Cohort analysis is an analytical method that groups users, customers, or entities based on a shared characteristic and tracks their behavior over time. Instead of analyzing all users together, cohort analysis helps businesses understand how different groups behave across specific periods.

The most common cohort is a time-based cohort, such as:

• Users who signed up in January

• Customers who made their first purchase in Q2

• Accounts onboarded after a product launch

Other cohort types include:

• Behavior-based cohorts (users who completed onboarding)

• Acquisition-based cohorts (users from a specific channel)

• Product-based cohorts (users of a specific feature)

Cohort analysis is widely used to measure:

• Retention and churn

• Customer lifetime value trends

• Feature adoption

• Product stickiness

• Marketing effectiveness

A classic example is retention analysis. Instead of asking “How many users are active this month?”, cohort analysis asks “Of users who signed up last month, how many returned this month?” This reveals whether the product is improving over time.

From a BI perspective, cohort analysis requires carefully structured data. Events must be timestamped, user identifiers must be consistent, and cohort definitions must be stable. Most cohort tables are built using SQL, window functions, and time-based aggregations.

Visualization matters. Cohort data is often shown using heatmaps, where rows represent cohorts and columns represent time periods. This makes trends and drop-offs easy to spot.

One common mistake is mixing cohorts or redefining them inconsistently. This leads to misleading insights. Strong governance and clear definitions are essential.

Cohort analysis shifts analytics from static snapshots to behavioral patterns over time, making it a core technique for product, marketing, and growth teams.