cohort-analysis

# Cohort Analysis & Retention Explorer ## Purpose Analyze 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. ## How It Works ### Step 1: Read and Validate Your Data - Accept CSV, Excel, or JSON data files with user cohort information - Verify data structure: cohort identifier, time periods, engagement metrics - Check for missing values and data quality issues - Summarize key statistics (cohort sizes, date ranges, metrics available) ### Step 2: Generate Quantitative Analysis - Calculate cohort retention rates and engagement trends - Identify retention curves, drop-off patterns, and anomalies - Compute feature adoption rates across cohorts - Calculate month-over-month or period-over-period changes - Generate Python analysis scripts using pandas and numpy if requested ### Step 3: Create Visualizations - Generate retention heatmaps (cohorts vs. time periods) - Create line charts showing cohort progression - Build comparison charts for feature adoption - Visualize drop-off points and engagement trends - Output as interactive charts or static images ### Step 4: Identify Insights & Patterns - Spot one or more significant patterns: - Early churn in specific cohorts - Late-stage engagement changes - Feature adoption clusters - Seasonal or temporal trends - Highlight surprising findings and deviations - Compare cohort perf...