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

buy reviews ios

buy reviews ios

Subscription providers are booming, with a median of $20 spent per 30 days per buyer. And whereas just one% of apps monetize with subscriptions, over 90% of cellular client spend comes from subscription apps. With a lot income at stake, it’s very important that builders are environment friendly in how they optimize their funnel.


Table of Content

As we highlighted in a latest article with, it’s much more necessary for apps that monetize by way of subscriptions to have a great consumer opt-in technique publish iOS 14.5+ to make sure that strong deterministic knowledge for all factors alongside the consumer lifecycle, could be collected. For subscription apps, the consumer journey is often longer and extra convoluted than in different monetization methods, that means it pays to have all the information you may get.

However even for customers who select to opt-out, having a sturdy SKAdNetwork plan in place offers you the chance to work out consumer LTV with some confidence.

Getting the opt-in

Securing excessive consumer opt-in charges will permit apps to realize a big aggressive benefit, each when it comes to accessing factual, deterministic knowledge about their customers, in addition to permitting them to create fashions based mostly on the conduct of their customers who opt-in.

Using pre-permission prompts will help clarify to customers the good thing about consenting to user-level monitoring, and there’s loads of recommendation on the best way to craft the proper pre-permission immediate.

For subscription apps, having insights into when customers’ cost methodology fails, once they pause or cancel subscriptions, or once they resume, are all key insights that may assist optimize your app. With Regulate’s subscription monitoring answer, you may get an unprecedented view into the consumer lifecycle. Nonetheless, with out the IDFA it turns into more and more tough to get dependable knowledge on how customers are navigating this mazy journey towards conversion.

Utilizing SKAdNetwork

For apps that monetize by way of subscriptions, the issue in iOS 14.5+ is twofold. Firstly, having the ability to reliably defer the SKAdNetwork timer past 24 hours poses a problem, even when it is perhaps helpful for gathering indicators out of your customers.

It’s attainable to increase the timer through the use of a bit to delay the conversion window, merely triggering a conversion worth replace (as an illustration from 000001 to 000011 and so forth) periodically to realize one other 24 hours — nevertheless it requires the consumer to log in day by day in order that the conversion worth set off can run with the app within the foreground. If the consumer doesn’t open the app once more, the conversion worth can’t replace, that means that you simply lose out on the information you have been hoping to delay the timer to gather.

Secondly, getting sufficient knowledge from the consumer within the first 24 hours to make dependable long-term predictions is hard. With solely a restricted variety of touchpoints attainable, as a result of restricted 6-bits of attainable values, it is very important just be sure you actually zero in on those which might be essentially the most significant and get essentially the most out of those necessary first 24-hours.

Sign versus noise

There are two primary methods you should utilize the 6-bits given to you by SKAdNetwork. The primary is utilizing a ‘bit masking’ strategy, the place you assign every of the six bits to an occasion, and whether or not that corresponding bit is ready to a 0 or a 1 tells you whether or not that occasion occurred.

Our commonplace SKAdNetwork answer lets you map conversion occasions to the subscription occasions you already observe within the Regulate dashboard.

The second choice is to assign ranges of values to completely different conversion values, which lets you create ‘buckets’ of customers relying on the place they fall inside the ranges you outline. Our superior conversion worth administration system helps creating customized schemas to outline these buckets.

For video streaming or courting apps, consumer engagement is among the many most necessary metrics —  so some corporations are optimizing utilizing the “classes” situations in our superior conversion worth answer.

The “classes” situation lets you observe the overall variety of classes logged. Within the instance beneath, a conversion worth of “3” will probably be returned if the consumer registers between 5 and 10 classes.

  • count_min(defaults to 1) – The entire quantity of classes tracked shouldn’t be lower than the required quantity;
  • count_max(defaults to limitless) – The entire quantity of classes tracked shouldn’t exceed the required quantity;

Making a mannequin

Predictive LTV modeling makes use of the conduct of a consumer on their first day of utilizing the app to foretell income going ahead within the medium time period. Such predictive modeling works higher when used for broader buckets or classes.

For that reason, subscription apps could need to use ‘trial begin’ as their SKAdNetwork sign to optimize towards, each as a result of this may increasingly occur extra reliably within the window the place you’ve visibility and since it’s an motion inside that preliminary window that is stuffed with intent.

Nonetheless, merely utilizing ‘trial begin’ may lead you down the mistaken path. And with out an perception into the occasions which might be occurring throughout the trial, post-IDFA it may be even trickier to imagine {that a} free trial essentially converts to a consumer that generates income.

The trial

For that reason, it’s possible you’ll need to think about ‘trial begin’ and a further, associated sign that has the potential of enriching the kind of trial. As an example, a consumer could set off ‘trial begin’, and be assigned an preliminary conversion worth. You possibly can then replace the conversion worth in the event that they cancel the trial throughout the conversion worth window. That instantly removes numerous people who find themselves unlikely to pay, creating a large bucket of ‘canceled trial’ customers that we will assume are possible to have a decrease LTV.

Or from the opposite perspective, perhaps you need to observe individuals who join a free trial and embody their cost info. Those that embody their cost information are already indicating they’re open to changing — and maybe extra prone to grow to be long-term paying customers.


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