cohort analysis (cohort table)

Cohort analysis groups users by when they started (sign-up week, first purchase month) and follows each group separately over time. Its standard view is the cohort table: one row per cohort, one column per period since joining (week 0, week 1, week 2…), and in each cell the share of the cohort that was active in that period, or the revenue it brought. The table is triangular, because newer cohorts haven't lived through as many periods yet.

It lets you read three directions:

• Along a row: how one cohort decays over time and whether its curve flattens.
• Down a column: whether newer cohorts retain better than older ones at the same age. This is how you see whether product changes are working.
• Along a diagonal: cells that fall in the same calendar week. If a whole diagonal dips, something happened on that date (an outage, a holiday) rather than in the product's long-term quality.

Averaging a column needs care. Weight each cohort by its size, and include only cohorts that have reached that period:

$\bar r_k = \frac{\sum_c \text{active}_{c,k}}{\sum_c \text{size}_c} \quad \text{over cohorts } c \text{ old enough to have period } k$

A simple mean of percentages lets a small cohort count as much as a large one. Dividing by every cohort's size, including ones too young to have the period, drags the average down (this is right-censoring). Beyond the average, cohort tables are the basis for comparing segments (channel, platform, group type), spotting a retention plateau and forecasting active users.