Running experiments in a high-velocity marketplace environment involves a range of real-world challenges — from sample imbalance and session leakage to assignment logic and infrastructure limitations. This paper outlines hands-on practices used to improve experimentation reliability and decision-making speed. It highlights how assignment methods, cross-functional alignment, and strategic analysis play a critical role in producing valid, actionable results at scale.
Introduction
Experimentation plays a central role in product development for large-scale marketplaces. Rapid iteration depends on the ability to validate features, user experiences, and optimizations with measurable impact.