Our approach to research and A/B testing

Our VP of Engineering and Head of Data and AI Ya Xu shared the following post on our Engineering Blog about our approach to research and A/B testing. 

We are constantly striving to improve the experience on LinkedIn for our members and customers, with research and experimentation, such as A/B Testing, playing a key role in that work. 

Nearly a decade ago, I discussed the importance of these techniques in our journey to create economic opportunity for every member of the global workforce. Today we have a strong principled approach to how we design and run A/B tests on everything from UI designs to AI algorithms, and feature launches to bug fixes. As our platform continues to grow and evolve, these techniques have become even more essential for us to deliver on our vision of creating economic opportunity for every member of the global workforce. More specifically, we use these techniques to:

  • Deliver the best experiences to our community by leveraging innovation at scale. Through testing and measuring, we continuously evolve our products to add more value, making our platform safer, more engaging and more enjoyable with every interaction. We use various methods to evolve our products and services from member surveys to in-depth offline data analysis to online A/B tests when we have a new feature that we think will benefit our members. For example, we recently tested new ways to help members discover relevant news, conversations, and voices from people and organizations they might not otherwise know. 

  • Avoid guessing; instead test, measure and test again. We don't assume that we inherently know what is best for our members, as their needs evolve over time. By testing and constantly measuring, we seek feedback and insights to help guide us in the right direction. If a product feature we build doesn’t deliver the impact we intended, we make adjustments.

  • Move quickly and thoughtfully. Our well-defined process includes design evaluations, committee reviews, and quality checks aimed at preventing unintended consequences. We also use observational causal studies to analyze historical data and discover causal patterns whenever applicable. The development of our T-REX platform has also standardized and improved our A/B testing processes. 

Throughout all of this, we believe in the importance of sharing knowledge. We regularly share insights from our tests with the broader engineering community through papers, open source, and academic partnerships.

We are proud to have developed a culture at LinkedIn where research and experimentation is celebrated. Not only does this work help us stay innovative, it reminds us that everything we do is in service of our community, and is to create more economic opportunities for our members around the world.