How do identity graphs connect online and offline customer data?
An identity graph connects online and offline customer data by linking multiple identifiers — such as email addresses, device IDs, cookies, phone numbers, and physical addresses — into a single, unified profile for each real person. This connection bridges the gap between digital interactions and real-world customer data, giving businesses a complete view of who their customers are. The questions below unpack how identity graphs work and why they outperform traditional data tools.
What types of data does an identity graph connect?
An identity graph connects both online and offline identifiers into one unified customer profile. Online data includes email addresses, device IDs, cookies, mobile ad IDs, and IP addresses. Offline data includes phone numbers, mailing addresses, and purchase history. The graph links these identifiers together so they all point to the same real individual, regardless of where the data originated.
This breadth of data coverage is what makes identity graphs so powerful. A customer might browse a website anonymously on their phone, make an in-store purchase with a loyalty card, and later open a promotional email on their laptop. Without an identity graph, these three touchpoints look like three different people. With one, they resolve into a single, coherent customer record.
- Online identifiers: email addresses, cookies, device IDs, mobile ad IDs
- Offline identifiers: postal addresses, phone numbers, loyalty card data
- Behavioral signals: browsing history, purchase events, app activity
- Professional data: job titles, company affiliations, professional email addresses
How does an identity graph match identifiers across devices?
An identity graph matches identifiers across devices using a combination of deterministic and probabilistic matching. Deterministic matching links identifiers that are definitively connected — for example, the same email address used to log in on two different devices. Probabilistic matching uses statistical inference to connect identifiers that are likely related, such as devices sharing the same IP address and usage patterns.
Deterministic matching is highly accurate but limited to moments when a customer authenticates, such as logging in or submitting a form. Probabilistic matching extends coverage to anonymous interactions, filling in the gaps where explicit identifiers are not available. Together, these two methods allow an identity graph to maintain accurate, real-time profiles across a customer’s full range of devices and channels.
The result is a continuously updated view of each individual that reflects both authenticated and anonymous behavior, enabling businesses to recognize the same person whether they are signed in or not.
Why can’t a CRM or customer database do what an identity graph does?
A CRM or customer database stores records about known customers but cannot resolve identity across anonymous touchpoints or multiple devices. These systems depend on a customer actively providing their information. An identity graph, by contrast, is built to connect fragmented, often anonymous data points and resolve them to a real individual in real time, including before a customer ever identifies themselves.
A CRM is a relational database: it holds structured records linked by a customer ID that the business assigns. It cannot natively match an anonymous website visitor to an existing customer record, nor can it bridge offline and online behavior without manual data imports. An identity graph is fundamentally different in structure and purpose. It is built around the individual, not around the data the business happens to hold.
Key distinctions include:
- CRMs require known data; identity graphs resolve unknown and anonymous identifiers
- CRMs are static between updates; identity graphs update in real time
- CRMs link records by business-assigned IDs; identity graphs link by real-world identity signals
How FullContact helps you build and use an identity graph
We built our Resolve platform specifically to address the limitations that CRMs and traditional databases cannot overcome. Our identity graph spans over a decade of authenticated and anonymous data, connecting online and offline identifiers to real individuals in real time, with API responses delivered in under 150 milliseconds. With us, you can:
- Match anonymous visitors to existing customer profiles without requiring authentication
- Append 900+ personal and professional insights to new and existing records
- Resolve identity across devices using both deterministic and probabilistic matching
We do this in a privacy-safe way, meaning your data stays yours and is never shared. If you want to see how identity resolution can transform your customer data strategy, contact us and we will walk you through exactly what is possible for your business.