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Optimizely Knowledge Base

QA: Whitelisting users in a Full Stack experiment

relevant products:
  • Optimizely X Full Stack

  • Use whitelisting to QA by forcing users into variations in Full Stack experiments

Use whitelisting to override the default behavior of the SDK by forcing users into particular variations in an experiment. In Full Stack projects, you can use whitelisting to QA your experiments. Publish your experiment live and enable whitelists to show specified variations to a few, select users. When you activate the experiment for these users, they can bypass audience targeting and traffic allocation to always see the variation you specify for them. Users who aren't whitelisted must pass audience targeting and traffic allocation to see the live experiment and variations.

For example, imagine that you create an experiment 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 that only 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%. After QA is complete, establish your production settings for audience targeting and traffic allocation.

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

The user IDs used in the whitelist must match the user IDs passed through the SDK in activate(). Otherwise, whitelisting will not work. These user IDs are often anonymous and cryptic (for example, a cookie value), and you have to copy and paste them.

See additional resources
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Here's our developer documentation, where you'll find code samples, full references for our SDKs, and getting started guides.

Create a whitelist

Here's how to create a whitelist for an experiment in a Full Stack 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 whitelisting

Use whitelisting only for preview, experimenting, and QA. You can also use whitelisting when you're unit testing your experiments in code with a mock datafile.

Use whitelisting 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.

To target an experiment to a larger group of users for QA, such as all employees in your organization or a staging environment, 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.