Mr. Vijaya Chaitanya Palanki

Enhancing A/B testing Velocity through Advanced Statistical Models

Abstract

In today's digital landscape, the velocity of experimentation can make or break product innovation. Our research presents a comprehensive framework for enhancing A/B testing speed and efficiency using advanced statistical models. We explore how the integration of Bayesian methods, multi-armed bandits, and sequential analysis can dramatically reduce time-to-insight while maintaining statistical rigor.

Key contributions include:

• Novel approaches to reduce sample size requirements without compromising test validity
• Implementation frameworks for continuous monitoring using Bayesian methods
• Practical solutions for handling multiple variants simultaneously
• Techniques for incorporating historical data through empirical Bayes methods

Our findings demonstrate that organizations can significantly accelerate their experimentation cycles while maintaining high statistical confidence. We address common implementation challenges, computational requirements, and provide a decision framework for selecting appropriate models based on specific testing scenarios. The methodology has been validated across various digital experimentation contexts, offering practitioners a robust toolkit for faster, more efficient A/B testing processes.