cohort-analysis

The cohort-analysis skill is a robust tool designed for product managers and data analysts to dissect user behavior over time. It transforms raw engagement data into strategic insights by segmenting users into cohorts based on their entry points, such as signup month or feature launch date. This skill is essential for diagnosing why users drop off, identifying which cohorts demonstrate the highest long-term value, and evaluating the effectiveness of feature rollouts. By providing a structured approach to quantitative analysis, it bridges the gap between raw data and actionable product decisions.

• Calculate cohort retention rates and visualize complex data through retention heatmaps, line charts, and period-over-period comparison trends.

• Perform feature adoption analysis to determine how different cohorts engage with specific product updates or new releases.

• Utilize statistical methods to detect anomalies, drop-off points, and long-term engagement trends within the user base.

• Generate Python analysis scripts using pandas, numpy, and matplotlib for reproducible, ongoing reporting.

• Design targeted follow-up research recommendations, including qualitative interview frameworks, session replay reviews, and A/B test experiments.

• The skill accepts structured data formats including CSV, Excel, JSON, and raw SQL query outputs.

• Users should ensure data contains clear cohort identifiers, time periods, and relevant engagement metrics (e.g., sessions, feature usage, purchase frequency).

• For optimal pattern identification, providing a dataset spanning at least 3-4 distinct time periods is highly recommended.

• Outputs provide a comprehensive summary comprising data validation, key quantitative metrics, visual dashboards, pattern identification, and prioritized research steps.

• This tool is specifically optimized for PM workflows within Claude Code and Cowork environments but remains platform-agnostic for general analytical tasks.

• It helps replace ad-hoc spreadsheet calculations with a rigorous, framework-led process aligned with product management best practices for reducing churn.