Identity Resolution
The process of matching and merging multiple identifiers — such as device IDs, email addresses, cookies, and login events — into a single, unified user profile.
What is identity resolution?
Identity resolution is the practice of connecting fragmented user data points into a cohesive profile that represents a single person. A visitor might browse your site on a phone, return on a laptop, click an email link, and eventually convert on a tablet. Each touchpoint generates a different identifier — a cookie ID, a device fingerprint, a hashed email, a login credential. Identity resolution links those disparate signals together so your analytics and marketing platforms understand they belong to the same person.
There are two primary approaches: deterministic matching, which uses known identifiers like email addresses or login events to create exact links, and probabilistic matching, which uses statistical models based on signals like IP address, device type, and browsing patterns to infer likely connections.
Why it matters
Without identity resolution, a single customer looks like three or four separate visitors in your analytics. This distortion cascades through every downstream metric:
- Inflated audience counts — Unique visitor tallies overstate your actual reach because the same person appears multiple times.
- Broken attribution — The ad click on mobile and the conversion on desktop are never connected, so campaign performance looks worse than reality.
- Poor personalization — Retargeting treats a returning customer like a stranger, wasting ad spend on someone who already bought.
- Inaccurate lifetime value — Revenue tied to one user is spread across multiple anonymous profiles, making it impossible to identify high-value customers.
Accurate identity resolution directly improves attribution accuracy, reduces wasted ad spend, and enables meaningful personalization across channels.
How it works
A typical identity resolution process follows this flow:
- Signal collection — The system captures identifiers at every touchpoint: first-party cookies, hashed emails, phone numbers, login events, CRM IDs, and click IDs from ad platforms.
- Identity graph construction — Collected identifiers are mapped into a graph structure where each node is an identifier and edges represent observed connections (e.g., cookie X was present when email Y was submitted).
- Profile merging — When a new identifier matches an existing graph node, the profiles are merged. A deterministic match (same email address) triggers an immediate merge; a probabilistic match may require multiple corroborating signals before linking.
- Continuous reconciliation — As new events arrive, the identity graph updates in real time, resolving previously anonymous sessions and retroactively attributing past behavior to the now-known user.
Deterministic vs. probabilistic identity resolution
| Dimension | Deterministic | Probabilistic |
|---|---|---|
| Match basis | Known identifiers (email, login, phone) | Statistical signals (IP, device, behavior) |
| Accuracy | Very high — exact matches only | Moderate — confidence scores vary |
| Coverage | Limited to authenticated events | Extends to anonymous sessions |
| False positive risk | Minimal | Higher without careful tuning |
| Best use case | Cross-device stitching for known users | Expanding reach to pre-login touchpoints |
How Ingest Labs handles identity resolution
Ingest Labs uses deterministic identity resolution powered by its MPID — a server-set first-party cookie placed on the customer's own subdomain with an approximately two-year lifespan. Because the MPID persists across sessions and survives browser restrictions like ITP, Ingest Labs stitches user journeys across devices and browsers without relying on third-party cookies or probabilistic guesswork. The result is 95% attribution accuracy and a unified view of each customer from first touch to conversion.
See how Ingest Labs handles identity resolution
Book a demo to see server-side tracking, identity resolution, and data quality in action.