How Does SKAdNetwork Work? Core Principles & Best Practices

SKAdNetwork has become a key tool for advertisers. On one hand, it maintains campaign analytics. On the other hand, it conducts optimization in a new privacy-first environment. Apple has continued to evolve the system, addressing issues in its earlier versions. Shortly, Apple Ads will be integrated with SKAdNetwork versions 1.0, 2.0, and 3.0. The update can resolve the issue of duplicate conversions by introducing a unified attribution model. However, there are concerns that Apple Ads may begin to “steal” last-touch conversions from third-party ad networks.

What Is SKAdNetwork?

SKAdNetwork (SKAN) is a framework that provides attribution data to advertisers on iOS. Ad networks receive information about ad clicks, impressions, and app installs. All while maintaining strict user privacy.

Since its launch, SKAdNetwork has undergone significant changes. Let’s take a closer look at each version.

SKAN 1.0

SKAN 1.0 was introduced as an alternative to IDFA. It offers a basic attribution model with strong protection of user data on iOS. Advertisers track installs following an ad click and receive only one postback per install.

The postback data is limited. It includes a campaign ID (with a maximum of 100 values) and the install time, which is delayed by 24–48 hours. SKAN1.0 did not support post-install events (such as registration or purchase) or view-through attribution.

SKAN 2.0

SKAdNetwork 2.0 was released alongside the launch of App Tracking Transparency in iOS 14. Apple introduced the conversion value, a more flexible post-install tracking window, and greater control over event attribution. Advertisers can update the conversion value within 24 hours of installation. Each update would reset the timer for sending the postback.

However, this version still has several limitations. SKAN 2.0 provides only one postback, allows only a limited data size of 6 bits. It does not support view-through attribution either. Additionally, if the conversion value was not updated within 24 hours, it would automatically reset to 0.

SKAN 3.0

SKAdNetwork 3.0 was designed to enhance support for advertising and monetization, including view-through attribution. With SKAN 3.0, installs could be attributed ad clicks and ad impressions. Additionally, it introduced limited support for web-to-app scenarios and offered improved compatibility with DSP platforms.

Nevertheless, this version still had its limitations. It continued to allow only one postback, did not support retargeting. It also required complex logic to manage the conversion value (timer + “last touch wins”).

SKAN 4.0

SKAN 4.0 addresses the key limitations of its predecessors (1.0–3.0). Particularly, it’s about data flexibility and depth of analytics. This version allows advertisers to gain deeper insights into campaign performance, introduces multiple postback events. SKAN 4.0 also offers more transparent control over attribution windows.

How does SKAdNetwork 4.0 work?

1. Ad interaction. A user sees and interacts with an ad, for example, by clicking a video ad on TikTok. This event is recorded as an impression or click. The ad network then sends this data to SKAdNetwork.
2. App install. If the user installs the app within a defined time window, the system credits the interaction.
3. Validation and postback. Apple verifies the installation. Once the user opens the app, SKAN sends a postback to the ad network or MMP. Advertisers receive information such as the campaign ID, conversion window, and conversion data.

Pros of SKAdNetwork

The introduction of Apple’s SKAdNetwork during the post-IDFA era created significant challenges for many marketers. The early versions had numerous limitations. On top of that, Apple launched App Tracking Transparency (ATT) — a prompt shown in apps asking users for permission to track their activity.

Each year, fewer users opt in to ATT. According to Singular, by mid-2024, the global ATT opt-in rate across all verticals dropped to just 13.85%. In light of ATT’s declining effectiveness, several key advantages of the SKAdNetwork product stand out:

1. User Privacy Protection. All installs and engagement data are aggregated and anonymous, complying with Apple’s ATT policy and GDPR.
2. Official Apple Support. Platforms using SKAN operate in full alignment with Apple’s privacy framework, ensuring consistency and compliance.
3. Reduced Attribution Overlap. iOS SKAdNetwork removes the need for dual attribution methods (like using both IDFA and fingerprinting), leading to cleaner and more accurate reporting.

There are several alternatives to SKAdNetwork in the mobile marketing space that offer significantly more accurate tracking:

1. Web-to-app — A tracking model that allows marketers to track user transitions from the mobile web to an app, enabling better attribution of installs and engagement.
2. Web-to-web — A tracking model where users move between web pages within a mobile browser without installing an app, while still allowing for traffic source identification and conversion attribution.
3. Probabilistic attribution — A probability-based measurement method used by ad networks to estimate conversions and user actions without deterministic identifiers.
4. Ad Attribution Kit — A solution based on SKAN methodology, supporting alternative app stores and allowing attribution of re-engagements.

We explored each of these alternatives in more detail in the other article.

What Are the Limitations of SKAdNetwork?

Compared to other attribution methods, iOS SKAdNetwork comes with certain limitations. Here are the most important ones:

1. Limited LTV tracking. In SKAN versions 1.0, 2.0, and 3.0, lifetime value is restricted to just 24 hours and depends on the activity window set by the advertiser. The last version expands this with three postback windows: from 0 to 2 days, from 3 to 7 days, and from 8 to 35 days.
2. Delayed postbacks. Due to postback delays of over 24 hours, advertisers are limited in their ability to make real-time campaign optimizations.
3. Granularity constraints. Early SKAdNetwork versions supported no more than 100 active campaigns. In the last SKAN version, this limit was increased to 10,000, greatly improving segmentation potential.
4. No re-engagement attribution. Because of Apple’s strict privacy measures, user-level data is not collected, making it impossible to accurately track re-engagements.
5. Privacy threshold. SKAdNetwork only sends conversion values if enough data is collected to protect user anonymity. If Apple determines the volume of events is too low, conversion values may be dropped. Similarly, the source app ID might be hidden if traffic originates from a lesser-known app, and the ad campaign ID can be replaced with generalized data.
6. Version compatibility requirement. The specific version of SKAN must be implemented in both your app and the app where ads are displayed. This directly affects attribution accuracy and optimization capabilities. For example, if your app supports SKAN 4.0 but the ad network only supports 3.0, you’ll be limited to SKAN 3.0 features only.

How Can Businesses Get the Most Out of SKAdNetwork?

To make the most of SKAdNetwork, follow these best practices:

1. Use the latest version of the SKAdNetwork product. Each new version offers more data and advanced features. Multi-conversion windows, hierarchical source identifiers, and coarse conversion values give you deeper insight into user behavior.
2. Optimize your conversion value mapping. A well-structured conversion value system lets you track installs and key post-install events during the crucial first 72 hours after app download.
3. Integrate with trusted MMPs. Mobile Measurement Partners help decode SKAN postbacks, consolidate campaign data, and offer clean, actionable analytics despite SKAN’s limitations.
4. Segment creatives and traffic sources. Take advantage of the Source Identifier (up to 4 digits in SKAN 4.0) for A/B testing, different traffic sources, geos, and campaign variations.
5. Adapt to SKAN’s reporting logic. Since SKAdNetwork works anonymously and with delayed postbacks, expect gaps in reporting. Build your analytics strategy with this in mind and combine it with LTV modeling for a more complete picture.

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