ユーザージャーニーとは、ユーザーと広告のインタラクションと、それに伴うビジネス上の成果を意味します。広告とのインタラクションの個々をタッチポイント、ビジネス上の成果をコンバージョンと呼びます。

Airbridge uses device IDs to connect clicks to installs. Available device IDs include IDFA and IDFV for iOS, and GAID and OAID for Android.

In addition to device IDs, Airbridge can collect cookie IDs while redirecting users to an app store. When a user clicks on an in-app ad, the Airbridge tracking link used in the ad will collect the device ID of the user. Then, when the user is briefly sent to a webpage called “Airpage” before being redirected to the app store, Airbridge collects the cookie ID of the user.

For ad campaigns on self-attributing networks (SANs), such as Meta Business, Google Ads, and Apple Search Ads, Airbridge uses identifiers provided by the SANs for attribution.

Tracking links are not used to measure campaigns on SANs, but whenever an app install occurs, Airbridge requests the SANs to send the touchpoints that may have driven the install. Airbridge uses the identifiers provided by the SANs for attribution.

SKAdNetwork (SKAN) is Apple’s privacy-centric attribution framework for iOS devices. SKAN allows for app install measurement without user or device identifiers. Airbridge supports SKAN in parallel with other attribution methods.

For more details on SKAN, refer to this article.

The user may have already installed the app on the device. In this case, upon clicking on a deep link-embedded ad, the user is directly sent to the app without landing on an app store. This allows Airbridge to connect the click directly to the app open using the information collected with the deep link.

Deep linking can be used for re-engagement attribution.

An install referrer is a string of information used to measure ad performance on Android devices.

When an Android user clicks on an advertisement and gets redirected to Google Play or another app store, Airbridge sends a referrer string to the app store.

Then, when the user opens the app for the first time, the Airbridge SDK requests the app store for information about the install. The app store shares the information based on the parameters set in the referrer string. Airbridge uses the information to perform attribution.

Note that Meta has introduced its own install referrer designed to attribute Android installs via ads on Meta apps. For more information about Meta install referrer (MIR), refer to this article.

Airbridge uses probabilistic modeling to infer the connection between touchpoints and conversions within a 6-hour timeframe.

• Additional attribution rules and machine learning-driven statistical modeling are used.

• Certain Airbridge features use statistical inference based on data collected via tracking links, such as the device model, OS name, and app ID.