Building and Applying an Experiment Platform: A/B Testing, System Integration, and Analysis

The article introduces the construction and application of an experiment platform, emphasizing A/B testing as a gold‑standard method for hypothesis validation and describing how the number of online experiments serves as a hidden metric of a company's scale.

Two main approaches for platform selection are discussed: purchasing third‑party solutions (e.g., Volcano Engine, Tencent, VWO, Google Optimize, Optimizely) for smaller user bases, and building a self‑hosted platform for large‑scale companies (e.g., Didi, Meituan, Alibaba, NetEase, Microsoft, Google) that require customized integration.

Beyond basic A/B tests, the article explores broader business applications, including product, backend, algorithm, and marketing experiments, and presents various experiment types such as flow‑based, joint, and cross‑business experiments, highlighting the importance of traffic, intervention, and analysis.

For faster system integration, the discussion covers experiment configuration distribution, flow execution services, parameter integration (variant‑based, key‑value, and configuration‑based controls), parameter priority, and experiment logging (flow logs for servers and trigger logs for clients), illustrating how logs support rapid analyst insights.

The final section focuses on rigorous experiment analysis, describing pre‑AA checks, SRM validation, and statistical tests (Welch’s T for mean‑type metrics, Z test for conversion rates) along with metric management practices, including key, guard, and OEC metrics, and three stages of metric governance: business‑driven, task‑driven, and event‑driven.

A Q&A segment addresses recommended literature, ensuring uniform traffic distribution in client‑side experiments, differences between strong and weak traffic perception, appropriate statistical methods for total‑value metrics, handling metric volatility, and strategies for evaluating numerous metrics simultaneously.