3 Ways AI-Powered Predictive Insights Can Help Publishers Overcome Identifier Loss


The loss of personal identifiers such as third-party cookies makes it hard for digital publishers to get to know their audiences. But AI-powered technologies can provide the insight and user targetability they need to deliver personalized experiences and effectively monetize content, explains Jürgen Galler, co-founder and CEO, 1plusX.    

Knowing who their audience is and what they care about is vital for online publishers and media companies. First, it allows them to personalize user interactions, delivering the content audiences want to see and ensuring a seamless experience across all domains and apps. Second, it enables them to monetize their content through highly targeted advertising that brands are willing to pay top dollar for and that users find relevant and engaging.       

The continued loss of personal identifiers from the digital ecosystem is making it far more difficult for publishers to gain this level of audience understanding. The drop in revenue from third-party cookie deprecation alone is estimated to be over 50%Opens a new window , while changes to mobile identifiers such as Apple’s Identifier for Advertising (IDFA) will intensify this impact. With Google saying it will not support alternate identifiers for user tracking across its environments, the publisher situation looks precarious.      

But solutions are already being explored and implemented to address the absence of personal identifiers while maintaining user privacy, and artificial intelligence (AI) is taking a central role. Google’s FLoC initiative, for example, presents a cohort-based approach to targeting using machine learning (ML). Questions around its anti-competitive nature aside, FLoC will encourage innovation in ML-based segmentation and personalization, which is a positive trend for the industry. Here are just a few other ways AI will help online publishers overcome the loss of personal identifiers and get to know their audiences in the coming months and years.    

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Leverage First-Party Data More Effectively

As the direct point of audience interaction, publishers are in a strong position to leverage their own first-party data. They can use this information to create basic reach among known users and then syndicate user profiles through identity networks.This ties first-party data to a larger identity connectivity layer and creating bigger first-party-based addressable audience segments. AI-powered technology with high processing and orchestration capacity can help organize and harness this first-party data, consolidating and translating it into a holistic pool of manageable audience insight that is simple to activate. 

Using AI to make the most of data is highly beneficial. But, with log-in screens or paywalls frequently deemed too disruptive to the user experience to implement universally, most media companies will need to create scale on top of first-party strategies to effectively deliver personalized experiences and targeted advertising.    

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Increase Reach With User Attribute Predictions

Predictive modeling using AI and ML technologies is a powerful way for publishers to fill the gaps and achieve addressable reach extensions against customizable and verifiable accuracy rates. Smart algorithms can be used to analyze consented on-site activity and generate a deep understanding of user interests, habits and preferences to feed into user profiles. In addition, known attributes, such as account information, can be used as a basis for state-of-the-art audience modeling and expansion. This enables intricate attribute patterns to be identified and used to target users with similar traits, even when they aren’t logged in. This approach can also be used to predict user-level attributes, such as gender, age, and interest, which is what truly drives the value of first-party data.

Predictive modeling powers up the potency of publisher data and can be combined with real-time context and content data to make impressions addressable, even without personal identifiers. The addition of clean-room technology can further increase content monetization potential through retargeting by directly matching publisher and advertiser audiences based on similarities rather than a one-to-one connection. AI-powered predictive modeling allows publishers to enhance user engagement and drive monetization potential in a way that maintains user privacy, as it relies on logical rather than declared user attributes. 

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Supercharging Contextual Targeting

Contextual targeting, a tactic that aligns advertising to the context of digital content, is far from a new concept. But it is regaining popularity as the online ecosystem moves away from personal identifiers and is being supercharged by a new generation of AI-powered technology. A real and proven approach to cookieless targeting, this next-level contextual technology can support socio-demographic targeting and more. 

Advanced automated analytics allows publishers to identify the content users are accessing across multiple properties and uncover highly specific interest areas beyond simple keywords. Enriched contextual data will also enable media companies to enhance their audience targetability on mobile-native channels, as well as on CTV and podcast environments. This approach benefits brands, which can reach users actively engaged with applicable content as well as audiences who enjoy a user experience that is relevant to their immediate interests.    

As digital publishers and media companies look ahead to a world with fewer shared personal identifiers, no single solution will enable publishers to understand their audiences, personalize the user experiences, and effectively monetize content. Instead, they will need to take a multi-layered approach, with each aspect driven by AI and ML capabilities. By embracing advanced AI-powered tools, media companies can use valuable first-party data as a basis on which to build holistic audience insight and deliver both relevant, targeted advertising and a positive, personalized user experience.