Remember segmentation analysis from our last session? Cohort analysis takes that grouping concept and adds time tracking to reveal how customer groups behave over their lifespans.

Instead of static snapshots, cohorts show how groups change and evolve, revealing patterns that traditional analysis completely misses.

Engagement Message

What might you discover by tracking the same customers over multiple months?

Here's what makes cohort analysis unique: you group customers by when they first engaged with your business, then track each group's behavior over time.

Think January signups, February signups, March signups - each becomes a cohort you follow for months or years.

Engagement Message

Why might customers who joined in January behave differently from March joiners?

Cohort analysis reveals the truth behind misleading averages. Your overall retention might be 60%, but January customers might retain at 80% while March customers retain at 40%.

This time-based insight helps you understand what drives long-term customer value.

Engagement Message

What is one business decision that could change based on cohort retention differences?

The most common cohort structure tracks customer behavior month by month after their first purchase. Month 0 is their first purchase, Month 1 is their second month, etc.

This creates a grid where each row represents a cohort and each column represents time since first purchase.

Engagement Message

What patterns might emerge when comparing early months versus later months?

Remember time series analysis? Cohort analysis adds depth by showing whether changes over time reflect customer maturation or external factors.

If retention drops in December, is it seasonal (affects all cohorts) or behavioral (affects only recent cohorts)?

Engagement Message