What’s new in Everhort: Slice Cohorts with First Purchase and Collection Filters

We’re excited to share announcements about two big updates we launched in the last month to make filtering cohorts more powerful in Everhort.

First Purchase Filters

Customers like using Everhort’s order filtering tools to analyze cohort performance based on certain criteria about the customer’s purchase, such as a specific product they purchased, or a property on the order.

Up until now, those filters would always match customers having any order that matched the filter. A new feature we launched last week now gives you more control over which of a customer’s orders have to match:

Expanding the new dropdown gives you the following options:

Selecting “Their first purchase” will restrict the filter to only those customers making the indicated purchase on their first order (if they made the purchase again later, that’s ok). Selecting “A subsequent purchase” will restrict the filter to only those customers making the indicated purchase on an order placed after their initial purchase (if they made the purchase on their first purchase also, that’s ok).

Collection Filters

Many eCommerce sites offer collections (or categories) to organize and present their products to customers.

We’ve recently added a new option to filter by collection when viewing reports in Everhort. This filtering option is available in your account if you’ve connected your store using our automatic Shopify data import tool.

To filter by collection, select “Collection” from the dropdown on the “Order” tab:

Like the other order-level filters, collection filters also work in conjunction with the “first purchase” filters described above, so that you could, for example, filter cohorts by customers who made their first purchase from a given collection:

Happy filtering!

More Flexible LTV Forecasts

Sometimes when you’re forecasting LTV, you want to see how projections based on recent performance compare to projections based on longer time periods.

Up until now, Everhort’s LTV forecasts were always based on a linear projection of the blended average performance of the last 12 monthly cohorts. There were reasons for this. If customer retention rate drops off after 6 months, you typically want to factor that into your forecast. But if average order value has been increasing recently, you want to account for that as well. A trailing one year period strikes a good balance between considering enough historical context to know how customers engage with the business over time, while still incorporating recent data, which is why it’s still the default time period for LTV forecasts in Everhort.

But sometimes your business changes significantly. Maybe you’ve been experimenting with new offers or eCommerce subscriptions. You’ve seen big changes and you’re curious how that could impact future LTV if recent trends continue. For this reason, we’ve recently enhanced Everhort’s LTV forecast tool to tailor its projections to the currently selected time period:

You can see from the example above that the one year forecast based off the trailing 6 months of cohort performance is $1,943, compared to $1,276 when using the last 12 months of cohort performance.

One last thing to note: When a shorter historical time period is selected, Everhort will also shorten the time period used in its forecast.

We believe having the flexibility to compare forecasts based on different historical time periods will help you get a better sense of what you can expect LTV to look like in the future.

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.