The Third-Party Cookie Is Nearly Gone, But Remarketing Isn’t
Chrome is gradually closing the door on third-party cookies, and Safari and Firefox have been blocking them by default for several years. Many marketing managers interpreted this as “the end of remarketing.” That is not the correct conclusion: remarketing hasn’t disappeared, but the infrastructure it relied upon has fundamentally changed.
Those who continue to run campaigns on old lists built on cookies are seeing increasingly shrinking coverage, because a growing share of web users is simply no longer visible. The report still shows data, but it is based on less and less of the actual audience, and this decline is gradual enough that advertisers might not notice it in time. This is especially evident in legacy campaigns that have been running on the same list for months.
There’s no need to look for ways to “bypass” the block. You need to build remarketing that doesn’t depend on it in the first place. Businesses that understood this early on have already built an alternative data infrastructure, and those still waiting for a “technical fix” are missing their window to prepare.
First-Party Data Is the New Foundation
First-party data is information that a business collects directly from its customers: mailing lists, registered users, app users. It does not rely on cookies and does not disappear with a browser update; it is also more accurate because it comes directly from the customer rather than from statistical inferences about browsing behavior.
A business that does not collect it systematically enters the post-cookie world without tools. The first question to ask is not “which remarketing tool should I run?” but rather “do I even have a sufficiently large and up-to-date list of customers and interested prospects to build upon?”
In practice, this means reviewing every touchpoint with a potential customer: Are they signing up for a newsletter? Is an email collected on the contact form? Are existing customers invited to join a club/loyalty program? Each of these channels feeds into the same data infrastructure, and in many businesses, several of them already exist—they are just not connected to a single, organized advertising system.
A business looking to start today can map out what it already has within a week and connect it to advertising platforms. It often turns out that the data is already being collected somewhere, but no one is leveraging it for remarketing. Such mapping also reveals redundancies: the same customer registered in two systems that don’t communicate with each other.
Customer Match and Customer Lists as a Genuine Alternative
Google, Meta, and LinkedIn allow you to upload an encrypted customer list and build a direct remarketing audience from it, independent of cookies. Identification relies on details provided by the customer themselves—email or phone number—rather than browser tracking that can be blocked at any moment.
From this list, you can also build lookalike audiences—new audiences that are statistically similar to existing customers. This is the most powerful tool remaining in the post-cookie world, as it does not depend on individual cookie consent from every user.
The only condition is list quality. An old, outdated, or overly small list will not produce a good lookalike audience because the platform does not receive enough data points to learn from. Regularly updating the list is part of ongoing maintenance, not a one-time setup task. An updated list also shrinks, as anyone who unsubscribes is removed from it.
It is also advisable to segment customer types within the list. A one-time buyer differs from a repeat customer, and someone who only requested a quote differs from both. Such segmentation produces more focused lookalike audiences, rather than a single large list that lumps together entirely different levels of intent.
Contextual Is Back, in a Smarter Version
Contextual advertising—displaying ads based on the content of the page a user is browsing rather than who they are—is returning to the forefront after years of being considered outdated. The new version is far smarter than the “simple context” of the past: it analyzes topic, sentiment, and page intent with much greater precision, rather than just isolated keywords.
This does not replace data-driven remarketing, but it adds a layer that reaches new users about whom there is no existing data yet. Combining both approaches covers more ground than either alone and extends reach to audiences that haven’t yet joined the customer list.
Measurement When You Can’t Track One-to-One
Modeled conversions are the platforms’ answer to the reporting gap caused by blocked cookies. Instead of measuring every conversion individually, the system statistically estimates how many conversions occurred based on patterns in the data that is available, filling the gap with a model-based estimate. The interface only partially indicates this, making it easy to forget that part of the reported number is estimated rather than measured.
These numbers are less precise than legacy tracking, meaning they represent a trend rather than an absolute figure. Anyone comparing an isolated metric from period to period without realizing it is modeled risks making budget decisions based on statistical noise rather than genuine performance shifts.
It is better to look at trend shifts across several consecutive campaign cycles rather than react to a single fluctuation. A consistent change over time is much more meaningful than a one-time spike that may simply be noise in the data.
What This Means in Practice for Your Remarketing Budget
Our recommendation to clients in this era: shift part of the budget previously allocated to broad remarketing toward building first-party data—namely, quality lead generation and updated lists. A remarketing budget without a strong customer list will always start with partial coverage, no matter how much money is spent on it. It’s a shift in the media mix, not a budget cut.
We measure all of this against a cost-per-registered-user methodology: not how many impressions the remarketing audience received, but how many of them actually converted into registered users. A business that only looks at click-through rates in a remarketing campaign risks missing that it is driving return traffic without an end business result.
This is how you run remarketing on Facebook and Instagram that keeps working even when the cookie disappears: grounded in data the business controls, rather than a product decision by one browser or another.