Visualizations
22- Generate retention heatmaps (cohorts vs. time periods)
23- Create line charts showing cohort progression
24- Build comparison charts for feature adoption
25- Visualize drop-off points and engagement trends
26- Output as interactive charts or static images
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28### Step 4: Identify Insights & Patterns
29- Spot one or more significant patterns:
30  - Early churn in specific cohorts
31  - Late-stage engagement changes
32  - Feature adoption clusters
33  - Seasonal or temporal trends
34- Highlight surprising findings and deviations
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