Cohort Analysis is a way to analyze customers in groups. Instead of analyzing their behavior, cohorts are formed based what characteristics they have in common.
In ecommerce the typical cohort analysis uses the customer's first ordering date. For example, if a customer first orders in December 2022, then they'll belong to the
2022-12 cohort along with all other customers who first ordered in that month.
Cohort analysis assumes that many customers who ordered at the same time will have similar behavior. This is a solid assumption as you might be running specific promotions, have specific messaging, or the ordering date is during a seasonal or cyclical time.
Once you have customers grouped into a cohort, you can analyze them for any number of metrics. Remember, a cohort is a group of customers so any advice you find on metrics or customer segmenting can also be applied to cohorts. (In fact, it's useful to compare each cohort to your other customer segments as a way to benchmark your data).
Common metrics used to measure and gauge a cohort include:
A strong cohort analysis can do a lot to help you understand how your customers behave and how that behavior changes over time. The further back in your store's history you do a cohort analysis, the more valuable it can be. Limiting cohorts to only the last year or last two years can hide a lot of powerful insight.
It can be difficult to build and calculate cohorts so if you need help, Repeat Customer Insights can create them for you using your Shopify data.
The following are various topics on Cohort Analysis and working with customer cohort data.
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