Cohort Analysis definition: Cohort Analysis is a type of behavioral analytics that groups related subsets and measures them over time. In ecommerce, cohort analysis measures groups of customers and the value they bring to your business over their entire lifecycle with your brand. Generally, customer cohorts are grouped based on acquisition period, most commonly by months. But they can also be grouped by other commonalities like number of orders, discount codes used, customer tags, or other unique qualities. In Peel, cohorts are automatically grouped by their acquisition month. So if a customer first made their purchase with your brand in January 2023, they will remain in that January 2023 cohort forever.
Cohort Analysis Example
There are many different forms of cohort analysis and cohort metrics in Peel. In a general sense, you can think of your most important cohort analyses bucketed into 3 main categories:
- Revenue Cohorts - As the name suggests, Cohorts Revenue metrics follow how your customers are spending with you and how much monetary value they are bringing to your business over time. This includes key revenue metrics like customer lifetime value (LTV), average order value (AOV), Net Sales, Gross Margin, and more.
- Retention Cohorts - With the Cohorts Retention metrics, you get a view of how your acquisition groups are coming back to repurchase over time. It’s all about understanding when your customers are coming back and how often they are making purchases. Retention metrics are some of the most important for ecommerce growth as companies want to aim for 2+ purchases from all their customers. This includes key retention metrics like Days Since First Order, Number of Orders per Customer, Repurchase Rate, and more.
- Subscription Cohorts - Subscription Cohorts combines the best of both worlds from Cohorts Revenue and Retention metrics, but analyzes your subscription business. This allows you to zoom into your subscriber behavior to help you improve that side of your business if you offer subscriptions.