Making data driven decisions through experimentation is an extremely important part of the culture at LinkedIn. It’s deeply ingrained in our development process and has always been a core part of Linkedin’s DNA. We test everything from complete redesigns of our homepage, to back-end relevance algorithm changes and even infrastructure upgrades. It’s how we innovate, grow and evolve our products to best serve our members. It’s how we make our members happier, our business stronger and our talent more productive.
We have built an internal end-to-end A/B testing platform, called XLNT, to quickly quantify the impact of any A/B test in a scientific and controlled manner across LinkedIn.com and our apps. XLNT allows for easy design and deployment of experiments, but it also provides automatic analysis that is crucial in popularizing A/B tests. The platform is generic and extensible, covering almost all domains including mobile and email. Every day, over 300 experiments are run and 1000s of metrics computed to improve every aspect of LinkedIn,
LinkedIn’s experimentation efforts do not stop at this. Scientists and engineers are constantly looking for rigorous ways to achieve better experimentation. For example, to better understand network influence in experiments, to measure long-term impact, and to automatically identify insights on why metrics move. These problems are extremely exciting and challenging at the scientific and engineering levels, but more importantly are key to the continued success of LinkedIn.