In the past, any app installed on the device could retrieve the device ID, allowing marketers and developers to measure campaign and in-app activities without access to users’ personal information.

However, that changed with the introduction of Apple’s App Tracking Transparency (ATT) framework, which now requires app owners to get permission to access an Apple user’s device ID. This shift towards a privacy-first world brings challenges for marketers looking to measure and optimize their campaigns. With less data available on individual users or devices, aggregated data is becoming increasingly important — we’ll explore this in more detail later.

Different types of device IDs

There are two main types of device IDs: Apple uses the ID for Advertisers (IDFA) and Android uses the Google Advertiser ID (GAID). They essentially work in the same way to connect a user’s actions with an ad campaign, an install, or in-app activities. The key difference today is that for Apple devices, marketers and app owners can only access the IDFA if the user has opted in.

The Apple IDFA comprises eight characters, a dash, and then three sets of four characters. All letters are upper case. Here’s an example:

It makes it possible to segment users by device type, usage patterns, and more for sharper campaign targeting.

It gives app owners a better understanding of how much users are engaging with their app by collecting in-app event data. This allows them to pinpoint when, where, and why users engage the way they do: whether they drop off or churn at a certain point, or continue down the funnel and become loyal, revenue-generating users.

Previously, Apple users had to opt out of tracking by enabling LAT (Limited Ad Tracking), which caused their IDFA to display as a string of zeros. The current ATT, on the other hand, means users can choose to opt in .

The proportion of users who do opt in to tracking varies by app type, but 2022 figures show an average global opt-in rate of 46% . That leaves a significant proportion of devices that can’t be tracked for marketing purposes. But it’s not game over for marketers: new tech solutions make it possible to measure campaign success without compromising user privacy.

The future of measurement with (or without) device IDs

With device-level data now in shorter supply, marketers are turning to aggregated data to inform their iOS campaigns. With this approach, data is consolidated across groups of users, revealing broader trends rather than individual actions.

As marketers adapt to the privacy-first landscape, various alternative methods have emerged to help attribute marketing activities.

SKAdNetwork

This is Apple’s privacy-centric framework that aims to measure app installs and campaign performance without compromising user anonymity.

While SKAdNetwork offers a solution based on aggregated campaign data, it comes with various limitations and complexities that marketers need to navigate. For example, postbacks (reports on activity) are delayed and there’s a limited number of them, focusing on the early stages of the user journey. This makes it harder to measure lifetime value or re-engagement activity.

Working with a mobile measurement partner (MMP) can help you get more from SKAdNetwork: they’ll handle the data securely and provide in-depth analysis and insights.

Machine learning and predictive analytics

Machine learning algorithms can help us understand trends in user behavior, so that we can predict how valuable these users might be over time, and whether a campaign is likely to be successful.

Probabilistic modeling can make predictions based on aggregated data, revealing behavioral trends while protecting user privacy. Combining these techniques with machine learning is a powerful and accurate way to understand user behavior and predict campaign success early on.

Incrementality

Incrementality testing uses a control and test mechanism to show the true value of your marketing efforts. In particular, it can tell you what portion of business was the result of a campaign, and what would have occurred organically.

This additional layer of intelligence can give you greater confidence in the success of your campaign.

Web-to-app flows

Web-to-app flows take a user from a webpage to a corresponding app.

In the context of iOS 14.5, web-to-app flows help marketers connect the dots without the use of the IDFA. This is because the journey includes ad networks and owned media, so first-party data can be used to optimize the experience.

Frequently asked questions

What is a device ID in mobile technology?

A device ID is a unique identifier assigned to a mobile device, used for tracking and analyzing app installations and in-app activities without revealing any personal user information.

How do device IDs differ between Apple and Android?

Apple devices use the ID for Advertisers (IDFA), and Android devices use the Google Advertiser ID (GAID). The main difference now is that Apple requires user consent to access IDFA, while Android devices provide GAID access more freely.

Why are device IDs important for marketers?

Device IDs are crucial for marketers as they enable deterministic attribution, providing an accurate picture of user behavior. They also help with personalized advertising, user segmentation, and detailed analysis of app engagement, all while maintaining user privacy.

What impact have privacy changes had on device IDs?

Privacy enhancements have made it more challenging for marketers to collect comprehensive user data for targeted advertising. This is particularly true on iOS, where the App Tracking Transparency (ATT) framework requires app users’ consent to share their device ID.

How can marketers adapt to the privacy-first landscape without device IDs?

Marketers can use aggregated data, SKAdNetwork for app install attribution, machine learning for predictive analytics, incrementality testing for measuring campaign effectiveness, and web-to-app flows to navigate the privacy-first landscape effectively.

Key takeaways

While device IDs used to be central to measurement and optimization in the mobile ecosystem, the shift to a privacy-first landscape has pushed app marketers to explore new solutions.