Redefining ATT opt-in measurement: Setting the standard for data accuracy

The introduction of Apple’s App Tracking Transparency (ATT) framework in 2021 marked a fundamental shift in digital measurement, reinforcing user privacy as a non-negotiable standard.

Changing the default from opt-out to opt-in significantly reduced the availability of user-level data, creating new challenges in measurement and attribution while increasing data fragmentation across the industry. Despite this, opt-in rates have been higher than expected and have even increased over time.

Recognizing the importance of accurate opt-in measurement, AppsFlyer has invested heavily in refining a precise methodology. In this blog, we will share our method and demonstrate why we believe it sets the industry standard for data accuracy in the post-ATT era.

The role of the ATT prompt

Before we dive in, a quick reminder: The ATT prompt serves as the gateway to obtaining user-level data on iOS 14+ devices.

Apps are not required to display the prompt, but those that choose not to can no longer collect user-level data and must rely solely on SKAdNetwork (SKAN) for deterministic, aggregate attribution. Given this, it should come as no surprise that the vast majority of apps choose to display the prompt at some point in the user journey.

If a user does not select “Allow” in both the source app (where the ad was seen) and the target app (where the ad directed the user), the IDFA is not shared with ad networks or Mobile Measurement Partners (MMPs), preventing ID matching for user-level attribution.

On the other hand, users who do allow tracking provide valuable insights, enabling marketers to analyze full data granularity and use this data to model the behavior of the larger non-consenting audience.

Because the value of iOS users is high, marketers must continue to engage these audiences despite privacy restrictions. Opt-in rate benchmarks play a critical role in helping apps:

• Understand the impact of the prompt on the user experience
• Leverage consented data to model non-consenting cohorts more effectively

Collecting consent: A more accurate approach

AppsFlyer’s methodology focuses on the user experience, capturing a complete and precise picture of the opt-in journey by categorizing each status accurately.

Let’s see how this works in the following illustration:

As shown, the first step is to exclude users who will never see the ATT prompt, such as:

• Children (under Apple’s age restrictions)
• Users who have disabled tracking requests at the system level

Data on the share of users labeled as Limit Ad Tracking (LAT) in iOS 14 and earlier was combined with ‘restricted’ data statuses we receive from the ATT prompt. Since LAT rates ranged between 25–30% while 15% of users are classified as ‘restricted,’ we estimate that 10-12% actively denied access at the system level.

From there, we analyze what happens in two key scenarios:

• The ATT prompt appears before the SDK initializes.
• The SDK initializes before the ATT prompt is shown, but an in-app event later triggers the prompt.

Crucially, in the second scenario, where the prompt is shown after the user has already engaged with the app, we observe a slightly higher opt-in rate based on the share of users who were first recorded as “not determined” and later their status changed to “denied” or “allowed”. 

We believe the higher opt-in rate in this scenario (45% vs. 36%) is due to the notion that trust builds over time—the longer a user interacts with an app, the more likely they are to opt in when prompted.

That said, we also recognize that many users maintain fixed opt-in or opt-out preferences, regardless of context, which is why the opt-in rate gaps are not significant. 

The bottom line is that the true opt-in rate is 40%, as it accounts only for users who actually see the prompt, providing the most accurate insight into user behavior.

However, if we take a broader view of targeting—factoring in users who don’t see the prompt because they are restricted or have actively denied access at the system level—the rate drops to 30%. That said, these LAT users were always excluded from targeting even before ATT, which is why we exclude them in our standard calculation.

Why accuracy matters

AppsFlyer’s methodology ensures a precise and trustworthy view of both consented and non-consented users by accurately distinguishing between user engagement with the ATT prompt, whether it was shown before the SDK was triggered or later via an in-app event. This approach provides a more reliable opt-in rate, eliminating distortions and offering a clearer picture of user behavior.

Maintaining accuracy in ATT measurement is essential for the industry’s long-term success. Without it, marketers risk making misguided decisions based on incomplete or misleading data, affecting everything from attribution modeling to budget allocation.

By prioritizing accuracy, the industry can continue to measure effectively in a privacy-conscious world, ensuring transparency and meaningful results.