cohort-analysis

To calculate cohort retention rates from a CSV dataset, you need period-indexed cohort identifiers and user-level engagement metrics. This skill processes the data to compute retention curves, identify drop-offs, and generate a retention matrix for tracking user engagement over time.

A retention matrix visualizes cohort engagement by displaying retention rates across sequential periods. This skill generates heatmaps from your aggregated engagement metrics, allowing you to visually inspect retention curves and identify specific drop-off points or churn drivers within user segments.

Yes, you can analyze feature adoption across user cohorts because the skill outputs reproducible Python pandas scripts. It measures and compares feature usage curves across cohorts to surface early adopters or lagging groups using your provided engagement metrics.

Yes, cohort analysis works with Excel and JSON data formats, alongside CSV files. The skill requires period-indexed cohort identifiers and user-level engagement metrics within these files to successfully output retention matrices, visualizations, and reproducible Python scripts.

You identify churn drivers and anomalies in cohort engagement data by applying the skill's analysis to your dataset. It detects unexpected drop-offs, seasonal effects, and cohort-specific behaviors, translating these insights into actionable research and testing recommendations.