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.
When you first start digging into how cohort analysis can help you learn about the health of your business, you’ll typically encounter a lot of triangle-shaped retention charts that look like this:
Orienting yourself to these charts can take a minute, even if you’ve done it before. What do all these numbers mean? What’s with the colors? And why is it triangle-shaped?
These charts tabulate the percentage of customers who return each period after their first purchase and make a repeat purchase. Each row represents a cohort of customers acquired during a given period (usually a week or a month), which is listed in the first column. Cohorts are ordered from oldest to newest moving down. As you move to the right along a row, each column shows how many customers from that cohort returned or made a repeat purchase in each subsequent period. Older cohorts have more periods of possible retention than newer ones, which is why each row is shorter by one column than the row above it, producing the triangle shape. As for the colors, typically these charts will highlight cells above a given retention percentage in green, cells below a certain percentage in red, and those in between in yellow.
Once you’ve familiarized (or re-familiarized) yourself with the structure of these charts, you’re faced with another, more challenging question: how do you make sense of the data? Should you read it top down? Left to right? Diagonally? As humans, we have a natural tendency to see patterns in data, and your eye is probably drawn to multiple patterns in the above chart. Are those patterns significant, or are they just noise? What do they mean? How is my business actually doing?
Retention charts are confusing and overwhelming. There’s a better, more intuitive way to understand the health of your business.
Lifetime Value by Cohort
Customer lifetime value (LTV or CLV) is the single best metric for understanding the health of a business. If average order value goes up, lifetime value goes up. If margins improve, lifetime value improves. If customer retention improves, lifetime value improves. The opposite is also true. If order value, margin, or retention drop off, customer lifetime value is also going to suffer. LTV should be your go-to metric for the simple reason that it reflects the aggregate impact of many of the underlying drivers of profitability.
If we view LTV through the lens of cohorts grouped by acquisition date, we can open up another important dimension of understanding: how things are changing in the business over time.
Let’s see how this works by looking at an example LTV by Cohort graph exported from Everhort for a fictitious company:
Orienting yourself to this chart is easy. Each line shows the cumulative average lifetime value of a cohort over time. The longer (and darker) the line, the older the cohort. The Y-intercept, month 1 for all cohorts, shows the average value of their first order. From this chart we can see that first order value has been increasing steadily on average over the last 12 months, because the shorter, lighter lines have higher Y-intercept values than the longer, darker lines.
By comparing the shape and slope of each line, we can understand how much value each cohort contributes to the business over time. Lines that increase more sharply provide more value more quickly. In the chart above, we can see that recent cohorts have steeper slopes than older cohorts. This means the business is improving at recognizing more value in less time.
Here’s an example of a different fictitious company:
Like the previous company, this company is steadily increasing average first order value, as the Y-intercepts are rising for shorter, younger cohorts. Unlike the previous company, however, this company’s cohorts quickly flatten out after about 6 months. Flattening cohorts are a warning sign in these graphs. It means customers in the cohort have virtually stopped engaging and are no longer producing value.
LTV by Cohort charts help us quickly answer important questions like:
How long do newly acquired customers engage and generate value for our business ?
How does the rate of engagement of new customers compare to older ones?
Is the business improving the rate at which it generates profit from customers over time?
Answering these question can help confirm if tactics being worked on to improve underlying drivers of LTV are working or not, or what areas might need to be investigated further.