Identity Graph

What is an identity graph?

An identity graph (also called an ID graph) is a database that links multiple identifiers associated with a single consumer — email addresses, device IDs, phone numbers, browser cookies, loyalty IDs, and more — into a unified consumer profile. Identity graphs allow advertisers and publishers to recognize and address the same consumer across different devices, channels, and contexts, enabling consistent targeting and measurement across the customer journey.

How does an identity graph work?

Identity graphs ingest and match data from various sources—both online and offline—to create a single profile at the individual or household level. For example, Fluent’s identity graph uses 200M+ first-party profiles and enhances them with Experian’s identity data (spanning 2.75B IDs and 250M individuals), combining behavioral, transactional, and intent signals. AI and machine learning then power segmentation, targeting, and personalization in real-time.

Types of identity graphs:

Identity graphs vary in how they resolve and link customer data.

• Deterministic: Based on verified, first-party data (e.g., login, purchase).
• Probabilistic: Uses statistical models to infer identity connections.
• Hybrid: Combines deterministic accuracy with probabilistic scale—like Fluent’s integration with Experian—for broader identity resolution.

How to measure an identity graph:

The effectiveness of an identity graph is measured by how accurately and comprehensively it can identify, connect, and activate customer data.

• Match Rate: The percentage of known users that can be linked to an identity graph.
• Coverage: Number of unique profiles or households identified.
• Accuracy: Precision in linking data to the correct person or device.
• Performance Lift: Improvement in metrics like ROAS, CTR, or conversions from targeted campaigns.
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Why is an identity graph important to marketers?

Identity graphs power personalization at scale—helping marketers understand who their customers are, what they want, and how to engage them. For commerce media, this means better targeting, increased ad relevance, and stronger revenue impact. Fluent’s graph enables smarter post-transaction offers and audience segmentation, improving both customer experience and partner ROI.

Who needs to know about identity graphs:

Anyone responsible for driving personalized experiences, optimizing media performance, or unlocking deeper audience insights should understand how identity graphs work and what they enable. 

• Marketers and advertisers
• Commerce media platforms
• Data analysts and product teams
• Retailers and publishers

Identity graphs and commerce media

In a commerce media context, identity resolution is what enables a post-transaction placement to be relevant rather than random. An identity graph connects the transaction signal from a publisher — 'this consumer just completed a purchase' — with the broader behavioral and demographic profile of that consumer, allowing the commerce media platform to determine which advertiser's offer is most likely to resonate.

In the Fluent context

Fluent's commerce media platform is powered by an identity graph that connects 200M+ first-party profiles with 2.7B+ Experian IDs, enabling high-confidence consumer matching at scale.

Use identity graph in a sentence:

By integrating Experian’s identity data into its proprietary system, Fluent’s identity graph enables brands to deliver highly personalized, high-performing offers that convert.