The Era of Static IDs Is Over – What Comes Next?
For publishers looking to grow ad revenue, moving on from third-party signals is critical. Adaptive identifiers could be the answer...
Despite yet another delay to Google’s cookie deprecation plans—hold the cynicism— the identity crisis facing publishers hasn’t gone away.
Third-party signals are deteriorating fast but at the same time, buyer expectations are rising: they want scalable reach, measurable performance, and regulatory compliance across every media environment (web, app, CTV, you name it).
For publishers, the old playbook—third party cookies, hashed emails, or deterministic logins—is no longer enough. Monetising fragmented audiences without sacrificing privacy or performance is the new challenge. So what does a smarter, scalable identity solution actually look like in 2025?
In this Q&A, Morwenna Beales, VP of Publisher Development at ID5, lays out the case for adaptive identity — an approach that uses machine learning to stitch together multiple pseudonymous signals and maintain addressability, even as traditional identifiers fall away. She explores what this means for targeting, measurement, and monetisation — and why publishers can’t afford to sit still.
What are the biggest limitations of static identifiers when it comes to targeting and measurement?
Static identifiers like hashed emails or third-party cookies simply weren’t built for today’s complex online ecosystem. For instance, cookies break down across browsers, and hashed emails rely on logins that many publishers don’t have at scale. In both cases, the result is patchy coverage and blind spots in measurement. More importantly, they create a limited, binary model of addressability, where users either have the ID or they don’t. For publishers, that means missed monetisation opportunities and an inability to offer advertisers consistent reach or performance metrics.
Despite Google’s reversal on deprecating third-party cookies, why is it urgent for publishers to move beyond cookies?
Because real signal loss is already here. Safari and Firefox have long blocked third-party cookies. Increasingly, we’re seeing instability in other signals, like IP addresses or device IDs, especially in channels like CTV.
The truth is, the web is already fragmented. If third-party cookies are still the foundation, publishers are already behind. Audience addressability is being constrained every day they remain the primary strategy. The most proactive publishers are already putting adaptable frameworks in place that work across today’s fragmented landscape.
How does a machine learning-based approach offer publishers something different?
Static identifiers depend on one fixed signal to identify a user; machine learning takes a different route. It pulls in multiple inputs, from contextual clues to pseudonymous IDs and login events, and links them using probabilistic techniques. This gives a dynamic, evolving picture of the user that holds up even as signals change across platforms and environments, giving publishers a more stable and consistent view of their audiences, whether users are on mobile web, connected TV, or browsing in cookie-restricted environments.
Ultimately, this gives publishers a smarter, more durable way to maintain addressability at scale, even when deterministic signals drop out.
In practical terms, what does that smarter approach look like for publishers?
At its core, it means being able to extend the value of audiences across browsers, devices, and even off-site. Many publishers initially implemented IDs for basic targeting, but we’re now seeing much broader use cases. Private marketplaces, audience extension, off-site monetisation, CTV integrations—all of that depends on having a unified framework in place. Ultimately, publishers need a system that can make sense of multiple signals, and activate them responsibly and effectively across the whole supply chain.
What’s changed in how publishers are thinking about their data strategies?
We’re seeing a real shift in mindset. Historically, many focused on deterministic IDs or waited for buyers to drive activation. Now, more are taking ownership, looking at how they can use identity to protect and commercialise their audience relationships directly.
Part of that means recognising the value of their unauthenticated users. Not every site has logins, and they shouldn’t be penalised for that. An adaptive identity framework gives those publishers a pathway to monetise that audience in a compliant and scalable way.
How do machine learning technologies help reinforce data protection and user privacy?
They help publishers honour consent mechanisms and apply the right data protection measures wherever their audiences are, adjusting in real time to user preferences to ensure data is only used in ways that meet the relevant legal requirements; whether that’s GDPR, CCPA, or another regulation. And because adaptive identity frameworks powered by machine learning work across jurisdictions, publishers can maintain addressability while enforcing the right privacy controls for each environment, making it possible to scale recognition and monetisation in a way that’s both compliant and respectful of the trust users place in them.
What are the risks of publishers sticking with a fragmented, legacy setup?
Ultimately, it limits revenue. Buyers want to activate audiences across the open web, but they need confidence in reach, measurement, and privacy alignment. A fragmented setup makes that hard to deliver. Operational inefficiency is another one, stemming from the various ID vendors, disconnected data sets, and siloed platforms that create friction for advertisers.
Looking ahead, how do you see identity evolving over the next few years?
We’re heading towards broader adoption of adaptive frameworks that work across channels, formats, and user states; logged-in or not. I think we’ll see publishers continue to expand their use cases, from onsite monetisation to offsite activation and audience stitching.
We’ll also likely see more convergence across buy and sell sides around what constitutes a standard for identity; something that supports measurement, frequency capping, attribution, and targeting without relying on fragile or opaque signals.
Publishers who prepare now, who adopt a scalable, flexible infrastructure, will be in the best position to thrive as the next phase of digital advertising takes shape.
About: Founded in 2017 by industry experts, ID5 is redefining identity for digital advertising, building solutions where privacy and addressability work in sync. Its Adaptive Identity technology learns and adjusts, ensuring seamless recognition across media properties, devices, and channels. This allows media owners to unlock sustainable revenue, enables advertisers to deliver measurable results, and helps platforms to maximise data and inventory value.