Repeat ORDERS Rate vs. Customer Repurchase Rate vs. Customer Returning Rate

Returning Customers rate looks at the amount of customers that return each month, so by the 12th month since original purchase, a % of the customers came back from that cohort to buy again. Customers Returning Rate is looking at that amount each month that comes back.


Customers Repurchase Rate looks at how many customers from the entire cohort - cumulatively have come back at by that time. So in a particular cohort and a month, x% of the cohort has come back at least once to come back. Each customer is counted once. This is the metric where you can make summarizing statements, like by 6 months, x% of our cohorts have come back and repurchased at least 2 additional times, or 5 additional times.

While returning rate — a customer can be unique each month if they come back each month and be included.


Repeat Orders Rate per Cohort looks at Orders! By the a certain month, there have been XX REPEAT Orders, so the xx% is comparing the order amount to the number of customers. If they got to 100%, that means that there is an order equivalent to the amount of customers. If it got to 200% it means that there are 2 orders equivalent to the amount of customers. It is showing you how some customers are more loyal to the brand and order more than the rest of the cohort. It is counting the same customer multiple times because it is looking at orders.

Repeat orders looks at total number of Orders. So that means, if the same customer came back 10 times, those 10 times would be counted each time because we are counting orders.

Repeat Orders Rate tells you the # of orders that are repeat orders and dividing that by customers. It looks at orders compared to customers. 200% means that all the customers from that cohort ordered two repeat orders - a loyal customer base!

Formula = number of returning orders / size of the cohort
Indicates the weighted likelihood of customers coming back by a certain month
100% = doubled the number of orders, 200% = tripled, etc.

We can segment by products or codes or tags to see if anything jumps out.