Excluding Traits in Algorithmic (Look-Alike) Models

In this video you will learn how and why to exclude specific (or groups of) traits from an Algorithmic (Look-Alike) Model.

Use cases for this feature include:

  • Extremely common traits such as site visitor traits bias the model which won’t be useful in finding a quality look-alike audience. Customers no longer have to create a separate data source and store common traits in the new data source, but can now simply exclude them.
  • There is now a way to use a subset of traits from a third party, such as just behavioral interests, rather than all the information in a model. Third parties usually send a lot of data which might not be useful for the customer. In some cases, they won’t be allowed to use all of the data from a legal perspective in modeling. Now you can exclude traits or folders of traits that you don’t want to include in the model.

For more information about this feature, see the documentation.

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