Cohort analysis is a method used to track and analyze the behavior of groups of users who share common characteristics over specific time periods, providing deeper insights into customer behavior and lifecycle. Unlike general customer segmentation, which groups users by shared traits regardless of time, cohort analysis focuses on temporal patterns, revealing insights such as when customers are most active or likely to churn. Businesses benefit from this analysis by crafting personalized marketing strategies, reducing churn, and enhancing fundraising efforts through more nuanced data stories. There are various types of cohort analysis, including time-based, behavior-based, size-based, and event-based cohorts, each offering unique insights into user behavior. The process can be executed using tools like SQL for structuring data, Python libraries such as Pandas and Matplotlib for in-depth analysis and visualization, and R packages for streamlined cohort creation and visualization. Additionally, platforms like Hex enhance cohort analysis by integrating SQL, Python, and visualization capabilities in a collaborative environment, simplifying the process and making it accessible even to non-technical team members through AI-driven tools.