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Identity resolution is the process of connecting fragmented data signals from different channels, devices, and touchpoints into a single, accurate profile of one real customer.
It is the foundation for personalised marketing, accurate measurement, and effective suppression in a world where most consumer journeys span multiple devices, browsers, and environments.
For EMEA retailers and brand advertisers, this is no longer a technical nice-to-have. With third-party tracking in structural decline and GDPR raising the bar on how customer data must be collected and used, identity resolution has become the infrastructure on which compliant, effective digital retail marketing is built.
TL;DR Identity resolution unifies fragmented customer signals across devices, channels, and in-store touchpoints into one accurate, persistent profile. This eliminates duplicate profiles, enabling smarter activation strategies across channels including CTV, audio and display. Name and address anchored identity resolution, as used in Epsilon COREid, consistently achieves higher match rates, better channel reach, and more reliable attribution.
The short answer: identity resolution works by ingesting multiple data signals and matches them to determine which signals belong to the same real person.
What underpins the accuracy of any identity resolution platform is the quality of its core matching data.
Email is among the most common anchors used across the industry, but it is also one of the weakest. Customers use multiple email addresses, share family accounts, and change addresses over time. Platforms that anchor on email alone consistently under-match audiences and accumulate duplicate profiles, creating the very fragmentation email-first solutions set out to resolve.
Name and postal address data provides a more durable anchor. It changes far less frequently, is more consistent across channels, and is uniquely effective at capturing the in-store and offline purchase behaviour that email-only approaches miss entirely.
The output is a persistent identity: a single, pseudonymised view of a real person that travels with them across every interaction, regardless of device, channel, or session.
Find out more about Epsilon’s industry leading identity resolution solution, Epsilon CORE Identity.
The nuance is this deterministic data matching only works where strong first-party data exists. If someone is not logged in for example, many systems simply fail to match. Most serious identity strategies now start with deterministic data as the backbone and layer probabilistic methods - signals like device attributes, browser behaviour, location patterns, or network data - on top to extend reach and insight.
This is what makes it possible to reach the same individual with a relevant message whether they are browsing a retailer's app, watching a video ad on CTV, or walking into a physical store.
The short answer: an identity graph is the mechanism through which identity resolution operates at scale. It is a living record of all known identifiers for each individual, spanning both online devices and browsing to in-store purchases and loyalty use.
Rather than resolving identities on a transaction-by-transaction basis, an identity graph maintains a persistent record of the connections between device IDs, email addresses, loyalty card numbers, hashed identifiers, and postal addresses.
Think of it as a continuously updated network where each connection represents a verified or inferred link between an identifier and a single real person. When that network is built on verified offline data as well as digital signals, it extends identity resolution into territory that purely digital approaches cannot reach.
For a grocery retailer, this means connecting a customer's in-store loyalty card scan to their mobile browsing session, their click on a sponsored product listing, and their desktop checkout. Without an identity graph, each of those touchpoints looks like a different person. With one, they resolve into a single profile with a complete picture of purchase behaviour, channel preferences, and the full path from ad exposure to transaction.
The quality of an identity graph is determined by three things: the breadth of data signals it ingests, the accuracy of its matching methodology, and how regularly it is refreshed. Stale graphs generate false positives, over-messaging customers who have already purchased and spending media budget on audiences who have lapsed beyond the point of re-engagement.
The short answer: most of your audience is already invisible to traditional tracking, and the gap is growing. Identity resolution is how you reconnect the dots.
For years, marketers relied on third-party cookies to recognise customers across the web. That approach is failing. According to Comscore's 2025 State of Programmatic report, more than half of all mobile impressions (54%) and over a third of desktop impressions (36%) no longer carry any user identifier at all, including alternative IDs. That is not a future problem. It is the current reality.
The causes are multiple and compounding. Apple's App Tracking Transparency framework saw around 96% of iOS users decline tracking at the app level. Safari and Firefox have blocked third-party cookies for years. Chrome, the largest browser by market share, has moved away from its own cookie deprecation timeline, but the broader shift toward privacy-first browsing is irreversible. GDPR and the UK Data Protection Act reinforce it further.
The short answer: not really, no. With cookies, someone who browsed your website, clicked an ad on a tablet, then completed a purchase in-store would appear as three separate, unconnected people in your data.
Most legacy advertising infrastructure was built around devices and browsers rather than people. A cookie assigned a unique identifier to a browser session and followed that session across websites. When a customer switched devices, cleared their cookies, or browsed in a private window, the link was broken. The same person appeared as multiple different users.
Device-based identity approaches, including those built on email hashes and third-party digital IDs, inherit the same fundamental limitation: they match identifiers, not individuals. Matching requires an exact identifier in common across two data points. Any gap in that identifier chain produces a broken profile. Two interactions that cannot be linked by a shared digital ID are treated as two separate people, even if they belong to the same individual.
Person-based identity resolution works differently. Instead of requiring exact identifier matches, it uses AI and probabilistic modelling to evaluate multiple signals together and build a holistic, unified view of each individual. It fills in data gaps rather than abandoning profiles where those gaps exist. The result is a more complete, more accurate, and more dynamic picture of your customers, including the ones who do not leave a consistent digital footprint.
This distinction matters most in retail and digital media. A retail media network built on person-based identity can reach its full customer base, including in-store shoppers, infrequent digital visitors, and shared-device households. One built on device-based identity reaches only the fraction of customers who interact consistently through a single, trackable digital channel.
The short answer: for retail media and loyalty to work effectively, a retailer must resolve identities across online shopping behaviour, in-store data and loyalty.
The link between identity resolution and retail media is structural.
European retail media advertising reached €13.7 billion in 2024, growing 21.1% year-on-year according to IAB Europe, while the broader European digital ad market grew at around 6% over the same period. In the UK, retail media spend is, growing at an average annual rate of 17% through to 2030 according to IAB UK.
This growth is being driven by first-party purchase data: the verified, transaction-level audience signals that retailers hold. But that data is only as valuable as the identity infrastructure that activates it.
Consider what a retail media proposition actually requires to deliver on its promise. A brand advertiser wants to reach pet food buyers who have not purchased in 90 days, across both the retailer's owned website and offsite programmatic display. To build and activate that audience, the retailer must resolve identities across in-store loyalty data, online browsing behaviour, and email engagement, then push that resolved audience into a DSP for offsite activation, and close the measurement loop back to actual sales.
At every stage of that workflow, identity resolution is the enabling layer. Without it, the audience is incomplete, the targeting is imprecise, and the measurement cannot be connected to real outcomes.
Loyalty programmes compound this further. They generate some of the richest first-party data in retail, but only when the identities within them are resolved accurately. A loyalty card linked to one email address but not to a customer's in-store transactions, mobile sessions, or household profile is a partial view. Identity resolution closes that loop, connecting loyalty programme membership to the full breadth of a customer's commercial behaviour and making that data actionable across every retail media format: sponsored listings, display, offsite programmatic, CTV, and video.
Example
A major UK grocery retailer’s retail media network could reach approximately 40% of active loyalty members through digital channels. After resolving in-store transaction history against verified name and address data, digital reach extended to over 75% of the same customer base, capturing a large proportion of shoppers whose purchase behaviour had previously been invisible to the digital targeting stack. Suppression accuracy improved substantially too: customers who had already purchased a promoted product stopped receiving that promotion within 24 hours rather than continuing to be served irrelevant ads for days afterwards.
Epsilon's COREid is built on verified name and address data, not email alone. This foundational difference is what allows it to resolve identities across the full breadth of a retail customer base, including in-store shoppers, infrequent digital visitors, and shared-device households that email-only platforms consistently miss.
COREid uses AI-driven probabilistic matching to connect disparate data points into a single pseudonymised view of each individual. It fills data gaps rather than discarding incomplete profiles, which means resolved audience sizes are larger, more accurate, and representative of real purchasing behaviour rather than digital behaviour alone.
Because COREid underpins every Epsilon product, a resolved identity is not a static data asset. It powers personalisation and performance across every channel: display, video, CTV, and digital audio. It connects ad exposure to verified sales data both online and in-store, offering true closed-loop attribution, so the business impact of every impression is measurable against real transactions.
This is what makes COREid the identity layer for retail media specifically. When a retailer's first-party loyalty data is resolved through COREid, that audience becomes activatable across onsite sponsored placements, offsite programmatic, and CTV simultaneously, with measurement that closes the loop back to the till. The same identity infrastructure that drives retail media performance also powers loyalty programme activation, personalisation, and customer acquisition, all from a single, stable, GDPR-compliant foundation.
Epsilon was the only identity solution to capture all nine of the potential identifiers outlined in Digiday's guide to ID alternatives for publishers, reflecting the breadth of signal coverage that underpins COREid's match rates and reach across EMEA markets.
