Research: Sequential Analysis in A/B Testing: Enhancing Conversion Rate Optimisation wi...

Research question: How does the integration of sequential analysis into A/B testing frameworks affect the statistical power and efficiency of conversion rate optimisation in digital marketing campaigns?

Background & Motivation: A/B testing is a cornerstone methodology in digital marketing for measuring the impact of website or campaign changes on key conversion metrics. Traditionally, these tests run for a fixed duration or until a predetermined sample size is reached, often delaying actionable insights and potentially increasing costs.

Research Gap / Question: While sequential analysis methods allow for interim data evaluation and early stopping, their application in marketing A/B testing remains underexplored. The core question is whether sequential analysis can improve the efficiency and reliability of conversion rate optimisation without inflating Type I error rates.

Approach & Expected Contribution: This project systematically reviews the literature on sequential analysis and A/B testing, develops a simulation-based and empirical framework, and compares traditional and sequential A/B methodologies using real or synthetic campaign data. Expected contributions include practical guidelines for marketers and statistical insights into error control, test duration, and resource allocation.

Why It Matters: Improving the speed and reliability of conversion optimisation directly impacts marketing ROI. The findings will inform digital marketers, analysts, and business decision-makers seeking to adopt more adaptive and cost-effective experimentation strategies.