What is an identity graph
An identity graph consists of three elements:- Nodes are individual identifiers: a hashed email, a mobile advertising ID (MAID), a cookie, a phone number, a postal address
- Edges are linkages between identifiers, representing evidence that two identifiers belong to the same person or household
- Connected components are clusters of nodes where every identifier is reachable from every other through some path of edges—each component represents a resolved identity
Why graph quality matters
Graph quality is a balance between two failure modes:| Problem | Cause | Consequence |
|---|---|---|
| Under-connected | Too few or too conservative linkages | Fragmented user profiles, duplicated outreach, inflated audience counts |
| Over-connected | Too many or too liberal linkages | Merged distinct individuals, corrupted targeting, wasted spend on wrong audiences |
How the graph grows
Identity graphs typically build in layers:- First-party data as foundation. Login events, CRM records, and transaction data create high-confidence, deterministic linkages. A customer who logs into your app with their email on their iPhone creates a direct edge between that hashed email and that device’s IDFA.
- Third-party data adding edges. External identity providers contribute additional linkages that your first-party data cannot observe. A provider might link your customer’s email to a second device ID or a postal address that you have never seen.
- Deterministic vs. probabilistic linkages. Deterministic linkages come from direct observation (same user logged in on two devices). Probabilistic linkages are inferred from signals like shared IP addresses, co-location patterns, or behavioral similarity. Deterministic linkages are more reliable but harder to scale; probabilistic linkages offer broader reach but carry higher false-match risk.
Two approaches to using third-party identity data
When organizations purchase third-party identity data, the data serves one of two distinct purposes:| Graph enrichment | Addressability expansion | |
|---|---|---|
| Primary benefit | Improved identity resolution | Improved media activation reach |
| Effect on graph structure | Adds or strengthens edges between nodes | Appends identifiers to existing nodes |
| Impact on segmentation | Can change segment composition | Does not change segment composition |
| Impact on match rates | Indirect (better resolution enables better matching) | Direct (more identifiers per record increases match rates) |
| Risk profile | Higher (bad linkages corrupt graph structure) | Lower (bad appends reduce match rates but don’t corrupt structure) |
Graph Enrichment
Strengthen your identity graph’s structure with third-party linkage data
Addressability Expansion
Improve downstream activation reach by appending additional identifiers
Evaluating Identity Data Providers
A structured framework for choosing and measuring identity data providers
Identity graphs in Narrative
Narrative’s platform connects to identity graph concepts in several ways:Rosetta Stone and unique identifiers
Rosetta Stone’sunique_identifier attribute provides a standardized way to match identifiers across suppliers. When you join on unique_identifier.value and unique_identifier.type, Narrative handles format normalization—consistent MAID casing, standardized email hashing—so that identifiers from different sources match correctly.