Designing Scalable Multivariate Testing ‎Frameworks for High-Traffic E-Commerce ‎Platforms

  • Authors

    • Eshita Gupta University of Tampa
    https://doi.org/10.14419/47mq5944

    Received date: September 2, 2025

    Accepted date: September 12, 2025

    Published date: December 10, 2025

  • Multivariate Testing; E-commerce Optimization; Scalability; Experimentation Framework; ‎Statistical Analysis

  • Abstract

    Abstract: High-traffic e-commerce outlets use experimentation to perfect the user experience and, in ‎turn, conversions and revenue. Multivariate testing, however, acts as an elegant solution, ‎considering changes occurring in multiple variables and their interaction effects simultaneously, ‎thus generating richer insights than in the scenario of an A/B test. Nonetheless, the implementation ‎of a scalable multivariate testing framework on such large platforms harbors considerable ‎architectural challenges, data infrastructure issues, and, most importantly, statistical and operational ‎integration concerns. This paper covers complete principles, system design considerations, and ‎statistical methods that guide the construction of engineering-grade privacy-compliant ‎experimentation systems that support millions of concurrent users. By considering architecture ‎approaches, data pipelines, performance improvements, and integration with personalization, ‎inventory, and marketing systems, current best practices for performing experimentation as a ‎fundamental operational capability are offered. Topics discussed in case studies offer evidence-‎based stories on successes and pitfalls on the canvas, emerging trends such as reinforcement ‎learning and privacy-preserving analytics that will dictate the future of experimentation in e-‎commerce‎.

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  • How to Cite

    Gupta, E. . (2025). Designing Scalable Multivariate Testing ‎Frameworks for High-Traffic E-Commerce ‎Platforms. International Journal of Basic and Applied Sciences, 14(8), 167-173. https://doi.org/10.14419/47mq5944