Self-Attributing Network (SAN)

What is a Self-Attributing Network (SAN)?

A self-attributing network (SAN) is an ad network that tracks and reports its own performance metrics, such as installs and in-app events, without relying on third-party attribution providers.

What makes a SAN unique is that it controls both sides of the equation: it places mobile ads within its own ecosystem and directly measures the conversions resulting from those ads. In other words, a SAN both shows the ad and claims the credit.

This consolidates two roles that are usually separated displaying advertisements and attributing user actions like app installs and in-app events back to those ads into a single platform.

In simple terms, a SAN does its own homework and reports its own grade.

Because SANs operate in closed ecosystems with massive amounts of first-party data, they can attribute conversions with a high degree of accuracy on their own platforms. This is both their strength and the reason advertisers still rely on a mobile measurement partner (MMP) to keep them honest across the full marketing mix.

例: A user sees an ad for a mobile game on Facebook, clicks it, and installs the app. Facebook as a self-attributing network matches that install back to the ad it served using its own data, and reports the conversion directly to the advertiser. No third-party click URL is involved in that attribution.

Types of Self-Attributing Networks

SANs are generally categorized by the platforms they operate on social media networks, search engines, and other large digital ecosystems. In mobile advertising, the major platforms that operate as self-attributing networks are:

• Facebook (Meta)
• Google Ads
• Twitter (X)
• Snapchat
• アップル検索広告
• TikTok

Each of these networks provides a controlled environment where they can directly attribute user interactions like app installs or purchases to the ads displayed on their platforms.

TikTok as a Self-Attributing Network

TikTok operates as a SAN, meaning it attributes installs and events from TikTok ad campaigns using its own data rather than relying on third-party click tracking. For advertisers running TikTok campaigns, this means attribution data flows directly from TikTok and an MMP is what reconciles that self-reported data against your full cross-channel performance.

セルフアトリビューションネットワークはどのように機能するのか？

The operation of a SAN involves several steps. Here is how the attribution process works:

1. The user interacts with an ad on the SAN and installs the app. The SAN captures the user's advertising ID.
2. The app launches for the first time. The SAN's SDK, integrated into the app, collects the advertising ID.
3. The ID is communicated back to the SAN. The network matches it against its own ad interaction data.
4. If a match is found, the SAN attributes the conversion of the install or other event to the specific ad engagement within its platform.

Because this all happens inside the SAN's own ecosystem, the process is direct and the data is first-party. There is no external click tracking URL involved the way there is with non-SAN networks.

例: A user taps a Google Ads campaign for a fitness app and installs it. On first launch, the SDK collects the device's advertising ID and sends it to Google. Google matches the ID to the ad click in its records and attributes the install to that specific campaign all within its own platform.

SAN vs Non-SAN Networks

Understanding the difference between SAN and non-SAN networks is key to understanding how mobile attribution works as a whole.

With non-SAN networks, the MMP generates unique click tracking URLs and performs the attribution itself. With SANs, the network performs its own attribution and reports back and the MMP's role shifts to validating that data and unifying it with the rest of your channels.

Do I Need a SAN SDK? How to Use the Tenjin SDK for Attribution

If you are using Tenjin, you do not need to integrate the SAN SDK separately for attribution on self-attributing networks.

The Tenjin SDK runs attribution on SANs and provides you with attribution data and downstream metrics all from a single integration. This means you can measure performance across both SAN and non-SAN networks without managing multiple SDKs.

This is one of the core benefits of working with an MMP: a single SDK handles communication and attribution across every connected network, including the major SANs.

Why SANs Matter for Mobile Marketers

Self-attributing networks represent some of the largest and most important channels in mobile user acquisition. Facebook, Google, TikTok, and Apple Search Ads account for a significant share of mobile ad spend. Understanding how their attribution works is essential to interpreting your performance data correctly.

Here is why SANs matter:

• They control a large share of mobile ad spend. If you run user acquisition campaigns, you are almost certainly working with at least one SAN.
• They self-report performance. A SAN attributes conversions using its own data. This is accurate within its ecosystem but it also means each SAN has an incentive to claim as much credit as possible.
• Cross-channel reconciliation is essential. A user might interact with ads on multiple SANs and non-SAN networks before installing. Without a neutral MMP to unify and reconcile this data, you risk double-counting installs and overvaluing certain channels.
• They offer direct, low-discrepancy attribution. Because SANs operate in closed ecosystems, their attribution tends to have fewer discrepancies than third-party tracking at least within their own platforms.

例: A casual game runs campaigns on both Facebook and TikTok. Both networks claim credit for the same install because the user interacted with ads on each. The advertiser's MMP applies consistent attribution logic across both, assigns the install to the correct source, and prevents the team from paying twice or misjudging which channel actually drove the conversion.

主要な結論

A self-attributing network is a fundamental concept in mobile attribution — and understanding it changes how you read your performance data.

• A SAN attributes its own conversions. It both displays the ad and measures the result, using first-party data inside its own ecosystem.

• The major SANs are the biggest channels in mobile. Facebook, Google Ads, TikTok, Snapchat, Twitter, and Apple Search Ads all operate as self-attributing networks.

• SANs self-report, which is both a strength and a caution. Their attribution is accurate within their platforms, but each network has an incentive to claim credit. This is why cross-channel reconciliation through an MMP matters.

• SAN attribution differs from non-SAN attribution. Non-SAN networks rely on MMP-generated click URLs. SANs attribute internally and report back.

• You do not need to manage multiple SAN SDKs with Tenjin. A single MMP SDK handles attribution across both SAN and non-SAN networks.

• SANs provide direct, low-discrepancy attribution. Operating in closed ecosystems gives them clearer insight into conversions on their own platforms.

By understanding how SANs attribute conversions and pairing them with an MMP that unifies your data, you can:

• Reconcile self-reported data across all your channels
• Avoid double-counting installs claimed by multiple networks
• Make accurate, confident decisions about where your budget performs best

With Tenjin, attribution on self-attributing networks is built into a single SDK — giving you clear, unified insight across every channel without dependency on data teams.

Related Terms

• Mobile Measurement Partner (MMP)
• Attribution
• Last Click Attribution
• SKAdNetwork
• Ad Network
• Custom In-App Event
• Cost per Install (CPI)
• Advertising ID (IDFA and GAID)

よくある質問

セルフアトリビューションネットワーク（SAN）とは？ 

A self-attributing network (SAN) is an ad network that tracks and reports its own performance metrics including installs and in-app events without relying on a third-party attribution provider. It displays ads within its own ecosystem and directly attributes the conversions those ads generate.

Which networks are self-attributing networks? 

The major self-attributing networks in mobile advertising are Facebook (Meta), Google Ads, Twitter (X), Snapchat, Apple Search Ads, and TikTok. Each attributes conversions using its own first-party data within its platform.

How do self-attributing networks work? 

When a user interacts with an ad on a SAN and installs the app, the SAN captures the user's advertising ID. On first launch, the SDK collects that ID and sends it back to the SAN, which matches it against its ad interaction data and attributes the conversion to the specific ad engagement.

Is TikTok a self-attributing network? 

Yes. TikTok operates as a self-attributing network, meaning it attributes installs and in-app events from TikTok campaigns using its own data rather than relying on third-party click tracking. An MMP reconciles TikTok's self-reported data against your full cross-channel performance.

What is the difference between a SAN and a non-SAN network? 

A SAN attributes its own conversions using first-party data and reports them directly. A non-SAN network relies on an MMP to generate click tracking URLs and perform the attribution. With SANs, the MMP's role shifts to validating and unifying self-reported data.

Do I need a separate SAN SDK for attribution? 

Not if you are using Tenjin. The Tenjin SDK runs attribution on self-attributing networks and provides attribution data and downstream metrics from a single integration, so you do not need to manage a separate SDK for each SAN.

Why do I still need an MMP if SANs attribute their own conversions? 

Because each SAN self-reports and has an incentive to claim credit, and because users often interact with ads across multiple networks before installing. An MMP reconciles self-reported SAN data with your other channels, prevents double-counting, and gives you a single neutral source of truth.

Are self-attributing networks more accurate than third-party attribution? 

Within their own ecosystems, SANs offer direct, low-discrepancy attribution because they operate in closed environments with rich first-party data. However, that accuracy is limited to their own platform, which is why an MMP is still needed to measure and compare performance across all channels.