Cohort Analysis
Cohort analysis is an analytical method in which users are segmented based on a shared characteristic, most commonly, the date they first interacted with the product, in order to evaluate their user journey individually. This approach avoids treating the audience as a homogeneous whole, dividing it into distinct groups ("cohorts") and monitoring the evolution of each segment over various time intervals.
Without this segmentation, aggregated data can hide critical trends. For example, an overall retention rate might appear stable even if recently acquired users drop off much faster than earlier ones, a problematic issue that total numbers can easily mask.
Evaluating groups clarifies precisely these aspects. By comparing distinct segments side by side, such as users acquired in January versus those from March, it becomes easy to see the extent to which an interface update, a new promotional campaign, or a pricing adjustment has influenced the behavior of newcomers relative to previous segments.
How is a cohort analysis actually used?
Typically, these groups are structured based on the initial signup date, after which the retention rate is analyzed, specifically, what proportion of each segment remains active after a one-week, one-month, or three-month interval. Data visualization is usually achieved through a matrix table, where each row corresponds to a group and the columns track the passage of time from the starting point. Beyond retention rates, the analysis can also track other relevant metrics, such as generated revenue, usage rates, or the adoption level of a new feature.
At the same time, it is essential to emphasize that the relevance of a cohort evaluation comes from comparing groups side by side, rather than looking at a single isolated value. A retention rate viewed in isolation provides a limited picture; only through comparison with previous or future segments can you clearly determine whether a trend is favorable or unfavorable.
