How will this vary for different business models?
For most businesses, the lifetime revenue cohort analysis chart will show a large amount of spending in the initial period and then increase more slowly over time. That initial spike is because customers are more likely to make their first purchase soon after they are acquired than at any other time. In cases where the acquisition event itself is a purchase, 100% of customers make a purchase in their first period. In cases where registration can happen before purchases, this effect is less drastic.
As an example, Groupon would likely have a much lower initial jump than Amazon, because many of the people who sign up for Groupon do not make a purchase right away. Unless there are a high number of refunds, this chart will slope up and to the right after the initial jump. The rate of growth tends to decrease over time because customers are most active when they first sign up. This causes the average to drop because the number of people in the cohort stays constant regardless of how many come back to buy more. In subscription businesses, the slope will decay less aggressively than in businesses where people make one-time purchases.
Occasionally, a subscription business will actually have a slope that increases over time. It is rare to see this, but it is a great signal for the business when it happens. This does not mean that there are zero churning customers, but rather that upgrades for customers that stay more than make up for the customers that leave.
How is this calculated?
There are two simple inputs to this calculation: how many members are in the cohort (which never changes), and how much revenue those members generated in the given period. To determine the members in the cohort, you count the number of users who were acquired in the period in question. An acquisition can be a first purchase, account creation, newsletter sign-up, or some other event. The revenue calculation is a bit more complicated. You want to sum revenue for orders that were placed by members of this cohort and took place within a fixed time period from their acquisition date (for example, the first three months). Finally, you divide the revenue by the number of members in the cohort for each time period in the chart and add this value cumulatively over time.
What are the variations of this chart?
There are many different kinds of useful cohort analyses. The most common variation is filtering by user acquisition source. For example, you might want to look at this chart for customers who came from organic search, paid search, or an affiliate program. This helps you understand if the customers from one acquisition source are more loyal or valuable than another. You can also filter by demographics or other user attributes.
Another way to look at the data is with an incremental, rather than cumulative, data perspective. This shows the incremental amount that an average user spends in each month after they are acquired. This is useful for forecasting the number of repeat purchases that you get from existing users. You can look at this with other things besides revenue as well. Some examples include margin and nonfinancial metrics like invites, votes, or messages.
