Skip to main content
menu_icon.png

Everything you need to switch from Optimizely Classic to X in one place: See the Optimizely X Web Transition Guide.

x
Optimizely Knowledge Base

Manage experiments in Full Stack

THIS ARTICLE WILL HELP YOU:
  • Pause, archive, and change experiments
  • Use the Experiments dashboard

In Optimizely X Full Stack, you'll use the Experiments dashboard to pause, archive, and change experiments.

See additional resources
Here are all our articles about the Optimizely dashboard, which you'll use to create projects, add collaborators, manage privacy settings, and more: 

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

Pause an experiment

To pause a running experiment, click the Actions icon (). Choose Pause from the dropdown menu. After pausing an experiment, all users will start to receive the original experience. 

Pause a variation

Once you've started an SDK experiment, you cannot delete a variation. However, you can pause variations instead. When you pause a variation, traffic from that variation will be redistributed among the experiment's other variations, but the results will still be accessible for the paused variation.

Clicking Confirm will tell Optimizely to stop sending traffic to treatment_b. However, the variation can be un-paused later by clicking Resume.

All previous results will still be available for any resumed variations.

User profiles and sticky bucketing

Pausing a variation is only necessary if you are using Optimizely's user profiles feature. User profiles allow Optimizely users to ensure variation assignments are sticky in any SDK.

If you are working with user profiles and want to ensure that a variation no longer receives any new traffic, you have two options:

  • Pause the variation, which ensures that it will no longer receive any traffic, or

  • Set variation traffic distribution percentage to zero, to ensure the variation will no longer receive new traffic, while users who have previously been exposed to the experiment will remain in their assigned variation.

If you haven't implemented user profiles in the SDK, pausing a variation is no different than setting that variation's traffic distribution percentage to zero. See the Optimizely developer docs for more information on implementing user profiles.

For more information on how bucketing works in Optimizely, see this article.

Archive and un-archive an experiment

Archive an experiment to remove it from the Experiments dashboard. Optimizely keeps your experiment data, so you can un-archive the experiment later, if you wish.

To archive an experiment, click the Actions icon (). Choose Archive from the dropdown menu. When you're prompted to confirm, click Archive Experiment.

Here's how to un-archive an experiment, with step-by-step instructions below:

  1. Click the filter menu and choose Archived from the dropdown.

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

  3. Choose Unarchive from the dropdown menu.

Change experiment parameters

You can change the experiment parameters, such as traffic allocation, variations, audiences, and events directly in the Experiments dashboard.

Here's a quick walkthrough. Scroll down for step-by-step instructions.

  1. Click the experiment name to open the Edit Experiment screen.

  2. Change your experiment parameters as desired.

  3. Click Save Experiment.

Changing your experiments will update your project’s datafile within a few seconds. It may take some time before your experiments are updated in production, depending on how often you retrieve datafile updates.

If you want your experiments to update in real-time, use Webhooks to receive datafile updates. 

In Optimizely X Full Stack, be careful when you’re changing experiment parameters, especially the experiment key and variation keys for a running experiment.

Don’t change the experiment key or variation keys unless you’re making the corresponding changes in your code. If you use a key that isn’t referenced in your code, no traffic will be sent to that experiment or variation.