Meta launches a Facebook Organic A/B Testing API

Facebook this month launched a Facebook Organic A/B Testing API, an enterprise solution for developers to programmatically test their video content.

A/B Testing
A/B Testing

Facebook this month launched a Facebook Organic A/B Testing API, an enterprise solution for developers to programmatically test their video content.

The Facebook Organic A/B Testing API allows developers to test up to four variants of a new post against each other to see which one performs best. The winning post is then published on the developer's Page and distributed to their full audience.

Benefits for developers and creators

  • Capture Key Audience Insights That Inform Content Strategy: Users could be able to create more engaging videos that resonate with a diverse audience.
  • Scale Content Performance Programmatically: Empower users to improve content performance at low cost.

The Facebook Organic A/B Testing API allows developers to create and manage A/B tests for reels and videos on Facebook Pages, and is available for integration with content creation platforms, social media management platforms, and large publishers and enterprise creators. This API can be used to test different versions of a reel or video to see which one performs better.

Creating an A/B Test

To create an A/B test, users need to send a POST request to the /PAGE/ab_tests endpoint, where PAGE is the ID for the Facebook Page. The request body should include the following parameters:

  • name: The name of the A/B test
  • description: A description of the A/B test
  • optimization_goal: The goal of the A/B test. This can be one of the following:
    • AVG_TIME_WATCHED
    • COMMENTS
    • IMPRESSIONS
    • IMPRESSIONS_UNIQUE
    • LINK_CLICKS
    • REACTIONS
    • REELS_PLAYS
    • SHARES
    • VIDEO_VIEWS_60S
  • experiment_video_ids: The IDs of the reels or videos that you want to compare
  • control_video_id: The ID of the reel or video that will be used as the control. This is the reel or video that will be shown to all users if the A/B test is inconclusive.

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