Data - Driven Design Optimization: A/B Testing in Large - Scale Applications

Satish Kathiriya, Suraj Kumar, Mahidhar Mullapudi

Abstract: In the rapidly evolving landscape of digital interface design, A/B testing has emerged as a crucial tool for optimizing user experience and business performance. This paper delves into the methodologies and impacts of A/B testing in large - scale applications, providing a comprehensive analysis of its role in enhancing user interface design. Through empirical data and case studies, the paper highlights the significance of A/B testing in informed decision - making and improving user interaction. The research methodology encompasses a blend of qualitative and quantitative approaches, offering insights into the practical challenges and opportunities in implementing A/B testing strategies. The findings underscore the importance of A/B testing in driving data - driven enhancements and its pivotal role in the design and development of high - traffic web applications. This paper serves as a valuable resource for designers, developers, and business strategists seeking to leverage A/B testing for optimal user engagement and business growth.

Keywords: A/B testing, large scale applications, Data - Driven Decision Making, Controlled Experimentation