论文标题
通过通过数据驱动的行为细分实验来告知产品更改
Inform Product Change through Experimentation with Data-Driven Behavioral Segmentation
论文作者
论文摘要
在线控制的实验被广泛采用，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整体治疗效果的常见实践。为了了解为什么以某种方式发生治疗效果，分割成为对实验结果进行更精细分析的宝贵方法。本文介绍了一个在线实验中创建和利用用户行为段的框架。通过将用户参与单个产品组件的数据作为输入，该方法定义了与产品开发周期中评估的功能密切相关的段。通过一个现实世界的例子，我们证明了此类行为片段的分析提供了深刻，可操作的见解，这些见解成功地了解了产品决策。
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.
