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Optimizely Knowledge Base

QA: Whitelist users in an SDK experiment

relevant products:
  • Optimizely X Full Stack
  • Optimizely X Mobile
  • Optimizely X OTT

THIS ARTICLE WILL HELP YOU:
  • Use whitelists to QA by showing experiments to certain allowed users in SDK experiments

A whitelist is a permissions list that grants certain users access to an experiment and variation. In SDK projects, you can use whitelists to QA your experiments. Publish your experiments live and enable whitelists to show specified variations to a few, select users. When you activate the experiment for these users, they will be able to bypass audience targeting and traffic allocation to always see the variation you specify for them. Users who aren't whitelisted will have to pass audience targeting and traffic allocation in order to see the live experiment.

For example, imagine that you create an A/B test that compares Variation A and Variation B. You want to QA the experiment's live behavior and show the variations to a few key stakeholders. Create a whitelist that includes the user IDs for the people who should see the live experiment.

To ensure no one other than your whitelisted users can see the experiment, create an audience targeted to an attribute no user will have, or set the experiment's traffic allocation to 0%. Once your QA is complete, establish your production settings for audience targeting and traffic allocation.

Optimizely allows you to whitelist up to 10 users per experiment.

Create a whitelist

Here's how to create a whitelist for an experiment in an SDK project.

  1. Navigate to the Experiments dashboard.

  2. Click the Actions icon () for the experiment. Click Whitelist.

  3. Specify user IDs and corresponding variations you want to force for those users.


    In this example, we forced one visitor into variation_a and two visitors into variation_b.

When to use whitelists

Use whitelists only for preview, testing, and QA, and for no more than 10 user IDs. Forcing variations with a large number of user IDs will bias your experiment results, so we limit you to 10.

If you'd like to target an experiment to a larger group of users for QA, use audiences instead. Create an attribute that every user in the group will share, and target the experiment to an audience that contains that attribute.