Enterprise Experimentation with Hierarchical Entities

TL;DR

This paper presents the design and implementation of the Enterprise Experimentation Platform at LinkedIn, addressing hierarchical entity challenges with innovative analysis and detection methods to improve enterprise product experimentation.

Contribution

It introduces a scalable experimentation platform that handles complex hierarchical entities and proposes new variance reduction and SSRM detection techniques.

Findings

Enhanced experimentation reliability through hierarchical SSRM detection

Improved analysis accuracy with taxonomy-based setup

Successful deployment demonstrating business impact

Abstract

In this paper, we address the challenges in running enterprise experimentation with hierarchical entities and present the methodologies behind the implementation of the Enterprise Experimentation Platform (EEP) at LinkedIn, which plays a pivotal role in delivering an intelligent, scalable, and reliable experimentation experience to optimize performance across all LinkedIn's enterprise products. We start with an introduction to the hierarchical entity relationships of the enterprise products and how such complex entity structure poses challenges to experimentation. We then delve into the details of our solutions for EEP including taxonomy based design setup with multiple entities, analysis methodologies in the presence of hierarchical entities, and advanced variance reduction techniques, etc. Recognizing the hierarchical ramping patterns inherent in enterprise experiments, we also propose a two-level Sample Size Ratio Mismatch (SSRM) detection methodology. This approach addresses SSRM at both the randomization unit and analysis unit levels, bolstering the internal validity and trustworthiness of analysis results within EEP. In the end, we discuss implementations and examine the business impacts of EEP through practical examples.