Identity graph companies: How to choose the right provider for your brand

Think about how air traffic control works. A single flight shows up as different blips on different radar systems as it crosses regions, and none of those systems know on their own that they're all looking at the same plane. It's only when a controller connects those signals into one flight path that the picture makes sense.

That's basically the job an identity graph does with your customer data. A shopper might show up as an email address in your CRM, a device ID in your ad platform, and an anonymous cookie on your site, and without an identity graph stitching those signals together, you're managing three strangers instead of one customer.

Brands without a working identity graph can end up targeting the same shopper three times across three channels, wasting ad spend that never needed to happen, and reporting on "customers" who are really just fragments of one individual. But before you bring on an identity graph provider, it's worth understanding what these companies actually do, how their identity resolution methods differ, and where even a strong identity graph falls short.

Key takeaways

• An identity graph links fragmented identifiers, like emails, device IDs, and cookies, into one unified customer profile.
• Identity resolution relies on a mix of deterministic matching (exact, verified links) and probabilistic matching (statistical best guesses).
• Major identity graph companies include Claritas, Experian, Amperity, and Acxiom, each with different strengths in coverage and data types.
• The right identity graph depends on your data infrastructure, compliance needs, and how much first party data you already have.
• Identity resolution is getting harder to maintain as cookies disappear and privacy regulations tighten.
• A strong identity graph helps with targeting and personalization, but it won't tell you whether your marketing spend is actually driving revenue.
• Marketing mix modeling (MMM) fills that gap by measuring what happened to revenue, independent of whether every customer touchpoint was ever fully resolved.

What an identity graph actually does

An identity graph is a data structure that connects the different identifiers tied to one person into a single, unified customer profile. Instead of treating consumer data like a work email, a phone number, and a mobile device as three separate contacts, an identity graph recognizes them as the same individual and links them together into one connected record built from dozens of underlying data points, each one a small clue pointing back to the same shopper.

Brands use identity graphs alongside advanced analytics to personalize customer experiences across channels, build more accurate target audiences for paid media, catch fraud, and feed customer intelligence into broader business decisions. The better the identity graph, the more reliably a brand can turn a connected customer view into one coherent person rather than a handful of disconnected touchpoints scattered across different systems.

Deterministic vs. probabilistic matching

Before comparing identity graph providers, it helps to understand how they actually build these connections, since this is where most of the accuracy differences between providers come from.

Deterministic matching links identifiers using exact, verified data points, like a login-authenticated email address matched across two platforms, leaving little room for guesswork. This kind of identity matching can be highly accurate, but it only works when a provider has that verified overlap in the first place.

Probabilistic matching takes a different approach to identity resolution, using statistical models to estimate that two identifiers, like a device and an IP address, likely belong to the same person based on patterns in behavior, timing, and location. It can extend an identity graph's reach into gaps deterministic data can't cover, but it comes with a margin of error that grows as the underlying signals get weaker.

Most identity solutions on the market use a blend of both approaches, and the strongest identity solutions are upfront about which one is doing the heavier lifting. A few things worth knowing about how that blend plays out:

• Identity graphs leaning heavily deterministic tend to be smaller but more reliable.
• Identity graphs leaning probabilistic can offer broader coverage across devices and channels, with somewhat lower certainty per match.
• The right ratio depends on your use case. Fraud prevention usually calls for deterministic confidence, while broad audience targeting can tolerate more probabilistic estimation.

Notable identity graph providers

The identity graph space includes a few enterprise-scale players and a longer tail of more specialized identity resolution tools. Here's a quick look at some of the more established names.

• Claritas Identity Graph: Tracks devices across the large majority of US households, combining deterministic and probabilistic matching for broad reach.
• Experian: Runs one of the larger identity resolution operations on the market, linking hundreds of millions of consumers and billions of devices, often used for fraud prevention and risk management alongside marketing.
• Amperity: A customer data platform-style tool that unifies offline data, digital interactions, and CRM files, built to sit inside existing cloud infrastructure like Databricks and Snowflake.
• Acxiom: One of the longer-standing names in identity resolution, helping brands build a first party identity graph and sync real-time signals with offline profiles.

For brands with more specialized needs, like B2B identity mapping or location-based resolution, smaller identity solutions focused on niche use cases can be a better fit than the enterprise names above.

How to evaluate an identity graph provider

Picking a provider for your customer identity graph isn't just about who has the biggest database. A few practical questions can help data teams narrow the field faster than a features list ever will.

How much of their identity resolution is deterministic versus probabilistic? Ask for real numbers here, not just marketing language about "accurate identity resolution." An identity graph company that's transparent about its match rates and the confidence behind them is easier to trust than one that isn't.

Does the identity graph integrate with your existing stack? Whether your data teams are working out of traditional databases or a modern data warehouse, the identity graph should fit into where they already work rather than forcing a new structure on top of it.

How is first party data handled? Providers vary in how much their identity graph relies on your own owned data versus third party data they've licensed or aggregated. Owned data tends to be more reliable and comes with fewer compliance risks, so it's worth understanding the split before you sign a contract.

What's the refresh rate? Customer identity changes constantly. New devices, new emails, and new addresses show up daily, adding fresh data points that an identity graph refreshed only once a month will fail to capture in time.

How does the provider handle compliance? Ask directly how the identity graph manages consumer opt-outs, data retention, and regional privacy rules. This matters more every year, which brings us to the next section.

Where identity graphs fall short

Identity graph providers solve a real problem marketers face, but even the best identity resolution setup has limits, and most of the marketing around this category glosses over them.

• Walled gardens don't share. Amazon, Meta, and TikTok all maintain their own internal identity systems and don't pass raw identity data to outside providers or their identity graph databases. That means even the best third party identity graph has visibility gaps around some of the biggest platforms in your media mix and your attempts at cross channel identity resolution may never be complete.
• Cookie changes keep shrinking probabilistic accuracy. As mobile apps and browsers restrict tracking, some of the signals probabilistic identity resolution relies on are disappearing, which makes any identity graph built heavily on that method less reliable over time.
• Compliance risk grows with data breadth. The more disparate data sources an identity graph pulls from, especially third party data, the more exposure a brand takes on under regulations like GDPR and CCPA.
• A resolved identity isn't the same as a measured outcome. Even a perfectly connected customer journey only tells you who someone is and what they did. It doesn't tell you whether a specific campaign is what actually drove them to buy, or whether that spend would have been better used somewhere else.

That last point is the gap between having a great identity graph and actually knowing your marketing efficiency.

Where identity graphs fit into your measurement stack

Identity graphs and marketing measurement solve two different problems, and mixing them up is a common mistake. An identity graph helps you know who your customer is across channels and devices so you can target and personalize more precisely. It doesn't tell you what would have happened if you'd spent your budget differently, or how much revenue a given campaign actually generated once you account for the ripple effects across your other channels.

That distinction gets more important as brands sell across more platforms. A shopper's connected customer journey might span your website, a mobile app, and an Amazon storefront, but a resolved identity graph doesn't automatically tell you which piece of your marketing mix deserves credit for the sale. That's a measurement question, not an identity resolution question, and it takes a different kind of model to answer it.

Where Prescient comes in

Building a strong identity graph is a smart investment, but it answers a different question than the one most marketing teams are actually stuck on: is this spend working, and where should the next dollar go? An identity graph can tell you that the same person interacted with your brand across five touchpoints, but it can't tell you whether cutting a campaign that looks weak in isolation would actually cost you revenue somewhere else (like your Amazon storefront).

That's where Prescient's marketing mix modeling comes in. Instead of relying on the identifiers an identity graph is built on, which erode as privacy rules tighten and platforms lock down their data, Prescient measures the actual revenue impact of every campaign using statistical modeling built on your historical performance, including the halo effects that spill over into branded search, organic and direct traffic, and Amazon sales. You get a complete picture of marketing efficiency that holds up regardless of what happens to cookies, device IDs, or walled garden access next, no identity graph required. See how it works and what insights the platform can reveal when you book a demo with our team.

FAQs

What's the difference between an identity graph and a customer data platform (CDP)?

An identity graph is focused specifically on resolving and connecting identifiers into unified customer profiles. A CDP is a broader platform that collects, stores, and activates customer data, often using an identity graph as one component underneath it. Think of the identity resolution graph as the matching engine and the CDP as the larger system built on top of it.

Do identity graphs still work now that third party cookies are going away?

Yes, but their accuracy shifts. Deterministic matching, which relies on things like logins and verified email addresses, isn't affected by cookie changes. Probabilistic matching takes more of a hit, since identity resolution often leans on the same browser and device signals that privacy changes are restricting. Identity graph providers with strong first party data sources tend to hold up better through this transition.

How do identity graphs handle privacy regulations like GDPR and CCPA?

Reputable identity graph providers build consent management, opt-out handling, and data retention limits directly into how their identity resolution operates. That said, compliance responsibility doesn't fully transfer to the vendor. Brands still need to understand where a provider's identity data comes from and make sure their own data collection practices meet the same standards.

Are identity graphs worth it for small and mid-size brands, or just enterprise?

Smaller brands can benefit from identity resolution, but the math is different. Enterprise identity graph providers often price and structure their tools for high data volume, so a mid-size brand may get more value from a lighter-weight identity matching tool or a CDP with built-in identity resolution rather than a standalone enterprise identity graph.