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Segment Canvas
Use the segment canvas in Data 360 to select direct and related attributes to narrow down a created segment to your target audience.
Steps
Drag Attributes from the library to your canvas to set filter criteria for your segment. You can drag attributes into their own container or combine them with other attributes in the same container to create an AND or an OR relationship between the attributes. From the Direct tab, you can select Data Model Objects (DMOs) that have a 1:1 or a N:1 relationship from the Segment On object (on some path). From the Related tab, you can select DMOs that have a 1:N relationship from the Segment On object (on some path). The library displays all object types including standard or custom objects as long as the objects are mapped.
Relationships
Attributes in the library can have multiple relationships to other objects. When an object has multiple relationships to the same object, the library displays both the object name and the related field names for easier differentiation when building a segment.
If an object has multiple relationships to itself through nesting, the attribute library displays only the object nested beneath itself one time.
Match and reconciliation rules guide the DMO graph and determine the DMOs that are displayed in the attribute library. When match and reconciliation rules are configured for an individual DMO, derived relationships are generated between the target entity DMO and the unified individual DMO. These derived relationships are traversable, and individual DMOs along with paths with derived relationships appear in the attribute library.
Object relationships in a container path are applied in a case-sensitive manner. Data 360 supports one case-sensitive method of joining data tables that’s not configurable. The values of the linked records must match exactly. For example, if the value of SalesOrder.SoldToCustomer is c12d3, and the value of Individual.IndividualId is C12D3, the records don’t link up because the case of the two values doesn’t match.
Direct Attributes
A direct attribute is a single data point or piece of information about the entity being segmented, such as a postal code, first name, or birthday. Direct attributes are often demographic attributes.
Related Attributes
A related attribute is a collection of multiple data points or pieces of information associated with the entity being segmented, such as purchase history, product numbers, or email interactions. Related attributes are often behavioral or engagement events.
Value Field
The value against which the attribute value is compared to determines whether it satisfies the condition. Queries honor exact matching on special characters and accents and aren’t case-sensitive. For example, querying on City | Is Equal To | Canon City doesn’t find the city Cañon City. A query on City | Is Equal To | Canon City finds canon city. Also, queries honor exact matching on types. For example, if the field type is a string for "Purchase Order Number", querying on Purchase order number | Is Equal To | 0852 finds a Purchase Order Number that’s equal to 0852. It doesn't find the purchase order number 852, which has no leading zero, or “0852” with quotation marks.
Segment Count
The segment count indicates the number of entities based on the filters applied. When the segment target is Individual or Unified Individual, then the count also excludes any Individual profiles where a Data Deletion or Restrict Processing request has been submitted. For more information, see Data Subject Rights.
Segment count isn’t applicable for dynamic segments if parameterized values are enabled. The segment population is empty for a dynamic segment with attributes where parameterized values are enabled.
Filter level count is available on direct attribute filters. It shows the segment population attributed to that filter (for example, for Segment On Individual, a filter Gender = Female shows the distinct count of individuals whose gender is female). No count is shown when you first add a filter; the ribbon shows Calculate population. Refresh to run the count. Counts are explicit and follow the same approximate population behavior as segment count when that option is enabled.
Container level count is available on related attribute filters (containers). It shows the segment population attributed to that container (for example, for Segment On Individual, a container with “at least one sales order has Product Line = Luxury watches AND Shipping City = California” shows the distinct count of individuals who have at least one such order). No count is shown when you first add a container; the ribbon shows Calculate population. Refresh to run the count. Counts are explicit and follow the same approximate population behavior as segment count when that option is enabled.
Primary Key and Foreign Key
A primary key is an object’s unique identifier of a record, such as a customer email address or a product SKU. A foreign key is a field in a table that refers to the primary key of another table. The foreign key links two data tables based on a data point, for example, a customer ID.
Primary key and foreign key attributes don’t appear in the segment canvas. To make these attributes available in the attribute library, create a custom attribute without assigning it as the primary or foreign key. Use this custom attribute when building segments.
- Segment Your Data with Attributes
After you create a segment, narrow your data based on related or direct attributes on the segment canvas interface. Narrow down a subset of the population from a filtered audience to see who’s included and excluded. Segment exclusion is optional. - Filtering Using Containers and Attributes
On the segmentation canvas, you can define rules to filter your population to a targeted group. You can create and combine filters in different ways for different results. When you define a filter using a related attribute, a container is added to the canvas with applicable rule options. The related attribute’s data model object (DMO) becomes the container object. - Approximate Segment Population
When building a segment in Data 360, use the Approximate Segment Population field to quickly check that the segment count meets your expectations. The approximate count provides an estimated segment population, represented as a range of possible values with 95% confidence. When the segment population is small, or when the record count for Segment On is low, the actual population can differ from the estimated range. You can then adjust your segment rules and save your segment to get the final segment population. To use this feature, enable Approximate Segment Population in Feature Manager. - Segmentation Operators in Data 360
For each attribute dragged to the segment canvas from the attribute library, select an operator to modify it and limit records that are selected. The operators that are available depend on the data type of the attribute. - Segment Time Zone
Segment publish schedules are interpreted in the user’s time zone. Your Data 360 org sets the publish time zones on the initial segment save and segment canvas. Segment and activation fields display the Publish Schedule Start Date and Time in the user's time zone. - Use Value Suggestions in Segmentation
You can enable value suggestions for data model object (DMO) fields if the data type is Text. Enable or deactivate the feature in the DMO record home. You can enable value suggestions for up to 500 attributes for your entire org. It can take up to 24 hours for suggested values to appear. - Multiple Identity Resolution Rulesets in Segments
Compare the impact of different identity resolution rulesets for the same entity on segment population counts. After using identity resolution to create different rulesets for the same entity, entities and attributes created by both rulesets are available in segmentation. Use attributes from both rulesets to validate, or A/B test, different population counts. Entities and attributes names include the Ruleset ID at the end when you select what entity to Segment On or add attributes in the attribute library. - Multiple Marketing Cloud Engagement Business Units in Segments
Filter segments by business unit (BU) to determine the true size of segments for campaign planning. If segments aren’t filtered by business unit, the difference between a Data 360 segment population and Marketing Cloud Engagement data extension count can be significant.
