Third-party cookies are not about to disappear, for most web traffic, they have already gone. Both Safari and Firefox have been blocking them for some time, global privacy regulations are increasing, and advertisers who believe there will be a single, seamless date to migrate are already late to the game.
What the Cookie-less Reality Actually Looks Like
Third-party cookies were never a precise tool. They were a workaround that the industry treated as infrastructure. When Safari began blocking them by default in 2017, most advertisers barely noticed because Chrome still dominated. That’s changed. Between browser-level restrictions, regulatory pressure, and growing consumer awareness, the ecosystem that was built on cross-site user tracking is fragmenting whether brands are ready or not.
The question isn’t whether your targeting strategy will be affected. It’s whether you’ve built anything to replace it.
The good news is that the replacement strategies aren’t compromises. They’re genuinely better, less reliant on surveillance, more aligned with how people actually want to interact with brands, and more durable over time.
Native Formats and the Ad-Blocker Problem
Ad blockers are a symptom, not a cause. When users install ad blockers, they’re responding to formats that interrupt, deceive, or slow down their experience. The solution isn’t to find technical workarounds, it’s to use formats people don’t feel the need to block.
Native advertising works because it’s designed to exist within the editorial environment rather than interrupt it. When the ad matches the visual language, pacing, and tone of the surrounding content, it gets read rather than reflexively dismissed.
Publishers maintaining strong ad revenue without invasive tracking are often doing it through native display ads that integrate with their site’s aesthetic and load without creating friction. The ad doesn’t announce itself as foreign. It earns attention the same way the surrounding content does.
This is good for publishers because native inventory is more defensible, it performs without relying on user tracking, it resists ad blockers, and it commands better rates from advertisers who care about attention quality rather than just impression volume. For advertisers, native placements in contextually relevant environments are a direct replacement for behavioral targeting that doesn’t require any user data at all.
Building a First-Party Data Engine
First-party data is information that is collected directly from your customers, with their consent and awareness. It is the data that they provide during the subscription process for your newsletter, after filling out a form with their preferences, or when they register an account on your website. This data is owned by you. No platform can take it away from you, and no browser update can make it obsolete.
For most companies, understanding this concept is not the problem, the real challenge is creating the mechanisms and motivation for collecting this kind of data in large quantities. People don’t easily give out their email addresses or other personal information unless there’s something in it for them. You need to offer an exchange for that.
There are several reasons people are willing to do this: for example, you might grant them access to exclusive content, reward them with loyalty points, present them with new products early, give them real-time usage data, or provide other sorts of valuable information. But when your approach is right and the customer clearly understands why sharing their personal data could benefit them, it’s an entirely different story.
The architecture behind first-party data collection matters too. A customer data platform (CDP) that unifies data from your site, your CRM, and your email platform gives you a single view of the customer that third-party cookies never actually provided. Cookies gave you fragmented behavioral signals. First-party data gives you identity.
Zero-Party Data: Let Customers Tell You What They Want
When customers sign up to receive your emails, and you tell them, “We’ll occasionally send you sales and new product announcements,” you’re tapping zero-party data. Similarly, when publishers ask paid subscribers, “What kind of content would you like to see more of?” that’s zero-party data too.
Given data privacy concerns and the new and improved regulations surrounding them, companies will have to rely more and more on zero-party data. Pepijn Rijvers, chief marketing officer at Booking.com, says, “Our industry is starting to forget what it’s like to have a personal relationship with customers, to ask them what they would like from you, and to build a relationship from there. That’s what we’ve done for centuries in the offline world. Online, we forgot to do that.”
Although many brands instinctively understand the value exchange that comes with zero-party data, they struggle to change longstanding methods of data collection. Marketers rely too heavily on third-party data and fail to recognize zero-party data as the key to creating personalized experiences in our ever-expanding privacy conscious world.
But implementing zero-party data collection tactics can be simpler than you might think. Zero-party data is essentially found in points in the customer journey where you give customers a choice and options, for example, do you want to receive updates about sales or new products? Or would you like to participate in monthly feedback surveys? Or would you like to take our short quiz to find out which style suits you best?
The Revival of Contextual Advertising
Contextual targeting has fallen into disuse because it seemed crude when compared to behavioral tracking: why target based on what someone is reading when you could target based on everything they’ve ever read? It turns out that behavioral targeting was only more precise in theory. In practice, as with any tech chasing the cutting edge, a lot of the specifics turned out to be more mirage than money.
But first, let’s define terms. The contextual targeting of old was pretty simple: didn’t your ad server have an “ad for running shoes” lined up for the moment an article about training tipped over into the danger zone of becoming an ad for lounging on the couch? Congratulations, contextually, you just became a reach block in Sunday afternoon’s NFL.
Obviously, that’s not what anyone is talking about today. In fact, when AI is used to perform contextual targeting at modern standards, the word often barely fits. Plugging into a publisher’s CMS, these tools read all of the pages before the first human pair of eyes ever lands on them. They aren’t looking for keywords, or matching content to predefined topic clusters. Instead, they’re analyzing sentiment and tone, imagery, even the layout of the page, to figure out what emotional state a reader might be in and what a piece of content is probably about.
Private Marketplaces and Direct Publisher Relationships
Open exchanges are good for reach. They’re relatively efficient ways for publishers to resell unused inventory and for advertisers to slim target audiences. The problem is that this all happens in a wild west-style open market where fraudsters, resellers, and poor-quality players also exploit that lack of friction.
PMPs remove some of that friction. They’re not going to make sense for every publisher or every campaign, but they do create cleaner markets where performance metrics are the shared currency between a publisher and advertiser. When they work well, the publisher has a new way to maximize revenue, and the advertiser gets to use smarter data to more tightly match their campaign strategy.
Programmatic guaranteed deals are an extreme version of this process. They’re almost as relationship-driven as a direct publisher insertion order. However, the back and forth is completed in software via a direct integration between the publisher’s inventory system and the advertiser’s campaign management platform.
Admittedly, the relative newness of this technology means they’re currently only practical for the largest global agencies and the biggest industry publishers, but this is the kind of tool that has some real staying power because both sides of the market benefit from the increased efficiency.
Alternative Identity Frameworks
For some advertising strategies on the open web, it’s necessary to have some type of user addressability. In such cases, deterministic identity solutions are filling the gap left by third-party cookies. These models create associations between users and their devices without inference, based on a specific identity attribute (that is the identity itself) which tends to be long-lasting across sessions and publishers.
Deterministic frameworks, such as Unified ID 2.0 (UID2), a project founded and facilitated by The Trade Desk; and LiveRamp’s RampID, invite users to log in on a publisher’s site, or consent to their registered email. That hashed and encrypted email can be shared with demanding platforms and other publishers to help them find this user across the myriad of websites and devices that they might use.
UID2’s identifier is encrypted, hashed, and rotated, meaning that even if a bad actor managed to use it for nefarious purposes, it would change before any damage could be done. RampID’s identifier is also similar to UID2: submitted through a direct or hashed email.
With either approach, when a user authenticates on a website that uses the identity partner, that user is instantly addressable on all websites that have activated the partner. These identifiers are shared in a decentralized way: individual publishers and advertisers access the identifiers separately.
Rethinking Measurement: From MTA to MMM
Multi-touch attribution was a flawed premise even before the cookie went away. The notion that you could accurately measure the influence of every touchpoint along a customer’s journey and then assign fractional credit to each was always suspect. The modeling and the data have a whiff of epistemic closure. Preordaining the solution, are we?
Media Mix Modeling (MMM), by contrast, is based on aggregate data, sales figures, spend by channel, assorted economic variables, and is used to estimate the contribution of all marketing channels over some recent period of time. No individual-level tracking is required. It’s a statistical model, not a surveillance model, and it’s been used by most of the world’s largest advertisers for several decades now, dating back to the pre-internet, pre-behavioral tracking era when we mostly imagined the web and tracked behavior with Nielsen panels and scanned diaries.
Integrating With Google’s Privacy Sandbox
For advertisers dependent on Chrome, Google’s Privacy Sandbox projects (specifically the Topics and Protected Audience APIs) serve as Chrome’s built-in tooling for interest-based advertising and remarketing without reliance on third-party cookies.
The Topics API assigns browsers to broad interest categories based on browsing history, without that history leaving the device. Advertisers can request topic signals when a user visits a participating site. The Protected Audience API handles remarketing by running auction logic inside the browser, keeping user lists local.
These aren’t the plug-and-play out-the-box crosshairs that cookies gave us. The categories are broader, the data less exact, and your DSP and ad tech stack have to integrate with the new standards Chrome is laying out. But if you’re running Chrome-based campaigns, working with these APIs now, getting your stack used to measuring and learning with these new targets, puts you a year ahead of very nearly every other brand. They’re going to wait for things to stop moving before they start anything.
The cookie-less web isn’t a temporary pain to adapt to a new normal for a bit. It’s the new normal. And the brands that use this as an alignment-check to build cleaner data relationships, put effort into contextual quality, and readjust an overreliance on bad metrics, are going to come out the other side with stronger advertising programs than they started with.
