Relation Data

Introduction

Relation Data is a new schema type in the Customer Data Platform (CDP) designed to support one‑to‑many relationships. It allows you to link a single profile to multiple related objects—such as subscriptions, courses, insurance policies, contracts, memberships, or other relevant entities and properties—without duplicating attributes across data sources.
This creates a scalable, flexible way to enrich profiles and build highly targeted audiences.

With Relation Data, the Audience Builder now supports filtering within relational objects, enabling segmentation logic that goes beyond flat profile attributes.

Key Capabilities

One‑to‑Many Data Modeling

You can now model structures where one person can be linked to many objects, each with its own set of attributes. Examples include:

One profile → many children
One profile → many vehicles
One profile → many subscriptions

The Relation Data schema provides an extensible structure to hold these objects and their attributes.

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How It Works

Schema Structure

In Data Management, go to Schemas, and click 'Create new schema':

Schema name: Relation Data
Primary key: RelationId
Each relational object is linked to a profile via an identifier (e.g., contactId, customerId)

Each relational record represents one object, meaning profiless with many related objects will have multiple Relation Data entries.

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Audience Builder Enhancements

The Audience Builder introduces expanded operators for working with relational objects, including:

All
At least / At most / Exactly

These give you fine‑grained control over how many relational objects must match the conditions.

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👀 Use case:

Use All when you want to ensure that every relational object linked to the profile matches all the attribute filters you have defined.
This operator is ideal when you want strict consistency across all related items.

For example:

“Profiles where all linked subscriptions are active.”
“Profiles where all owned vehicles have an emission class of Euro 6.”

Note: The All operator only makes sense when one or more filters are applied.

If no filters are selected, the condition cannot be evaluated and should not be used.

Attribute‑Level Filtering Inside Relations

The Audience Builder supports deep filtering within the relational objects themselves.
This means you can build audiences based on attributes not only on the profile level, but also within the related object.

Profiles who own at least 2 cars
Profiles who have exactly 1 child, where the child's age (in weeks) is between 24 and 36 and birth_remark equals born prematurely

These examples show how Relation Data allows for nested conditions—first selecting the profile, then applying logic inside the related items.

With Relation Data, you can now express segmentation logic that previously required heavy preprocessing.

For example:

“Profiles with gender = male AND who own at least one animal younger than 12 months where weight remark = overweight.”
“Profiles who have a related object matching multiple attribute conditions simultaneously.”

This makes it easier to activate campaigns that require relationship‑aware logic.

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Typical Use Cases

Here are generalized examples inspired by internal work files

Profiles who own 2 or more dogs, where at least one dog is younger than 12 months
Parents who have at least one child under 5 years old and another child aged 6–12
Profiles who own more than 1 vehicle, where at least one vehicle has a service due within 30 days
Profiles who have at least 2 active subscriptions, where at least one is about to renew within 14 days
Persons who have 1 or more medications, and at least one requires renewal within 7 days
Accounts where at least 3 contacts have interacted with your website in the last 60 days

These examples demonstrate how Relation Data enables segmentation involving secondary entities, not just the profile.