Average LTV by Cohort Chart
The Average LTV by Cohort chart is the most information dense report in Everhort. Once you learn how to read it, you will be able to quickly gain a lot of insight about how much value your customers produce over time.
First, we'll introduce how the chart is laid out, and then we'll walk through some example charts for fictitious companies to get a feel for how to read and interpret them.
Here's an example chart:
There are 3 things to know first to orient yourself with these charts:
- Each line represents a cohort of customers grouped by the month of their first purchase.
- The Y-Axis represents average cumulative lifetime value (LTV). This LTV value is based on the contribution margin of every purchase; that is, it accounts for cost of goods sold when available.
- The X-Axis represents the age of a cohort in months since they were acquired.
If we look at the box shown on the chart above, we can see that the April 2019 cohort had an average cumulative LTV of $1,103 per customer after 7 months, through October 2019.
There are some other things we can quickly understand about this company by looking at this chart:
- They make between $100-$300 on average on a customer's first purchase, with this first order value increasing steadily across the last 12 cohorts. We can tell this by looking at where all of the lines intersect the Y-axis on the far left side of the chart.
- More recent customers are returning more often and more quickly than older customers did. We can tell this by seeing how the lighter blue cohorts (newer cohorts) have steeper slopes than the darker blue cohorts (older cohorts).
- This company is very durable and has been for a while. All cohorts acquired in the past 12 months return at similar, robust rates and all of them still generate significant value for the company. When we look at the stacked area graph, we should expect to see profit increasing every month, even if customer acquisition rates are flat or decreasing, due to the ongoing contributions of old cohorts.
Here is an example of a different fictitious company:
What jumps out here is that the slope of the lines is much different for this company than for the first. After a few months of age, each cohort's cumulative LTV starts to flatten out. By the time a cohort is about 6 months old, its customers have almost completely stopped generating value for the company.
This company is a lot less durable than the first. It is more dependent on acquiring new customers to maintain its revenue stream, and it must acquire customers at a faster rate each month if it wants to grow profit. This company could look into how it might be able to increase engagement with customers after 6 months.
Using the Blended Average
Everhort will automatically calculate a blended average of recent monthly cohorts and plot this on the LTV chart as a light red line. In this example, you can see that the blended 1 YR LTV from all cohorts is $1,264. You can also quickly tell that newer cohorts (shorter, lighter lines) outperform the average, while older cohorts (longer, darker lines) underperform the average.
Comparing Filtered Segments to Baseline
If you apply a filter to your report, then a green line representing the blended unfiltered (baseline) average LTV will be added to the chart:
Cohorts matching the filter (and their average in light red) can then be compared against the baseline average.
All LTV values in the report are derived from the contribution margin when available.
If you have connected an online store using Everhort's Shopify data import, then Everhort will use the "Cost per item" field to calculate contribution margin if it is available.
If you have imported your data using Everhort's CSV file import functionality, then Everhort will use the "Contribution Margin" column if you have assigned one during the import process.
If contribution margin is not available for any order items, Everhort will use gross margin in its place.
As with all reports in Everhort, the data is also presented in tabular format and available for download as a CSV file. Read more about table views in Everhort.