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Jun 15 2022
In early June, Apple held its annual developer conference WWDC 2022, which revealed that Apple's iOS 16, iPadOS 16, macOS 13 and other systems will usher in important updates. For app developers, a lot of attention is focused on iOS 16.
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With more competition, it's harder than ever to get an app to No. 1 on the App Store, according to the latest data.
It is understood that non-gaming apps now require about 156,000 daily app downloads to rank first in the App Store charts, up from 114,000 in 2019.
The situation with mobile games is a little different. According to statistics, the median amount of app installs required for a game app to reach the top of the App Store chart is 93,000, down 46% from 171,000 in 2019.
In addition, according to the App Store ranking algorithm, in addition to simple downloads, Apple has other criteria for ranking, and there are many factors such as application usage, amount of new users, speed, number of uninstalls and App Store ratings.
Many of our users opt to buy app downloads on our platform to optimize this data, quickly driving app rankings and improving app visibility.
This may be the biggest update of the conference. Apple has announced the next version of its attribution solution, SKAdNetwork, with several updates to the framework.
As expected, Apple SKAdNetwork will now support web-to-app attribution, allowing paid and organic user acquisition to capture attribution data for app downloads from your website or other web sources.
Here's one more question: "How will privacy thresholds affect this data?" Or in other words, how much of your daily web traffic does it take for SKAdNetwork to return attribution data? We will continue to report on its viability and value as it rolls out.
With SKAdNetwork, the main limitation is to allow paid UA teams to get only one conversion value, which represents one downstream event (whether it's an in-app purchase, level completed, registration, etc.)
This makes it impossible to continuously collect downstream event data, so the ability to understand how much revenue an activity generates is severely limited.
With multiple conversions, SKAdNetwork can now return up to three conversion values per download, each with a different attribution node as 0-2 days, 3-7 days, and 8-35 days. Each of these transformation values will be able to contain multiple participations.
This means that a more accurate value can be associated with an activity, assuming a privacy threshold for a particular source is met. This still does not fully track downstream events, which would allow accurate calculation of ROAS. But it significantly increases the level of attribution data you need to make decisions because multiple in-app purchases can be tracked.
This is probably the biggest update to SKAN.
Additionally, SKAN will now return limited data for sources that do not meet a privacy threshold called Coarsed conversion values. Therefore, for these campaigns, you still see limited conversion value data that contains only one-third of the values (low, medium, and high). This can be used to get an initial look at these low-traffic activities, rather than having no data at all - which is the current reality.
Another important update is that more data on the activities driving the attributed download will be returned instead of just 100 activity ID values. SKAN will now allow returning the four-digit number associated with an activity ID. This can be used to add information to a campaign about things like placement type, creative type, campaign value, or more.
In fact, instead of 100 campaign IDs, you now have 10,000 possible values. This means insights can be generated in a way more similar to traditional attribution, where fine-grained data is available at the ad creative level.
This will also allow certain ad networks to better optimize their campaigns as they will have information on the value that different creatives, placements and even audience targeting configurations can drive. That's a big gap from the real-time data they used to get advertisers' campaign optimization.
This added information applies only to source and activity IDs that exceed the privacy threshold, so from a measurement perspective, it is beneficial to expand the spend for a specific activity here.
Another update: App Store Connect now provides you with benchmark data, allowing you to compare app performance against industry benchmarks.
This includes conversion rate benchmarks in terms of user acquisition, user retention and crash rate benchmarks (more similar to the Vitals data you can get from the Google Play Console for Android), and monetization benchmarks such as average pay-per-procedure users.
As with any benchmark, you need to take into account that you can't fully understand its composition, so it's hard to estimate how comparable the app and mobile game you're measuring.
For example, looking at recommended conversion rate benchmarks that largely represent paid user acquisition traffic, you have to take into account the fact that conversion rates are highly influenced by the ad network you use and the type of ad you're using.
If you find that your app's conversion rate is below the benchmark, that doesn't necessarily mean you're not doing well. It might just mean that the benchmarks include apps or mobile games with significantly different combinations of UA channels.
Having said that, you can definitely use the benchmark data as another data point to allow you to identify areas for improvement, as long as it's not the only data point you rely on.
What's new these updates for app marketers? The landscape for measuring paid user acquisition is still emerging, and these developments will be warmly welcomed by the UA side of the mobile marketing team.
We still need to remember that this plan looks great on paper, but the reality so far shows us that SKAN is far from a complete framework. It's almost impossible for teams to use it to actually generate insights that would allow them to optimize their UA spend.
This is partly due to privacy threshold restrictions and partly due to very inconsistent performance. But this evolution of SKAN as a framework will definitely require you and your team to go back to the drawing board and experiment more to see exactly how feasible it is, and how this additional information will enable you when making UA decisions benefit.
As this update is due out later this year, we'll keep you posted on how it's progressing, as well as the potential benefits the UA team will be able to gain from it.
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