In the subscription economy, there are generally accepted standard metrics: revenue, MRR (monthly recurring revenue), ARR (annual recurring revenue), and a total number of subscribers. These metrics are obvious, you can find them in the billing system of the provider, for example, in App Store Connect or Google Play Console. But they are not enough to properly monitor the status of your app. Below I will describe some unobvious metrics. They are extremely useful to evaluate the health of the economy of your app.
The economy of subscriptions is built on renewals. General revenue graph or other metric is unlikely to be useful to you because you can't assess the "long tail" of subscribers.
Let's take a look at the app's revenue graph:
And now let’s see, what’s hidden here:
In this example, we see that almost all money comes from activations. This means that the app generates revenue mostly from first charges, not renewals, and subscribers retention is low. It might be because an app is fresh and has annual subscriptions.
Marketing attribution means assigning a user to a specific traffic channel. Thanks to this you can see which channel, ad campaign or ads a user came from. Attribution used to work great on iOS, but with IDFA deprecation and the introduction of SKAD, things have gotten more complicated. Most of the traffic other than Apple Search Ads is attributed to organics (i.e. has no attribution), there is no user-level attribution anymore unless the user has agreed to the tracking.
However, although individual users can’t be attributed to a certain channel, it’s still possible to look at the traffic distribution over channels.
Cohort analysis allows one to analyze groups of users (cohorts) united by a certain trait. Using it you can see the changes in the group over time, like churn rate, retention rate, and revenue generated by these users.
We recommend building cohorts by installs instead of purchases. That’s because the end goal of buying traffic is installs. So, building cohorts based on installs, you can easily tell if the cost of customer acquisition pays off.
On the chart above the rows correspond to cohorts of users, who installed an app on a certain month. M1 is the month of install. Then by columns, you can see the dynamics of how users are paying and unsubscribing from month to month. A system like this allows us to estimate how much money the cohort will bring in the future and whether it will be profitable. We can see how much money we had from a particular cohort in a particular month and how many subscribers churned.
Auto-renewal indicates if the user will renew the subscription in the next billing period. The problem is that the user, who cancelled their subscription, still considered active till the end of the paid period. In this case, the data on active subscriptions can be misleading.
On the charts below the users are broken down by their auto-renewal status:
The percentage of users with active auto-renewal status will help you see how much money you'll get in the next billing period.
The high unsubscription rate like 30% on the chart above is a signal to go deeper into the product and analytics. It might be useful to see if there is a correlation between the marketing channel and unsubsription rate. If there is no correlation, the reason could lie in the product itself.
A similar breakdown can be done for users who are in the free trial period.
A subscriber can request a refund for subscription any time, even after 364 days of using an annual subscription. A developer is not able to decide if to make a refund or not, because this process is managed by Apple.
The main thing to keep an eye on is the ratio of refunds to purchases. You’d better to get vigilant if there are more than 10% of refunds. The app with 30% of refunds can be removed from the App Store.
In my opinion, these are the top metrics to pay attention to. Depending on the specifics of the app and the business, they can be expanded or slightly different.