Inform Product Change through Experimentation with Data-Driven Behavioral Segmentation (2201.10617v1)

Abstract: 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.

• The paper presents a framework integrating behavioral segmentation into online controlled experiments to uncover variations in treatment effects across different user subgroups.
• The methodology involves selecting behavioral features, processing data with techniques like PCA, and using K-means clustering to define user segments.
• An empirical study on Yahoo Finance showed segmentation identified specific user groups negatively impacted by design changes, enabling targeted modifications and improved engagement metrics.

Inform Product Change through Experimentation with Data-Driven Behavioral Segmentation

The paper "Inform Product Change through Experimentation with Data-Driven Behavioral Segmentation," authored by Zhenyu Zhao, Yan He, and Miao Chen from Yahoo Inc., presents a comprehensive framework for improving product development through the integration of behavioral segmentation in online controlled experiments. The research addresses a fundamental challenge in A/B testing: understanding the underlying reasons behind treatment effects by segmenting user behavior.

Framework Overview

The central proposition of the paper is the use of user engagement data to derive behavioral segments that are tightly linked to the features evaluated during the product development cycle. Unlike traditional methods that aggregate testing results across all users, this segmentation approach facilitates a more nuanced analysis that can uncover variations in treatment effects across different user subgroups. This is achieved through a systematic process of identifying, clustering, and analyzing segments based on pre-defined behavioral attributes.

Methodology

The methodology involves several key steps:

1. Feature Selection: Behavioral features are defined based on user interactions with different product components to capture intents and preferences.
2. Data Processing: The authors employ feature engineering techniques, including orthogonalization, log transformation, and normalization, followed by dimensionality reduction using PCA to curate the input data for clustering.
3. Clustering: K-means clustering is used to divide users into distinct behavioral segments. The choice of k-means is justified by its scalability and flexibility, allowing it to be suitable for large-scale, diverse datasets.
4. Experimentation and Analysis: The segmented user data is used to measure treatment effects both at the segment level and overall, helping identify which segments drive treatment outcomes.

Empirical Application

An empirical case paper on a redesign of the Yahoo Finance website illustrates the framework's practical utility. The initial A/B test results indicated a significant decline in certain user engagement metrics (e.g., classic page views), prompting a deeper analysis by segment. Through the behavioral segmentation approach, specific user groups were identified that reacted negatively to design changes. This granular insight allowed the development team to make targeted modifications, leading to improved user engagement metrics in subsequent tests.

Implications and Future Directions

The paper acknowledges potential pitfalls in behavioral segmentation, such as inflated false positives and biases introduced when segments overlap with treatment effects. However, by designing segments independent of the experimentation treatment and pre-experiment, the framework mitigates these risks.

Future research could explore the integration of more advanced segmentation algorithms and how this framework could be adapted across different domains, beyond web products, to further enhance decision-making in product development cycles.

Conclusion

This paper provides a robust methodology for enriching controlled experimentation with behavioral segmentation, offering actionable insights that drive product iteration decisions. The comprehensive approach demonstrates the power of data-driven segmentation, not only in understanding user behavior but also in strategically guiding product development. The successful application in a real-world context exemplifies the practical relevance of this framework, forecasting broader adoption and evolution in experimentation practices.