Salesforce Data 360 Segment Canvas

In Data 360, the segment canvas is the drag-and-drop interface where you define who belongs in your audience. You pick a target object to “Segment On”, typically a Unified Individual or an Account, and then narrow down that population by dragging attributes from the library on the left and configuring filter logic on the canvas.

UI description

The segment canvas (a.k.a. Segment Builder, Visual Builder) is a drag-and-drop workspace where you define audience logic without writing SQL. The interface has four main areas:

1. Left – Attribute Library with all available data points, split into Direct Attributes (1:1 fields like First Name), Related Attributes (1:N engagement data like purchases), and Calculated Insights (pre-computed metrics). On the second tab you can choose segments.
2. Center – the canvas, where you build logic. Dragging an attribute creates a Rule Container with an operator and value. The canvas has two filter tabs – Include and Exclude (max 50 filters each, 100 total) – plus a separate Group, Rank and Limit tab for prioritization.
3. Above the canvas – segment population count ribbons, which show the total qualifying count for the whole segment. Each container has its own Calculate Population icon for rule-level impact.
4. Top – details and buttons. You will find here the Segment On and Publish information, and Preview, Done, and Save buttons to the right.

Direct vs Related Attributes

On the segment canvas, the practical difference between Direct and Related Attributes lies in how they are visually structured, how their logic is configured, and how they interact with population counting tools.

Direct attributes land as standalone rules. You pick an operator, supply a value, and the container is done. The population ribbon calculates how many records match that field condition.

Related attributes land inside a Container that requires an extra step before field-level filtering: you first define a Measurement – how many times or in what way the related records should occur (Count, Sum, Average, Min, Max). Only then can you add specific field filters inside that container. The calculate population ribbon reflects the full container logic, not a single field value.

For Household segments, related attribute containers have one additional option: an Aggregate Values Across Household checkbox that evaluates combined behavior across all household members rather than per individual.

Containers

Containers are the canvas blocks that hold Related Attribute logic. Direct attributes sit on the canvas as standalone rules, but related attributes can’t, as a single profile can have hundreds of related records (purchases, email events, website visits), and all that interaction logic needs a defined boundary.

When you drag a related attribute onto the canvas, a container is created automatically. Everything inside it – the measurement, the attribute filters – applies exclusively to that specific set of related records. A filter inside a “Sales Orders” container won’t bleed into your “Email Events” container.

Each container shows three pieces of information in the UI:

• Container object name – identifies which DMO is acting as the aggregation entity for that block (e.g., “Sales Order”, “Web Engagement”).
• Relationship paths – shows the navigation path from your Segment On object to the related data.
• Population ribbon – a Calculate Population icon on each container lets you check how many profiles that container’s logic matches, independently of the rest of the segment. This is the fastest way to isolate an overly restrictive rule – calculate each container separately and find where the count drops unexpectedly.

Relationship paths

A relationship path is the route the system takes through your data model to reach an attribute that isn’t directly on the Segment On object. When you navigate the attribute library beyond the root level, you’re creating a path, and each step will result in a DMO join if you use an attribute in segment criteria.

When an attribute is reachable via more than one route, the library surfaces a path selector so you can choose which route to use – longer paths add more join levels to the definition. The system tries to optimize the navigation path, but in some cases it’s better to set this yourself. A shorter path means that fewer records need to be processed during the segment count, as Data 360 will join fewer DMOs in the query.

Aggregations

Aggregations (also called measurements) are the mathematical functions applied inside a container to summarize related records into a single value that can be tested against a qualification rule. Without an aggregation, the canvas has no way to evaluate 1:N data – you can’t filter on “Sales Orders” directly, you first need to define whether you want to look for specific order count, total value, or other.

When you drag a related attribute onto the canvas, selecting an aggregation is the first required step before any field-level filtering. Count aggregation is based on records, but others require selecting the attribute that will be measured.

Operators

Operators in the segment canvas work at two levels: you will find them in Aggregations, and also in the Segment Criteria. Let’s look at the full list of operators available in Segmentation, with a description and examples:

Include vs. Exclude tabs

Include and exclude tabs are the primary functional areas of the segment canvas. We can use them to define audience membership using standard Boolean logic.

The Include Tab is the default workspace for defining who should be in your audience. Any profile meeting the rules set in this tab is added to the potential segment population.

The Exclude Tab is used to define who must be removed from the audience, even if they meet the criteria in the Include tab. It effectively functions as a NOT operator for the segment definition.

The segment engine first evaluates the Include criteria to identify all qualifying members, then subtracts any members that meet the criteria defined in the Exclude tab to produce the final population count.

Group, Rank, and Limit

With the Group, Rank, and Limit feature, we can prioritize a group of people who are in the segment. For example, we can pick only the top 100 high spenders who are currently included in our segment.

The feature is accessed via a dedicated Rank and Limit tab and provides three options:

• Grouping – we can create a cohort or bucket based on a specific, limited number of people with proper criteria.
• Ranking – we can sort those people in a group using a chosen attribute, such as lifetime value, income, or engagement score.
• Limiting – we can limit the final population to a specific number of records.

The Limiting (Maximum Records) feature specifies the upper limit on the number of records returned by the query. If no Group By attribute is configured, the limit applies to the entire result set. If Group By is configured, the limit applies independently to each group. For example, a limit of 1000 with three groups may return up to 3000 records in total.

Hard Limits in Canvas

Segment Canvas brings some limitations that we should be careful about:

1. Data 360 enforces a hard limit of 100 filters per segment; this limit is split as 50 filters for each Include and Exclude Tab.
2. Exclusion logic is not available in Real-Time Segments.
3. You can use up to 3 nested AND/OR operators.
4. In Group, Rank, and Limit, you can use only Direct attributes and Calculated Insights.
5. Group, Rank, and Limit cannot be applied in nested segments.
6. You can have only one level of nested segments.