Brands, marketers and publishers have relied heavily on the use of third-party cookies to track users across digital properties in order to serve them targeted ads. This practice began in the late 1990s and has effectively served the advertising community since.

Then came the regulations and the outcry from consumers regarding how personal information was obtained and shared across the digital landscape. Safari and Firefox banned third-party cookies in 2013, while Chrome is expected to deprecate them in 2024.

With the demise of third-party cookies, advertisers and brands alike will have to find new ways to replace targeting and maintain conversions, all while protecting the user’s privacy. One way to do this is by leveraging authenticated traffic solutions, such as Universal IDs (UIDs).

Identity solutions, or Universal IDs (UIDs), are one possible path to personalization and the opportunity to optimize campaigns based on sound user information and preferences.

But do UIDs have the power to replace the third-party cookie entirely? ‍

Is the UID the answer to protecting personal information?

For the consumer, sharing personally identifiable information (PII) such as a phone number or email address can have benefits: a personalized experience or an ad that the user might be interested in. While a tailored online experience and engaging ads may benefit users, the practice of cross-domain tracking and sharing of personal information is where the issue of data privacy lies.

For publishers and advertisers, data means money. The more a brand or advertiser knows about the consumer, the better they can target their respective audience. With the removal of third-party cookies, new methods of understanding an audience’s preferences and interests for better personalization and targeting will have to be explored.

One such way is the UID.The UID is a single identifier made up of a user’s personal information, such as an email or phone number. That identity is then shared to identify the user across the digital supply chain.  

UIDs were introduced as a replacement for third-party cookies. However, unlike third-party cookies, UIDs create an ID that anonymizes the user which protects that user’s privacy.

UIDs are created using one or both types of data: probabilistic and deterministic data.

Deterministic data is information supplied and authenticated by the user. Deterministic data is said to be true because it is supplied by the user. Personal information, such as an email address, name and phone number, is collected then matched across devices and tied to that user.

As opposed to supplied and authenticated data, probabilistic data is the outcome of pattern analysis, inferences and as its name indicates, based on probabilities. In the digital world probabilistic data can be used to create an identifier. Advancements in Machine Learning modeling and the rise of novel approaches such as analyzing deep behavioral data, introduced probabilistic modeling that allows for high accuracy.

How will UIDs impact brands, marketers and advertisers?

In the past, digital marketers and ad agencies have relied heavily on third-party cookies to sync user information across digital properties creating more personalized ads and content.

For advertisers, UIDs solve a short-term problem of replacing the third-party cookie. Unfortunately, UIDs lack one critical component - scale. Authenticated traffic solutions only account for about 10-20% of the open web, the rest is anonymous traffic. It’s the anonymous traffic that will need to be addressed for those that work within the digital ecosystem.

What can online brands do now to prepare for a cookieless world?

For brands and online businesses, understanding your options is a start.

Deep behavioral probabilistic models are how you preserve a personalized digital world. Deep behavioral AI models analyze a user’s interactions on a site then segment that data for better personalization. This first-party data supports better user experiences, audience targeting and marketing activities, and all with user privacy at the core.

When combined with contextual semantic analysis, high-value interest segmentations are able to be predicted twice as fast. This allows online brands and businesses to better understand their customers’ needs while maintaining that user’s privacy.

Additionally, analyzing user behavior reflects important considerations not accounted for with personal or interest-based information.

Capitalizing on AI-powered behavioral data with a scalable solution will shape a better user experience without compromising privacy.