How to build a subscription app analytics strategy on iOS 14.5+

Subscription services are booming, with an average of $20 spent per month per customer. And while only 1% of apps monetize with subscriptions, over 90% of mobile consumer spend comes from subscription apps. With so much revenue at stake, it’s vital that developers are efficient in how they optimize their funnel.

As we highlighted in a recent article with gamesindustry.biz, it’s even more important for apps that monetize via subscriptions to have a good user opt-in strategy post iOS 14.5+ to ensure that solid deterministic data for all points along the user lifecycle, can be collected. For subscription apps, the user journey is typically longer and more convoluted than in other monetization strategies, meaning it pays to have all the data you can get.

But even for users who choose to opt-out, having a robust SKAdNetwork (SKAN) plan in place will give you the opportunity to work out user LTV with some confidence.

Securing high user opt-in rates will allow apps to gain a significant competitive advantage, both in terms of having access to factual, deterministic data about their users, as well as allowing them to create models based on the behavior of their users who opt-in.

The use of pre-permission prompts can help explain to users the benefit of consenting to user-level tracking, and there’s plenty of advice on how to craft the perfect pre-permission prompt.

For subscription apps, having insights into when users’ payment method fails, when they pause or cancel subscriptions, or when they resume, are all key insights that can help optimize your app. With Adjust’s subscription tracking solution you can get an unprecedented view into the user lifecycle. However, without the IDFA it becomes increasingly difficult to get reliable data on how users are navigating this mazy journey toward conversion.

For apps that monetize via subscriptions, the difficulty in iOS 14.5+ is twofold. Firstly, being able to reliably defer the SKAdNetwork timer beyond 24 hours poses a challenge, even if it might be useful for gathering signals from your users.

It is possible to extend the timer by using a bit to prolong the conversion window, simply triggering a conversion value update (for instance from 000001 to 000011 and so on) periodically to gain another 24 hours — but it requires the user to log in every day so that the conversion value trigger can run with the app in the foreground. If the user doesn’t open the app again, the conversion value can’t update, meaning that you lose out on the data you were hoping to prolong the timer to collect.

Secondly, getting enough data from the user in the first 24 hours to make reliable long-term predictions is tricky. With only a limited number of touchpoints possible, due to the limited 6-bits of possible values, it is important to make sure that you really zero in on the ones that are the most meaningful and get the most out of these important first 24-hours.

There are two main ways you can use the 6-bits given to you by SKAdNetwork. The first is using a ‘bit masking’ approach, where you assign each of the six bits to an event, and whether that corresponding bit is set to a 0 or a 1 tells you whether that event occurred.

Our standard SKAdNetwork solution allows you to map conversion events to the subscription events you already track in the Adjust dashboard.

The second option is to assign ranges of values to different conversion values, which allows you to create ‘buckets’ of users depending on where they fall within the ranges you define. Our advanced conversion value management system supports creating custom schemas to define these buckets.

For video streaming or dating apps, user engagement is among the most important metrics —  so some firms are optimizing using the “sessions” conditions in our advanced conversion value solution.

The “sessions” condition allows you to track the total number of sessions logged. In the example below, a conversion value of “3” will be returned if the user registers between 5 and 10 sessions.

"sessions": { "count_min": 5, "count_max": 10 }

• count_min (defaults to 1) – The total amount of sessions tracked should not be less than the specified amount;
• count_max (defaults to unlimited) - The total amount of sessions tracked should not exceed the specified amount;

Predictive LTV modeling uses the behavior of a user on their first day of using the app to predict revenue going forward in the medium term. Such predictive modeling works better when used for broader buckets or categories.

For this reason, subscription apps may want to use ‘trial start’ as their SKAdNetwork signal to optimize toward, both because this may happen more reliably in the window where you have visibility and because it is an action within that initial window that is full of intent.

However, merely using ‘trial start’ could lead you down the wrong path. And without an insight into the events that are happening during the trial, post-IDFA it's going to be even trickier to assume that a free trial necessarily converts to a user that generates revenue.

For this reason, you may want to consider ‘trial start’ and an additional, related signal that has the potential of enriching the type of trial. For instance, a user may trigger ‘trial start’, and be assigned an initial conversion value. You can then update the conversion value if they cancel the trial during the conversion value window. That immediately removes a large number of people who are unlikely to pay, creating a wide bucket of ‘canceled trial’ users that we can assume are probable to have a lower LTV.

Or from the other perspective, maybe you want to track people who sign up for a free trial and include their payment information. Those who include their payment info are already indicating they are open to converting — and perhaps more likely to become long-term paying users.

If you have any questions about the implementation of iOS 14.5+, don’t hesitate to reach out to your CSM or account manager. Our latest guide is available for download here or check out our iOS and SKAN Solutions.