Seeing More of the Picture: Stacked Activity Chart Now Includes Earlier Cohort Layer

Everhort’s interactive Stacked Activity chart is a great way to see how cohorts acquired within a given time period stack up and contribute to overall revenue during that period. It’s easy to see at a glance whether you’re getting significant contributions from multiple cohorts, or if you’re relying too much on new customers.

One piece of the picture had been missing from this chart, though. It wasn’t easy to see how the contributions from cohorts acquired during the chosen time period compared with those acquired before that time period.

With the introduction of a new layer in the chart, we’ve filled in this missing piece:

This new layer, which can be toggled on or off, represents the rolled up contributions of all cohorts acquired before the start of the selected period. As with the other layers, you can view these contributions in terms of revenue or number of returning customers, and you can click to isolate this group:

Good businesses know it’s important to get contributions from all customers, and we think this improvement to our stacked cohort activity chart makes it easier to see the whole picture.

Introducing baseline LTV and new cohort filtering options

We’ve recently added some new tools that work really well together to help you better understand customer behavior. These tools are available in your account now if you’ve connected your store using our automatic Shopify data import tool.

New Filtering Options

We’ve added two new ways for you to isolate different segments of customers for analysis in Everhort.

Customer Tags

When you click to add a filter, you’ll now see a dialog like the one below, with tabs to select filters at the customer level or the order level.

On the customer tab, you can now select to filter by customer tag. Tags will be populated automatically from your store if you are using Everhort’s Shopify import connection.

Order Properties

If you click over to the “Order” tab, you can filter customers based on certain criteria about their orders. On this tab, we’ve added the ability to filter by product property:

Like customer tags, product properties will be imported automatically from your Shopify store.

You can use several different types of matchers when filtering by product property, including Equals, Does Not Equal, Is Set, and Is Not Set. You can learn more about how these matchers work can in our support center.

New Baseline Average LTV

After applying a filter, Everhort will add a new green line to the LTV by Cohort chart showing how the “baseline,” or unfiltered average LTV compares to the performance of your filtered customer group.

For example, let’s say we’ve filtered by customers who are tagged “Subscriber:”

The new “12 mo. average (baseline)” curve helps us see that all monthly cohorts in the “Subscribers” segment of customers, especially recent cohorts, have superior LTV curves. After 11 months, customers in this segment have an average LTV of $2,000, compared to an average LTV of only $1,226 among the customer base as a whole.

We believe the new filters and baseline average tool work well together to help you understand how different segments of customers perform relative to each other. We will be adding more types of filters and matchers in the future to allow you take this even further.