A/B test AI Personalization
On this page
AI Personalization is a beta feature according to Algolia’s Terms of Service (“Beta Services”).
A/B testing, also known as split testing or bucket testing, compares two versions of a web page, app, or other product to decide which one performs better.
With AI Personalization, A/B testing involves comparing two versions of search results. These search results are based on the effect of the personalization re-ranking level you set.
By conducting an A/B test, you can find the ideal level of personalization that suits your business and allows you to maximize user engagement and satisfaction.
Prepare to A/B test AI Personalization
Before setting up an A/B test, make sure you have properly configured AI Personalization on the dashboard:
Alternatively, you can use the AI Personalization API to configure AI Personalization.
Launching an A/B test
Dashboard interface for launching an A/B test
You can launch an A/B test directly from the AI Personalization dashboard. The process involves the following steps:
-
Select the index on which you want to A/B test personalization. This is the index for which search results will be compared. You only have access to indices that are configured to use AI Personalization.
-
Define the personalization re-ranking level for the A/B test. This is the level of personalization you want to A/B test against the personalization re-ranking from the configuration.
-
Set the percentage of traffic. This is the share of searches that will be boosted according to the personalization re-ranking level set.
-
Set the duration of the test. This is the maximum duration of time the A/B test will be live after you start it. For the best results, we recommend that you set a duration that is more likely to lead to high confidence in results.
-
Launch A/B test. This triggers the launch of the A/B test. Your users will now receive personalized search results based on your setup.
Viewing the results of an A/B test
You can view the results of all AI Personalization A/B tests in the A/B testing dashboard.
