Accelerating our A/B experiments with machine learning

This post introduces the concept of Expected Revenue (XR) as a machine learning-driven metric for analyzing A/B experiments at Dropbox. It details how XR is calculated using Gradient Boosted Trees in TensorFlow to predict a user's two-year lifetime value, accounting for conversion probability, retention, and plan switching. The system leverages Airflow for orchestration, Hadoop on AWS for the backend, Hive for data storage, and Spark for evaluation. A key contribution is the method for calculating XR lift as a ratio to reduce systematic uncertainty, making experimental conclusions more reliable.