Using the Repeat Purchase Rate metric with customer cohorts can be a useful lens to predicting future customer orders.
When you’re evaluating cohorts of customers, it’s important to include their Repeat Purchase Rate (RPR) like Repeat Customer Insights does.
Cohort behavior typically looks more at how much they’ve spent, how many orders they’ve placed, and where they came from. All is useful to know, but they are all measurements of behavior in the past. They are all also heavily biased toward the first orders.
The metrics don’t really help understand what the customers are going to do in the future.
Know their Repeat Purchase Rate though and you can start to predict how those customers will behave in the future. RPR uses historic data but it can be used to look forward.
One prediction method compares the cohort performance to your overall performance.
Lets say this cohort has a 25% RPR, meaning only 1-in-4 has bought a second time. If your store’s RPR is 35%, then it’s very likely another 10% of that cohort’s one-time customers will be placing another order. How soon? That’s a question customer purchase latency can help answer.
(It’s fun how a lot of customer behavior metrics tie together)
This works really well with new cohorts, the ones that started only a few months ago. They haven’t had enough time to record their full customer’s lifetime of orders yet. By comparing them to your store’s overall performance, which has more completed lifetimes, you can get a glimpse of their future over the coming months.
Are those cohorts smaller than normal? You can assume the next few months will have lower-than-usual sales from repeat customers (perfect time to start building up new customer acquisition stuff).
As with any future projections, expect reality to be off a bit. Perhaps the cohort was attracted through a channel that has better-than-average customers (or worse-than-average). Perhaps a new marketing campaign is a flop (or a blockbuster).