Cohorts, Revisited

This week, we released our new loyalty analytics dashboards. And, as I’ve talked about here before, my favorite part of it is the Retention Overview section. 

The reason it’s so exciting to me is that one of the challenges brands face when changing retention strategies is measuring them: Retention takes time, and a change today may not result in a business change for months. 

Our cohort charts begin to show that.

But the inclusion of the section in our dashboard is exciting for another reason: It shows where Stamped is going.

Our view is that the biggest impact from loyalty—and retention strategies at large—starts with better merchandising in channel (like email and SMS) and then extending those experiences onsite. When you implement strategies, especially holistically, it isn’t as easy to measure as, say, a conversion rate optimization test.

To illustrate, take a look at this cohort graph for a Stamped customer in the beauty space:

In this chart, we’re showing how a brand’s newly acquired customers from November compare to customers who were acquired in previous Novembers (2023, 2022, and 2021). A year-over-year comparison of the same month like this helps understand longer term trends in retention behavior while controlling for business seasonality (especially a time like now).

What you immediately see here is that the value of a customer is not expanding at the same rate it previously did. In fact, even as the business has gotten better at increasing first purchase AOV, the long-term value of the customer has declined. This is something you don’t see when looking at monthly AOV or even your overall repeat revenue as a percentage of your business. It’s a business truth that is hidden from view unless you begin to look at your customers in this fashion. 

What you don’t see is why. 

To begin figuring that out, you can decompose lifetime value, which outside of first-purchase revenue, is made up of three metrics: 

• Repurchase rate

• Purchase frequency (or number of purchases per customer in a given time period)

• Returning purchase AOV 

In this case, we’ll look at purchase frequency and returning purchase AOV.

What you see very quickly here is that, while they’ve improved AOV (both on first purchase and on returning purchases), this brand is not getting customers back at the same rate as before.

And this is the point of a cohort graph: Retention is a tricky thing, because if you improve one metric immediately, it can take time for knock-on effects to take hold. And that’s what it appears is happening here. 

As you begin to find these answers, you’ll end up with more questions, like whether subscription revenue is rising or falling, how pricing (or other merchandising levers like bundling) has changed, and whether discounting tactics are different.

As brands make these sorts of changes, they need a way to measure their impact over time. And that’s exactly why we’re including this type of graph in our analytics now.

Stamped is focused on becoming a hub for activating holistic retention and lifecycle strategies and this type of reporting  will make understanding impact easier.

We can’t wait to see how you improve your cohort curves with us.