Sequential methods can estimate the number of samples required for statistical inference while the sampling takes place. Digital experiments are often concerned with comparing website designs, algorithms or features and determining most effective for a business process. Addressing the needs of this task, we propose a sequential approach for comparing multiple treatments against a baseline simultaneously and we demonstrate its ability to control Type I and Type II errors according to user preference using a Monte Carlo simulation study.