Attribute Value PickLists in Segmentation

In this release, Oracle Fusion Unity introduced a picklist option for attributes used in segmentation. These picklists let marketers using the segment builder choose values from a list, rather than having to type the full value exactly right every time. This enhancement eliminates the need to guess exactly what a value looks like—Is it capital or lower case? Is there a space or an underscore? Is it abbreviated?—and replaces trial-and-error with guided selection. By improving value discovery directly within the segmentation experience, picklists make building accurate segments faster, easier, and more reliable.

Business Benefits:

  • Eliminates guesswork when selecting attribute values in segments
  • Reduces errors caused by inconsistent casing, spacing, or naming conventions 

Steps to enable and configure

  1. Navigate to Customer Data Platform > Data Model
  2. Update the URL parameter from root=dataModel to root=expertConfig
  3. Execute a GET request to retrieve logical table column details: api-metadata/metadata/businessmodels/Default/logicaltables/Default.<TableName>/columns
  4. For each column requiring picklists, set "isLookupEnabled" to true
  5. Run required jobs based on the table type:
    • For Contacts, Accounts, or Customers: run the Identity Resolution job
    • For other logical tables: run the Data Warehouse job

Tips and considerations

How to Start Using

  1. In the segment builder, add a condition using an attribute with picklists enabled
  2. Begin typing in the value field to surface a list of valid options
  3. Select the correct value directly from the list instead of manually entering it

Tips and Considerations

  • Picklist search uses “contains” logic and ignores letter case
  • Picklists are supported for attributes with up to 10,000 distinct values
  • Attributes exceeding this limit will not return results
  • Particularly valuable for attributes with inconsistent or complex value formats
  • Reduces dependence on external tools or manual data exploration