miji.beCohort analysis groups users who share a common characteristic within a defined time period - such as first visit, sign-up, or first purchase - into a single cohort, then tracks that group's behavior over subsequent periods.
For example, you might define a cohort as "users who first visited in January" and then measure what percentage of that group returned in February, March, and April. This reveals retention trends that aggregate metrics can mask.
The key difference from standard analytics is the addition of a time-of-acquisition axis. Overall monthly active users might be growing, yet cohort analysis can reveal that retention among new users is declining - a "leaky bucket" scenario where growth depends entirely on ever-increasing acquisition.
Cohort analysis is highly effective for evaluating shortened URL campaigns. Define a cohort as "users who clicked the April campaign link" and track their subsequent site visits, purchase rate, and LTV (lifetime value). Comparing cohorts from different campaigns shows which one attracted the highest-quality users.
In Google Analytics 4, navigate to Explore then Cohort exploration. You configure the cohort inclusion criterion (first touch, meaning the user acquisition date, or the occurrence of a specific event or transaction), the metric shown in each cell (active users, for example), and the cohort granularity (daily, weekly, or monthly). Note that weekly cohorts run Sunday through Saturday rather than any rolling seven-day window, so a campaign that treats Monday as the start of the week ends up grouped one day out of step.