Enterprise Experimentation with Hierarchical Entities
Shan Ba, Shilpa Garg, Jitendra Agarwal, Hanyue Zhao

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…
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Taxonomy
TopicsBusiness Process Modeling and Analysis
