cohort-analysis

Analyzing user retention trends requires uploading cohort data files containing cohort identifiers, time periods, and engagement metrics. The analysis identifies drop-off points and trend shifts across time, generating retention curves and actionable insights for product teams.

Cohort analysis is a technique that quantifies retention patterns and feature adoption differences across user groups over time. By comparing engagement metrics across cohorts, it reveals long-term engagement signals and generates visualizations for data-driven product decisions.

Yes, you can visualize retention curves and adoption metrics from a CSV file. The analysis reads CSV, Excel, or JSON formats containing cohort identifiers and engagement data, automatically generating adoption visuals and retention curves for cohort comparison.

Generating reproducible Python scripts for cohort retention analysis involves processing your data through the analysis workflow. The system delivers optional Python scripts alongside retention curves and insights, ensuring your cohort comparison and anomaly detection results can be replicated.

To perform cohort retention analysis, you need data structured with cohort identifiers, time periods, and engagement metrics. Supported file formats include CSV, Excel, and JSON, which are read to identify retention patterns, detect anomalies, and generate trend insights.

Yes, cohort analysis works for detecting anomalies in long-term engagement signals. The analysis applies anomaly detection to cohort data, identifying unusual retention patterns and trend shifts across time periods to reveal significant deviations from expected engagement behavior.