Cookieless marketing Part 2: the rise of entity resolution
TL;DR
- A customer data platform collects, organizes, and activates customer data; identity resolution is the matching layer that links records, events, identifiers, and behaviors to the same person or account before those tools use the profile.
- Cookieless marketing makes identity harder because marketers work with partial, inconsistent, and first-party signals instead of one stable tracking identifier.
- Tilores resolves and assembles customer identity context at ingestion, so marketing, analytics, and customer systems can query current resolved context instead of repeating the matching work in every downstream tool.
Table of Contents
- Decision guide
- The tyranny of the Tag Manager
- The resolution will not be televised
- How inconsistent data can deliver consistent results
- CONCLUSION: entity resolution makes cookieless an opportunity
- Short answer
- How is identity resolution different from a CDP?
- Why cookieless marketing changes the identity problem
- Where Tilores fits in a marketing data stack
- What should marketers validate first?
- Frequently Asked Questions
Decision guide
| Question | Use Tilores when | Watch-outs |
|---|---|---|
| Do you need a CDP or identity resolution? | Customer, event, CRM, ecommerce, support, or analytics records need to be linked before a CDP or activation platform can rely on the profile. | A CDP may still be the right tool for campaign orchestration, audience sync, consent handling, and destination management. |
| What identifiers are available? | The available signals are partial or inconsistent, such as email variants, names, locations, devices, account fields, or server-side events. | Weak signals should be tested carefully because similar records can still refer to different people or households. |
| Where should matching happen? | Identity context should be resolved and assembled as data enters the entity layer, then reused by downstream systems when they query the customer. | Putting all matching logic in dashboards, campaigns, or warehouse reports can leave each team resolving the same customer separately. |
| How privacy-sensitive is the workflow? | First-party customer data needs to be connected with source references, controls, and enough lineage for internal review. | Consent, retention, data sharing, and privacy obligations depend on the organization's policy, systems, and jurisdiction. |
| How should scale be tested? | The marketing team needs identity context across growing customer data, not only a one-off cleanup of a small audience list. | Test match quality, latency, false positives, false negatives, and data-source coverage on the organization's own records. |
In Part 1 of this two-part series, you stepped back into cookie history … from their invention at Netscape in 1996 to when the EU’s GDPR legislation signed their death warrant in 2016. In Part 2, ahead, we’ll get past the problems – including those that persist in the new server-side data collection and tracking model – and focus more on solutions. Solutions you can adopt today that turn the diversity of data into an asset for your business.
(Which means Part 2 is more salesy, since one such solution is our product. We’re all about full disclosure here at Tilores.)
What you’ll discover is that connecting the different snippets of data users leave behind as they wander across the web needs more than just understanding data types and formats. It’s not just a change in technology – but a change in thinking.
The tyranny of the Tag Manager
Back in the Ye Olde Worlde of Cookies, tech-savvy marketers used TMSs – Tag Management Software – to pass data between their sites and the ad-serving technologies, analytics applications, and social media platforms they’d formed partnerships with.
The “tags” we’re talking about here are not HTML tags, or meta tags you write for search engines. A tag is a tiny chunk of code, often a single pixel “called” when an end user interacts with the website – enabling the site to keep track of that user as he or she journeys through the site and beyond. Those long strings of random characters that magically appear after a simple URL? They’re players in the tagging game.
For a while, tag managers were awesome. They gave marketing professionals a very useful web facility without requiring them to be coders; they’re incredibly versatile, handling tags of countless varieties from simple counters to bloodhound-like tags that follow users across devices. And as they developed, standards and conventions arose, so their community of marketing people could adopt a Tag Manager easily or switch to a different one without too much pain.
But to collect data, Tag Managers used methods that looked a lot like – guess what? Cookies. So they rose and fell on the same curve. But Tag Managers created a bigger issue: across the martech world, many people couldn’t imagine doing customer tracking with anything but tags. Tags had become the main, even the only, way to follow the breadcrumb trail of user behavior across pages and sites.
Cookies and tags had become accepted wisdom. So when browsers and laws started limiting data collected through them, marketers were all at sea, looking for alternative technologies that did the same thing. They were looking for evolution, when what they really needed was a revolution. Or rather, a resolution.
The resolution will not be televised
Consider two conceptual frameworks for building a data profile of an individual. We’ll dub the first “structured” and the second “unstructured”.
The structured view is that used in both cookie and tag manager paradigms. It looks at what links your different bits of tracking data, and expects those chunks of data to follow consistent, repeatable formats: an email address here, an IP address there, perhaps a Firstname-plus-Lastname or telephone number.
These are all good data points. And if you’ve got one common piece of uniquely identifiable data across all the datasets you’ve gathered – like an email address – it’s a pretty good basis for linking those datasets, and assuming they all relate to the same person. But for them to work smoothly, that key piece of data has to be what you expect, where you expect it. A partial email address, or a strangely formatted ZIP code, can fox it.
And the trouble is, you’ll write off a lot of data this way.
Because people can have more than one email; they may write their names in more than one way; the things they do on their owned devices may be completely different to the things they do on their work ones.
So the structured, consistent approach can lead to a partial profile that looks more complete than it is. It’s also a bit of a sledgehammer-to-kill-an-ant approach, since it needs a lot of computing crunch to scour endless gigabytes of records.
The second approach is less structured – and deliberately so. It builds on the structured approach by adding other datasets that aren’t normally used to build a detailed customer profile – using intelligent software to make educated guesses on how likely two datasets are to be related.
What kind of other datasets? Geographical data is one such, and an exceptionally useful one. Because knowing where an interaction takes place adds a rich backdrop of information to a customer touchpoint.
This alternative approach – expanding the universe of relevant data, even if that data tends to be messier and less structured – is the one we use at Tilores. It’s called entity resolution.
How inconsistent data can deliver consistent results
To understand entity resolution’s usefulness, think of the confusions in most tracking data. Your PC in the den contains a wealth of tracking information … but it’s largely valueless to marketers, because that data represents your family of five using it, each with very different habits.
Looking for consistent connections in that mass of data (like the way they share IP addresses) will certainly give you plenty of information … and it’ll almost certainly be useless.
Say the audience you’re building gains fourteen women named Jones. Traditional marketers might be overjoyed at increasing their prospect list by 14. But are there really that many individuals? You’ve got an E Jones. An E P Jones. An Ellen Jones. And a couple of Ellies. In reality, they’re all the same person.
The entity resolution approach – comparing that data against other data, in an intelligent way – can shake the real Ellen out of the mix.
Maybe eight of those entries share a geographical location. Or six of them visit the same website. Or two have the same birthday. By looking for what supports and what opposes the notion all 14 are the same person, the singular entity – Ms Ellen-Paige Jones of West Valley City, UT – can be resolved with a high degree of certainty.
Yes, using inconsistent, “unstructured” approach means more nuances. Not every question has a strict right/wrong or yes/no answer. There are shades of gray. But if you take the right approach, those shades of gray can be critically assessed for their level of confidence
This is entity resolution. And it’s ideally suited for cookieless marketing. Because with those datasets moving onto the web, entity resolution provides an ideal way to sift and sort the data that isn’t computationally expensive.
(As the size of the dataset increases, entity resolution methods require a linear rise in processing power – not an exponential one. Which means resolving an entity takes the same effort each time. Which means you can do it in real time, a huge advantage by itself.)
That’s why we believe at Tilores that entity resolution – rather than an evolution of Tag Management Software – is the best bet for large-scale marketers seeking to make sense of ambiguous tracking data.
Entity resolution lets you build high-certainty, real-time, self-cleaning data profiles about your customers. And the move of tracking data to server-side, where it can be compared and contrasted with other datasets, is a huge opportunity to get to know them even better.
CONCLUSION: entity resolution makes cookieless an opportunity
The web has undergone countless paradigm shifts in its short history. We believe the move to server-side tracking data is another one – but not enough marketers are doing it right yet.
Cookieless tracking data is a near-perfect scenario for entity resolution – and because it scales proportionally, it maintains performance and cost-effectiveness even as your customer list grows from thousands to millions.
If that sounds of interest to you, come talk to us.
Short answer
A customer data platform is the system that helps a marketing team collect, segment, activate, and measure customer data. Identity resolution is the capability that decides whether several records, events, or identifiers belong to the same customer or account.
The original article argues that cookieless marketing needs a change in thinking, not only a replacement for tags and cookies. Entity resolution supplies that change by comparing inconsistent signals, weighing supporting and opposing evidence, and creating a resolved customer context that other systems can use.
How is identity resolution different from a CDP?
A CDP is usually evaluated by how well it collects customer data, builds audiences, syncs destinations, and supports marketing activation. Identity resolution is evaluated by how accurately it links messy records and keeps enough source context for teams to trust the result.
That distinction matters because a CDP can only activate the customer profile it receives. If the same person appears under several emails, devices, names, or accounts, the identity layer determines whether the profile is useful before campaign logic begins.
Why cookieless marketing changes the identity problem
The source article frames the shift away from cookie and tag-manager assumptions as a move from consistent identifiers toward messier customer evidence. In that environment, the challenge is not only collecting more data, but deciding which data points belong together.
Entity resolution fits that problem because it can compare structured and less structured signals, assess supporting evidence, and avoid treating every exact field mismatch as a reason to discard useful context.
Where Tilores fits in a marketing data stack
Tilores fits as the entity-resolution layer beside existing marketing, CRM, analytics, warehouse, or CDP systems. It resolves and assembles customer context at ingestion, then lets downstream systems query the resolved context they need.
That gives teams a cleaner identity base without asking every destination system to implement its own matching logic. It also keeps the decision about campaign activation separate from the technical work of linking records.
What should marketers validate first?
A useful evaluation should include records that are easy to over-merge and records that are easy to miss: families sharing devices, duplicate emails, multiple work and personal addresses, changed names, and similar customers who should remain separate.
The goal is to prove that the identity layer improves customer context for the real marketing workflow while keeping source lineage, review paths, and privacy controls visible to the organization.
Frequently Asked Questions
- What is the difference between a customer data platform and identity resolution?
- A customer data platform collects and activates customer data for marketing and customer workflows. Identity resolution links records, identifiers, and events that belong to the same customer or account so those tools can work from a cleaner profile.
- Does identity resolution replace a CDP?
- Usually no. Identity resolution improves the customer context that a CDP, CRM, analytics tool, or activation platform can use. The CDP still handles audience management, destinations, campaign operations, and related marketing workflows.
- How does entity resolution help cookieless marketing?
- It helps marketers connect first-party and server-side data when one stable tracking identifier is unavailable. Instead of depending only on exact matches, entity resolution compares multiple signals and builds a resolved customer context.
- Should identity resolution happen at ingestion or query time?
- For Tilores terminology, resolution and assembly happen at ingestion. At query time, marketing, analytics, or customer systems retrieve and use the resolved context rather than doing the matching from scratch.
- What data should marketers test before adopting identity resolution?
- They should test real first-party and server-side records, including email variants, name variations, location signals, device or account fields, duplicate contacts, household ambiguity, and records that should remain separate.
Evaluate Tilores on your own data
Use the next step that matches your evaluation stage.
See what resolved entity data does for your business — and your AI.