通过数据驱动的行为细分实验通知产品变更
Inform Product Change through Experimentation with Data-Driven Behavioral Segmentation
摘要：
在线受控实验被广泛用于评估Web产品和移动应用程序快速开发周期中的新功能。总体实验样本的测量是量化总体治疗效果的常见做法。为了理解为什么治疗效果以某种方式发生，分割成为一种有价值的方法来更精细地分析实验结果。本文介绍了一个框架，创建和利用用户行为段在线实验。通过使用用户与单个产品组件的交互数据作为输入，该方法定义了与产品开发周期中评估的功能密切相关的细分。通过一个真实的例子，我们证明了对这些行为细分的分析提供了深入的、可操作的见解，成功地为产品决策提供了信息。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
