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An audience definition uses:
  • inclusion criteria that decide what units to include
  • allocation that sets the percentage of the included population to run the experiment on
  • a targeting key that specifies the field in the evaluation context to randomize traffic based on
  • exclusivity to control the behavior of the experiment in relation to other experiments, rules, and segments

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Inclusion Criteria

Inclusion criteria define which units are eligible for the experiment. You can combine multiple criteria types:
  • Attributes: Target users based on evaluation context fields like country, platform, or version.
  • Segments: Target users who belong to a pre-defined segment, letting you reuse the same audience across experiments.
  • Holdbacks: Include or exclude a random subset of users defined by a holdback on a surface.
  • Groups: Combine multiple criteria with AND or OR operators to build complex targeting logic.
Learn how to configure these criteria in the Define the Audience guide. For more details about allocation, randomization, and the targeting key, go to the audience page for flags.

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Randomization

Confidence uses randomization to assign variants to users. To randomize, Confidence needs to know which field in the evaluation context it should take the value from. Read more in the flag documentation.

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Sticky Assignment

Under advanced options in the audience section you can enable sticky assignments. When you enable sticky assignments, Confidence writes all assignments to a storage that is accessible at resolve time with low latency. Read more about sticky assignment in the flags documentation.

Audience Reference

Deep dive into audience configuration

Define Audience Criteria

Configure targeting criteria

Exclusive Experiments

Set up mutual exclusion

Treatments Reference

Configure experiment variants