Platform Data Foundation Identity Resolution
Data Foundation

Stitch every signal into one golden profile

Resolve fragmented identifiers across devices, channels, and time into a single persistent customer record continuously in real time.

1:1
Person-level accuracy
30+
Identifier types matched
Real-time
Continuous resolution
5B+
Profiles unified
THE CHALLENGE

One customer, a dozen disconnected identities

The same person logs in on mobile, clicks an email on desktop, calls support, and buys in-store and most systems treat them as four different people.

📱

Device sprawl

Cookies, mobile IDs, and app sessions splinter a single person into anonymous fragments across every screen.

🧱

Channel silos

CRM, web, POS, and call-center systems each hold their own version of the customer with no shared key.

Stale stitching

Nightly batch matching means identities are always hours behind the customer's actual behaviour.

THE IDENTITY GRAPH

Every identifier collapses into one resolved profile

The system uses both exact matches and smart guesswork to update a customer's profile in real time. The moment new data comes in, it automatically combines, separates, or updates profiles to keep them perfectly accurate.

Fragmented signals
✉️ Email address
📱 Phone / MAID
🍪 Cookie & device ID
🪪 CRM & loyalty ID
🔑 Login & auth token
EC
Emily Carter
Resolved · Golden profile
TNameEmily Carter
@Email address[email protected]
#Phone number+91 98200 41555
Device / MAIDa1f9-22c7-e480
Customer IDC-528130
Every touchpoint
🌐 Website & app
🏬 In-store / POS
🎧 Contact center
📣 Paid & social
💬 Email & messaging
HOW WE MATCH

Three matching engines combined to deliver one verdict

🔑

Deterministic

Exact matches on shared keys email, phone, customer ID deliver high-confidence links you can trust for activation.

Hashed PIIAuth events
📊

Probabilistic

Behavioural, geo, and device signals score the likelihood that two fragments are the same person when no key exists.

Fuzzy logicConfidence score
🧠

AI-assisted

Graph ML continuously re-evaluates edges, catching household splits, shared devices, and life-stage changes.

Graph MLSelf-healing
92%
Match rate across channels
<200ms
Resolution latency
40%
Fewer duplicate profiles
100%
Consent-aware merges
WHY FIRSTHIVE

Real-time graph vs. nightly batch stitching

CapabilityTraditional CDPFirstHive
Resolution timingNightly batchReal-time, streaming
Probabilistic + deterministicOne or the otherUnified, blended
Household & device awarenessGraph-native
Self-healing merges & splitsContinuous
Consent enforced at mergeManualAutomatic

Resolve your customer identities in real time

See how FirstHive collapses scattered identifiers into one living profile your whole stack can act on.