CUPED, short for Controlled-experiment Using Pre-Experiment Data, is a method used to increase the velocity of experimentation by reducing the uncertainty in estimates of experiment uplift. It is a feature of GrowthBook, an open-source feature flagging and experimentation platform. The primary reason to use CUPED is that it decreases the variance of experiment uplift, thereby increasing the accuracy of your experimental results and the speed at which you can see the effects of an experiment. In the right conditions, using CUPED can equate to getting 20% or more traffic during your experiment. For instance, Netflix reported that CUPED reduced variance by roughly ~40% for some key engagement metrics in 2016. In 2022, Microsoft reported that, for one product team, CUPED was akin to adding 20% more traffic to the analysis of a majority of metrics. CUPED works by using pre-experiment data to fit a simple model to predict the outcome metric and then uses those predictions to adjust the metric. The adjusted metric tends to have lower variance. The more correlated the pre-experiment data is with your metric of interest, the more variance reduction you can achieve. In simpler terms, if you know a particular user tends to buy a lot of products on your website before you launch an experiment, you can use that information to understand whether purchase behavior after an experiment is driven by that customer's innate tendency to buy a lot of products or whether it was due to the experiment. You can read more about CUPED in the [GrowthBook documentation](https://docs.growthbook.io/statistics/cuped).