| Audience | Data teams and marketers working with customer or account data |
| Prerequisites | Access to source data in your warehouse |
Understand what Identity Resolution does, what it produces, and how it fits into the Hightouch platform.
What is Identity Resolution?
Identity Resolution links related records across datasets to determine when they refer to the same real-world entity.
It groups those records into resolved identities--essentially unified customer or account profiles--which can then be used consistently across analytics, Customer Studio, and downstream activation.
Deterministic Identity Resolution runs directly in your data warehouse and produces queryable output tables that represent the current state of your identity graph. Probabilistic Identity Resolution keeps data at rest in your warehouse and provided external storage bucket but processes some data outside your warehouse.
What Identity Resolution produces
When an identity graph runs, Identity Resolution produces:
- Resolved identities, each represented by a synthetic
ht_id. You can think of each resolved identity as a single customer, account, or other entity, even if their data comes from multiple systems. - Output tables that map raw records and identifiers to those identities.
- Optional Golden Record output that creates a unified, one-row-per-identity view that many teams use as their “source of truth” customer or account table.
These outputs can be queried directly, used to build models in Customer Studio, or synced to downstream tools.
Identity Resolution glossary
Resolved identity
A group of input records that Identity Resolution determines belong to the same real-world entity (for example, the same customer or company across tools), represented by a single ht_id.
Identity graph
The structure that connects identifiers and records into resolved identities.
Golden Record
A flattened table with one row per resolved identity, containing canonical field values selected using survivorship rules.
Identity Resolution produces resolved identities—groups of records linked by matching rules. These identities can later be represented as customer profiles using Golden Record or Customer Studio.
Deterministic by default
By default, Identity Resolution uses deterministic matching, which links records only when identifier values match exactly according to the rules you configure.
This approach prioritizes correctness, explainability, and predictability, making it suitable for most customer and account data use cases.
For a deeper explanation of matching behavior and identity stability, see How Identity Resolution works.
Why and when to use Identity Resolution
Customers and accounts interact with your business across many touchpoints, including web and mobile browsing, purchases, support interactions, and marketing channels.
Each interaction can introduce different identifiers, such as:
- Email addresses and phone numbers
- User, account, or loyalty IDs
- Device and anonymous IDs
- Platform-specific identifiers from CRMs, support tools, or ad platforms
Over time, this creates fragmented views of the same real-world entity. The impact is practical and immediate:
- Profiles are incomplete or duplicated
- Analytics overcount customers or accounts
- Events can’t be reliably attributed to the right person or account
Identity Resolution links these signals together so related records are treated as a single identity.
You should use Identity Resolution when you need to:
- Unify customer or account data across multiple sources
- Deduplicate records before analysis or activation
- Build consistent audiences and profiles across tools
- Analyze behavior and revenue at the identity level rather than the record level
Identity Resolution supports person-level, account-level, and other entity-level use cases depending on how your data is modeled.
How Identity Resolution fits into Hightouch
Identity Resolution sits upstream of analytics, Customer Studio, and activation workflows. A typical flow looks like:
-
Source data lives in your warehouse
Product databases, event pipelines, CRMs, and other systems land data in your warehouse. -
IDR builds the identity graph
Hightouch runs Identity Resolution in your warehouse, linking related records into resolved identities (ht_id). -
Output tables are generated for querying and modeling
IDR writes_resolved,_resolved_identifiers,_unresolved, and optionally_golden_records. See Lookup table usage for more details. -
Identities are surfaced as models
Building an identity graph or a Golden Record creates parent models within the Customer Studio schema. Marketers typically work with these models (for example, Golden Record–backed schemas and traits), not with the identity graph itself. -
Unified identities and profiles are activated downstream
Syncs, analytics, and ML pipelines use the same identity foundation.
This design keeps Identity Resolution warehouse-native, tool-agnostic, and fully owned by you.
Use case
Let's walk through a hypothetical e-commerce customer journey to understand some example identifiers and events.
| User actions | Identifiers | Events |
|---|---|---|
| The user clicks on an ad for your e-commerce store while scrolling through Meta on their mobile device. | Their fbclid, their mobile device's MAID, device_id_1 | Page view |
| The user anonymously browses the store's catalog on their mobile device. | anonymous_id_1, device_id_1 | Page views for various product detail pages |
| On one product detail page, the user adds an item to their cart, but then drops off for some reason. | anonymous_id_1, device_id_1 | Add to cart |
| Later, the user returns anonymously to the site on their laptop and adds the same item to their cart. | anonymous_id_2, device_id_2 | Add to cart |
| This time, the user completes checkout and, in the process, provides email, address, and phone number, but doesn't create an account. | email, address, phone number | Check out |
| After some time, the user files a support ticket about their purchase from their mobile device. | email, device_id_2, Zendesk User ID | Support ticket creation |
| Later, the user makes another purchase, but this time decides to create an account. | email, Account ID | Add to cart, Check out, Account creation |
Throughout the journey, the user has multiple anonymous IDs, user IDs, device IDs, and events associated with them. Each event also has essential information, including timestamps and other details, such as information about the products they're interested in.
All these data points need to be reconciled with their PII once they complete an order and create an account.
Identity resolution helps merge all these into a unified profile by writing an identity graph that maps them all together.