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Overview

This guide walks you through creating Clean Room audiences. Depending on your workflow:
  • Publisher-driven workflows: Publishers create audiences on behalf of advertisers
  • Permutive-driven workflows: Advertisers create their own audiences
Both use the same interface described below.
Prerequisites:
  • Data sources uploaded and permissioned between parties
  • Publisher connection established

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Steps

1

Navigate to Cohorts

In the left menu of your demand-side workspace, click Cohorts.
2

Add a new cohort

Click + Add Cohort in the top right.
3

Name your cohort

Enter a Name for your cohort (e.g., “ACME - Travel Intenders - Lookalike 70%”). Optionally add a Description to document the cohort’s purpose.
4

Build your query

Build your Query using data from the available data sources:
  • Click the + button to add conditions
  • Select “FIRST PARTY” to access data source fields
  • Choose the data source (e.g., advertiser’s uploaded data)
  • Select the specific field or segment you want to target
  • Add multiple conditions using “any” (OR) or “all” (AND) logic
5

Set geo-targeting (optional)

Select a Country to geo-lock the audience to a specific market.
6

Select publisher connections

In the Connections dropdown, select which publisher connection(s) to deploy this cohort to.
7

Choose cohort type

Click one of the buttons:
  • Create Matched Cohort: Creates an audience of users matched between the data sources. Segmentation begins immediately.
  • Create Modeled Cohort: Creates a lookalike audience by finding similar users. Requires model training (~1 hour) followed by similarity selection before segmentation begins.

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Configuring Modeled Cohorts

After you create a modeled cohort, the lookalike model will train for approximately 1 hour. Once training completes:
1

Navigate to the cohort

Go back to the cohort in the Cohorts list.
2

Review the similarity-reach curve

You’ll see a curve showing the trade-off between audience similarity and reach.
3

Select similarity level

Choose a point on the curve based on your campaign goals:
  • Higher similarity (e.g., 90%): Smaller, more precise audience closely matching your seed data
  • Lower similarity (e.g., 70%): Larger audience with broader reach but less precise matching
4

Confirm selection

Confirm your selection. Segmentation will now begin and the cohort will be ready for targeting.
Important timing notes:
  • Matched cohorts start segmenting users immediately upon creation
  • Modeled cohorts require: (1) ~1 hour for model training, (2) user selection of similarity-reach point, then (3) segmentation begins
Best practices:
  • Use clear, descriptive naming conventions that include the advertiser name, audience description, and cohort type
  • Start with matched cohorts of at least 1,000 users for reliable targeting
  • For modeled cohorts, consider testing different model similarity levels (e.g., 70%, 80%, 90%) to balance reach and precision
  • Monitor cohort sizes and performance after creation to ensure they meet campaign requirements

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Next Steps

Configuring Ad Server Targeting

Set up targeting in your ad server

Back to Clean Room

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