Platform Module · Core Infrastructure

Resolve fragmented records into one controlled customer identity.

Identity Resolution is the core of the platform. It reconciles identifiers across apps, web, devices, transactions, loyalty, and legacy systems into a single, governed customer identity that every audience, journey, measurement, and monetization decision can trust.

Who uses it CTO, Head of Data, and Identity/Data-Platform engineers
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Direct answer

Identity Resolution is the module that merges fragmented customer records — across apps, web, devices, transactions, and legacy systems — into one controlled identity using deterministic and probabilistic matching. It is the control point of the platform: every downstream audience, journey, and monetization decision is only as accurate as the identity beneath it.

What it is

The module, and the problem it solves.

What it is

Identity Resolution

A governed identity layer that ingests identifiers from every source and resolves them into a single customer record with full lineage. Matching is deterministic where strong keys exist and probabilistic where they do not, with confidence scores attached to every link.

The problem it solves

Why it matters

Without resolution, the same customer exists as many disconnected records, so teams act on partial, conflicting views — duplicated audiences, broken journeys, and unreliable measurement. Identity is the dependency under all of it.

Who uses it

The teams that operate this module.

Data & identity engineering

Own match rules, keys, and confidence thresholds, and monitor resolution quality.

CRM & marketing

Rely on resolved profiles for accurate audiences and suppression.

Analytics & finance

Need one customer definition for trustworthy measurement and attribution.

Security & governance

Control which identifiers can be used and how identity is accessed.

How it works

From inputs to governed output.

1

Collect identifiers

Ingest keys and signals from apps, web, transactions, loyalty, and legacy systems.

2

Match

Apply deterministic rules where strong keys exist and probabilistic matching where they do not.

3

Merge & score

Resolve to one customer record with confidence scores and full lineage.

4

Expose

Publish the governed identity to profiles, audiences, journeys, and measurement.

Data inputs

What the module runs on.

Strong keys

Email, phone, loyalty ID, and account identifiers.

Device & web

App user IDs, cookies, and device signals.

Transactions

Purchase and payment records that link behavior to identity.

Legacy records

Existing CRM and system identifiers to reconcile.

Consent

Consent scope governing identifier use.

Operator view

What operators do here.

Tune match rules

Adjust deterministic and probabilistic logic and confidence thresholds.

Review merges

Inspect and override merge/split decisions where needed.

Monitor quality

Track resolution coverage, match confidence, and duplicate rates.

Govern access

Define which identifiers and identity fields are usable, by whom.

Outputs

What the module produces.

Unified customer ID

One governed identity reused across the platform.

Match lineage

Why records merged, with type and confidence.

Resolution metrics

Coverage and confidence signals for monitoring.

Measurement

How success is measured.

Resolution coverage

Share of records resolved into unified identities.

Match confidence

Distribution of deterministic vs probabilistic links.

Duplicate rate

Residual duplication after resolution.

Deployment considerations

How it deploys.

Deployment

Control boundary

Runs inside your environment so raw identifiers stay within your control boundary. Deterministic-only modes are available where probabilistic matching is restricted. See Deployment and Trust & Security.

Part of the platform

Works with the rest

This module is designed to run standalone or as part of the full Binoban operating layer. Explore the Platform overview to see how the layers connect.

Common questions

Answers for evaluators.

What is identity resolution?

It reconciles fragmented customer records across apps, web, devices, transactions, and legacy systems into one controlled identity, using deterministic and probabilistic matching with confidence scores.

Why is identity resolution the control point?

Every downstream audience, journey, measurement, and monetization decision depends on it — accuracy upstream determines accuracy everywhere downstream.

Can it run without probabilistic matching?

Yes. Deterministic-only modes are available where probabilistic matching is restricted by policy or regulation.

See this module in your environment.

Tell us your data sources, scale, and the outcome you need. We'll map how Identity Resolution fits your architecture and deployment model.

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