  Online controlled experimentation is widely adopted for evaluating new
features in the rapid development cycle for web products and mobile
applications. Measurement of the overall experiment sample is a common practice
to quantify the overall treatment effect. In order to understand why the
treatment effect occurs in a certain way, segmentation becomes a valuable
approach to a finer analysis of experiment results. This paper introduces a
framework for creating and utilizing user behavioral segments in online
experimentation. By using the data of user engagement with individual product
components as input, this method defines segments that are closely related to
the features being evaluated in the product development cycle. With a
real-world example, we demonstrate that the analysis with such behavioral
segments offered deep, actionable insights that successfully informed product
decision-making.
