RGM-CD-03 · Customer Data Platforms · Module 3 of 6
RGM° · Training

Identity Resolution

Identity resolution links a user's many identifiers into a unified profile. This module covers deterministic vs probabilistic, the major identifiers, and the quality measurement that prevents bad personalization.

What you will learn

  1. Why identity resolution is the heart of a CDP
  2. Deterministic vs probabilistic matching
  3. Identity graphs and their construction
  4. Common identifiers and their reliability
  5. Cross-device matching
  6. Cross-domain matching
  7. Online-to-offline matching
  8. Privacy considerations
  9. Vendor identity solutions (LiveRamp, Tapad, etc.)
  10. Measuring identity-resolution quality
  11. Common identity-resolution mistakes

1. Why identity resolution

A user appears in dozens of systems with different identifiers: web cookies, mobile IDs, email, phone, customer-ID. Identity resolution links these into a unified persistent profile. Without it, every marketing system has a partial view of the customer.

2. Deterministic vs probabilistic

TypeHow it worksAccuracy
DeterministicExact match on known identifier (email, phone, customer-ID)Very high when match exists
ProbabilisticStatistical match on signals (IP, device, behavior)Moderate; declining due to privacy

3. Identity graphs

An identity graph stores relationships between identifiers. Each node is an identifier; each edge is a confidence-scored relationship. The graph grows as new signals connect previously-disjoint identifiers.

4. Common identifiers

5. Cross-device

Linking a user's desktop, mobile web, and mobile app. Methods: login (most reliable), email collection, deterministic match via shared identifier, probabilistic via behavior/location.

6. Cross-domain

Linking the same user across multiple owned domains. Methods: first-party cookie + login, server-side identity stitching, third-party graph (LiveRamp).

7. Online-to-offline

Linking online identifiers to offline customer records. Methods: email/phone capture, in-store login, transaction matching, third-party graph providers.

8. Privacy

9. Vendor solutions

10. Measuring quality

11. Common mistakes

Treating one identifier as authoritative when conflicts arise. Not de-duplicating profiles after merge. Letting low-confidence probabilistic matches drive personalization. Not propagating deletion requests across the graph. Ignoring consent state in identity stitching.
How to use this module: The deterministic-vs-probabilistic table (Section 2), the identifier list (Section 4), and the quality metrics (Section 10) are the planning artifacts.

Sources & further reading


Part of the Customer Data Platforms series · RGM Training