IDFA and mobile advertising: operation, impact and alternatives after ATT

What is IDFA and how does it work?

The IDFA is an alphanumeric identifier assigned to each Apple device. Unlike cookies, it enabled users to be tracked across multiple applications without revealing their personal identity.

Technical operation of the IDFA

1. Allocation and storage: Each Apple device receives a unique IDFA stored in the operating system.
2. Access for developers and advertisers: Applications can access this ID via Apple’s API.
3. Inter-app tracking: Advertisers and tracking platforms (MMPs) use IDFA to track user interactions between different applications.
4. Advertising optimization: IDFA is used to create advertising profiles, measure conversions and allocate application installations to corresponding campaigns.

What were the risks associated with IDFA?

• Invasion of privacy: IDFA enabled cross-application tracking, raising concerns about the protection of personal data.
• Dependence on third-party platforms: Advertisers relied on IDFA for targeting and attribution, creating a dependence on Apple and DSP ecosystems.
• Risks of circumvention and fraud: Before ATT, practices such as illegal fingerprinting and attribution fraud abused the IDFA.
• Vulnerability to regulations: Evolving privacy laws (GDPR, CCPA) have progressively restricted the use of advertising identifiers.

The beginnings of privacy with Limit Ad Tracking

Limit Ad Tracking (LAT), created in 2016, was an option introduced by Apple allowing users to limit the use of their IDFA for advertising purposes. When a user activated LAT, the IDFA was still present, but apps could no longer use it for personalized targeting or cross-app tracking. This offered a first level of control over data confidentiality, but it was still possible to perform fingerprinting or exploit other signals for tracking.

Why has Apple introduced App Tracking Transparency (ATT)?

Apple introduced ATT in April 2021 to strengthen personal data protection and offer users more explicit control over ad tracking. There were several reasons for this decision:

1. Greater transparency: unlike LAT, which had to be activated manually in the settings, ATT requires each application to request authorization to track the user as soon as it is opened.
2. Protection against fingerprinting: by hiding IDFA by default and prohibiting alternatives such as fingerprinting, Apple has sought to prevent circumvention practices.
3. Regulatory alignment: ATT is part of a global trend of tightening data privacy laws (GDPR in Europe, CCPA in California).
4. Enhanced user experience: Apple wants to offer a more privacy-friendly browsing experience, avoiding the feeling of being constantly tracked and monitored.

The impact of App Tracking Transparency (ATT) on IDFA

With ATT, users must explicitly give their consent to be tracked via IDFA. This update has led to an upheaval in the mobile advertising landscape:

• Drastic reduction in the opt-in rate: Only 10% to 20% of users agree to be followed.
• Less usable data: Marketers have lost much of their ability to track the user journey accurately.
• Challenge for campaign targeting and optimization: The absence of granular tracking makes personalization more difficult.
• Changing media buying strategies: DSP platforms and advertisers have had to adapt their targeting methods, moving from an IDFA-based approach to more aggregate data-driven strategies.
• Impact on retargeting: Individualized remarketing has become much more complex, forcing advertisers to explore other solutions, such as cohort models or contextual targeting.

Post-IDFA alternatives and solutions

Using the SKAdNetwork

Apple offers the SKAdNetwork as an alternative solution for measuring conversions in an anonymized way. It works differently from IDFA:

• Attribution à l’échelle des campagnes, et non des individus.
• Délai de transmission des données pour préserver l’anonymat.
• Moins de granularité dans les insights obtenus.

First-party data processing

• Data collection via registration and user interaction.
• Audience segmentation based on in-app behavior.
• Using machine learning to analyze engagement patterns.

Contextual targeting and cohort-based advertising

• Align ads with viewed content.
• Grouping of users sharing similar characteristics without individual follow-up.

Use of platforms adapted to the post-IDFA environment

DSPs such as Moloco, AppLovin or Unity Ads exploit AI and machine learning to compensate for the loss of detailed data.

Optimize Mobile opt-in for better performance

In order to collect more consented data, there are a few best practices to adopt:

Carefully crafted introductory message

• Explain to users the concrete benefits of ad tracking (personalized experience, more relevant ads).
• Use clear, reassuring language, without technical jargon.

Choosing the right time to post the request

• Avoid displaying the request as soon as the app is opened.
• Wait until the user has interacted with the application and understood its value.

Use a pre-permission prompt

• Before the official Apple pop-up, display your own message explaining why the user should accept tracking.
• Example: “Accept tracking to see offers tailored to your preferences and support our free app.”

Personalize the experience in case of refusal

• If a user refuses, offer an alternative such as detailed onboarding on the benefits of tracking.
• Leave the option to change your mind later in the settings.

Testing different approaches (A/B Testing)

• Test several variations of the pre-permission message to see which generates the most opt-ins.
• Analyze the moments when users are most inclined to accept (after a purchase, after seeing an ad).

Offer added value in exchange for consent

• For example, offer a bonus or exclusive feature to users who accept the opt-in.
• Be careful to comply with Apple’s rules: opt-in must not be an obligation to use the app.

Reinforce internal signals

• Use in-app events to estimate campaign performance.

Key concepts related to IDFA

• SKAdNetwork : Apple’s tool for assigning installations without revealing the user’s identity.
• Fingerprinting : A technique that deduces a user’s identity from the characteristics of his or her device, but is increasingly regulated.
• First-party data : Data collected directly by companies, as opposed to third-party data.
• Ciblage contextuel : Advertising based on displayed content rather than user history.

Conclusion

IDFA used to be a mainstay of mobile marketing, but its reduced access is forcing advertisers to innovate. By adopting strategies based on first-party data, SKAdNetwork and artificial intelligence, it’s possible to continue running effective campaigns. The future of mobile advertising lies in a more ethical and transparent approach, fostering relationships of trust with users.