ID bridging refers to methods of matching of different End Users’ identifiers to create valuable datasets for targeting purposes without the total reliance on third-party cookies.
What is ID Bridging?
In brief, ID bridging implies the compilation of datasets to ensure high audience addressability using a variety of user identifiers, like consented login info, device details, and more.
From the technical standpoint, ID bridging uses the two methods of data matching: probabilistic and deterministic. Namely, probabilistic matching is known to be the most widely-used technique and implies the ML analysis of a broad scope of data elements, like IP address, browser history, device info and many more, and the use of high-end AI-powered algorithms to create audience datasets, aimed at securing high levels of targeting precision in digital advertising.
Meanwhile, deterministic matching relies more on the publishers’ first-party data, obtained with End Users’ consent, like hashed emails, and other so-to-speak, persistent identifiers, presumably consistent across different browsers and devices, hence ensuring maximum accuracy of the created audience datasets.
Benefits of ID Bridging
From a publisher’s perspective, the obvious benefit of using ID bridging is their ability to maintain the sufficient audience addressability in a more privacy-focused way, hence ensuring the high value of their inventory in direct and programmatic deals and keeping their online revenue streams consistent.
From an advertiser’s standpoint, ID bridging helps keep their online ad creatives visible to the precise target audience in the right context even without access to third-party cookies.
Potential Challenges
First and foremost, ID bridging inevitably requires substantial financial resources, both in terms of user signal data acquisition from multiple sources, and the continuous compliance with the changing privacy laws and regulations.
Secondly, ID matching usually requires extra operational resources in order to program the complex AI algorithms in order to at least maintain the minimum accuracy threshold of compiled audience datasets.
Last but not least, one of the major risks of ID bridging is the potential leakage of End Users’ personal data elements and their consequent use for shady and/or illegal purposes, especially in case the deterministic matching is applied.