In an omnichannel world, user journeys occur across multiple platforms and devices, causing the fragmentation of user identities. This makes it difficult to track customer behavior and analyze campaigns accurately. Thus, Airbridge's identity resolution engine helps to unify all fragmented user identities into one unified and unique identity for accurate marketing measurement.
Identity Matching Logics
Airbridge employs both Device Matching (deterministic) and the Probabilistic Modeling Matching method.
Device Matching (Deterministic Method)
- App Store Install Referrer (Android Only)
- If Android SDK is enabled to collect install referrer information from Google Play or Onestore, Airbridge can fetch the referrer information from respective app stores whenever a user installs an app after clicking an ad. Airbridge uses this information to match the user who clicked the ad and the user who installed the app.
- ID Matching
- Touchpoints occurring from in-app ads include a unique device identifier (GAID, IFA) - also known as Device UUID. Airbridge matches this Device UUID to browser cookies and ensures these fragmented identifiers of web and app belong to the same user.
- Deeplink Matching
- If a user clicks on a deep linked ad, Airbridge matches cookies in the deeplink parameter to the Device UUID of the deeplink event and ensures that the fragmented identifiers of web and app belong to the same user.
- Platform Matching
- In the case of Self-Attributing Networks (SAN) such as Google Ads and Apple Search Ads, Airbridge fetches touchpoint information of the converted event and ensures that the user who performed the event is the same user who created the touchpoint.
Probabilistic Modeling Matching
- Probabilistic Modeling Matching
- Probabilistic Modeling Matching collects non-unique information such as a device model name, OS, or IP address, and then matches the user that performed the event to the same user who created the touchpoint.