What Is Identity Matching and How Does It Work?

Identity matching has played a major role in digital advertising for several years. Historically, users have been identified based on cookie IDs, device IDs, and other parameters, data points that help gather audience information and deliver targeted ads across the open web.

However, tracking and identifying users has become increasingly difficult. Browsers like Safari and Firefox have restricted third-party cookies, and data privacy laws have added further constraints. While Google announced plans to phase out third-party cookies in Chrome, it reversed course in 2024, opting instead to retain support with enhanced user controls and Privacy Sandbox alternatives.

The broader industry has continued shifting toward privacy-safe, first-party, and identity-driven solutions, making identity matching one of the most critical challenges in digital advertising. As advertisers seek a more complete view of their audiences, the ad-tech industry has developed identity matching - a set of solutions that help publishers and advertisers differentiate one user from another and track behavior across multiple devices. The goal is to achieve accurate, omnichannel targeting amid growing fragmentation.

But before diving into the methodologies, it's worth understanding why identity matching is needed in the first place.

The Need for Identity Matching

Publishers and advertisers use personalization to make advertising more relevant. The challenge, however, is that users often switch between devices and platforms throughout their path to purchase.

For example, a user might first encounter a brand's product through a display or video ad on a website, then later see an ad from the same brand while browsing a social media app, and finally visit the brand's website directly to make a purchase. This fragmented journey makes it difficult for publishers and advertisers to accurately identify and track the same user across touchpoints.

IP address changes add another layer of complexity. Multiple users sharing a single WiFi network, such as colleagues in an office, may appear as one user, while the same individual connecting via mobile data or a personal network later will register a different IP entirely. This makes identifying individual visitors a primary concern for both publishers and advertisers.

Identity matching addresses these challenges by enabling accurate, omnichannel audience reach. Without it, personalized ad serving breaks down. Cookies can be deleted, CTV devices lack unique identifiers, and fragmented data reduces the effectiveness of targeted advertising. While third-party cookies persist in Chrome with user-controlled settings, their effective reach has declined significantly due to restrictive defaults in other browsers, rising opt-outs, and regulatory scrutiny — making robust identity matching essential for reliable attribution and personalization.

In short, identity matching is what makes it possible to deliver the right ad to the right person, regardless of where or how they're browsing. Understanding why it's needed is the first step. It's worth exploring how it actually works.

How Is Identity Matching Done?

Identity matching can be provided by data management platforms (DMPs), customer data platforms (CDPs), or other data aggregators. These platforms create data points to identify the same user across different channels, locations, and devices. Generally, these data points fall into three categories:

1. Device identity points
   cover IP addresses and other identifying information tied to a user's device. 
2. Digital identity points
   include email addresses, social network profiles, website registrations, and similar online identifiers. 
3. Terrestrial identity points
   encompass physical details such as home address, work address, and phone number.

The process of identifying a user typically follows three steps.
1. The relevant platforms (websites, social media, etc.), channels (eCommerce apps, in-store, etc.), and devices used throughout a user's journey are identified and connected.
2. The individual user is matched to each device, platform, or channel based on shared attributes.
3. The data is validated to confirm it belongs to the same user across all touchpoints.

This is the foundation of how identity matching works. In practice, it is carried out through two primary methods: deterministic matching and probabilistic matching.

This is a comprehensive overview of identity matching. In essence, identity matching can be done by two methods: 

1. Deterministic Matching
2. Probabilistic Matching.

What Is Deterministic Matching?

Deterministic matching, or explicit matching, is a method to find the exact match between two data sets to identify the same user across different channels and devices. Users are matched based on the following identifiers:

• Email address,
• Phone number,
• Log-in details (user name, address, date of birth, etc.)

Usually, the publisher already possesses these details (first-party data) as they collect them when users sign up for newsletters, subscriptions, or any other service. So, a match is only confirmed when the user’s data matches. 

Deterministic matching has higher accuracy and hence, improves the user experience. A user doesn’t have to view irrelevant ads or offers at any point in time. But, deterministic matching has a drawback for publishers who don’t store email addresses and other basic user information.

For this reason, many publishers have started collecting deterministic data to improve their targeted advertising by encouraging visitors to share their email addresses. Think, freewalls. You can register an account with your favorite website for free so that they can deliver a personalized experience while you are on the site. 

One example of a deterministic identifier is Google ID. Google generates Google ID when you create a Google account. Google uses its ID to identify users and personalize the ad experience across its properties and partnered third-party sites.

What Is Probabilistic Matching?

Probabilistic matching, also known as implicit matching, compares multiple data points to identify the same user across devices, channels, and platforms. Data aggregators and identity solution providers typically rely on a knowledge database and predictive algorithms to power this process.

In probabilistic matching, devices are linked by looking at the following data points:

• IP addresses, Wi-Fi networks,
• Device fingerprinting,
• Screen resolution,
• Operating system, and so on.

By combining these data points, probabilistic matching uses statistical likelihood to infer that different devices belong to the same user.

To illustrate how this works, consider an example from LiveRamp. Suppose a phone and a desktop connected to a household Wi-Fi are observed logging on throughout the day, every day of the week. A separate device belonging to a friend, however, only connects on weekends. An algorithm can use this pattern, alongside other signals, to infer that the friend's device does not belong to the same household.

Probabilistic matching can deliver useful scale and reasonable accuracy, particularly when built on a deterministic foundation. Its drawbacks, however, are well-documented, it is less precise than deterministic methods and offers limited transparency in how users are identified.

These limitations have grown more significant in recent years. Privacy regulations in many regions now classify signals like IP addresses as personal data, and device fingerprinting faces increasing scrutiny. Rising user opt-outs have further eroded its reliability. While probabilistic matching remains in use, especially for extending reach at scale, many industry experts are advocating for a shift toward higher-accuracy, deterministic signals for better attribution, compliance, and performance.

Most providers today blend both approaches, using probabilistic methods for reach and deterministic ones for precision.

To navigate these constraints, identity solutions such as UID2.0 and Google's Ads Data Hub offer more privacy-compliant alternatives, operating within consent frameworks shaped by regulations like GDPR and, more recently, India's updated data protection rules.

Sidenote: DoubleClick ID uses probabilistic signals; with Chrome's cookie retention, hybrid first-party + probabilistic graphs (e.g., LiveRamp, The Trade Desk UID2) enhance AdX and GAM personalization

Deterministic Matching Vs. Probabilistic Matching

It’s difficult to say which is the better option as it depends on the publishers’ and advertisers’ requirements. In some cases, only probabilistic matching would work. In other cases, you may be required to combine both to match the users better. 

In general, it is recommended to get started with a first-party data strategy and see if you can collect deterministic data points (for instance, email addresses) if the user has already shown interest. Many publishers are leveraging a spike in traffic to establish a strong relationship with visitors and convert them into ‘known’ readers.

Embrace hybrid identity graphs that layer deterministic matches (e.g., via universal IDs like UID2, RampID) on probabilistic signals for optimal accuracy and scale. In 2026, the industry increasingly favors deterministic as the foundation to navigate privacy constraints and deliver measurable outcomes.

You must use probabilistic matching as long as it exists, as advertisers typically want to extend their reach. They can’t match with just tens of thousands of users on the open and run programmatic campaigns. If you run a direct deal, you can sync first-party data with the advertiser to improve conversion rates, but a generic open auction requires probabilistic IDs.

Final Thoughts

Identity matching is a critical aspect of advertising for publishers, as it allows them to increase ad revenue and improve targeting and relevance. However, before choosing an identity-matching methodology, you should carefully consider data-specific factors such as the source of data collection, data quality, timelines, and accuracy.
In 2026, integrate consent management (CMP), data minimization, and tools like Google's Ads Data Hub to navigate enhanced regs while boosting revenue. 

Additionally, you should analyze first-party data and work closely with data aggregators to better understand the audience data. Doing so allows you to use identity matching responsibly without violating privacy laws to improve ad performance and revenue.