Use a concrete example: "Overall retention looked stable at 40%, but when we split by signup month, we saw newer cohorts retaining at only 25%."
Strong answers explain grouping users by a shared characteristic (signup date, first action, acquisition channel) and tracking their behaviour over time. Best examples show how aggregate metrics looked fine but cohort analysis revealed declining retention in newer cohorts, or that a specific channel produced lower-quality users.
Tests analytical depth beyond surface metrics. Candidates who only work with aggregate numbers miss critical trends. Cohort analysis is fundamental to understanding product health.