Bandit algorithms such as Thompson sampling (TS) have been put forth for decades as useful tools for conducting adaptively-randomised experiments. By skewing the allocation toward superior arms, they can substantially improve particular outcomes of interest for both participants and investigators. For example, they may use participants’ ratings for continuously optimising their experience with a program. However, most of the bandit and TS variants are based on either binary or continuous outcome models, leading to suboptimal performances in rating scale data. Guided by behavioural experiments we conducted online, we address this problem by introducing Multinomial-TS for rating scales. After assessing its improved empirical performance in unique optimal arm scenarios, we explore potential considerations (including prior’s role) for calibrating uncertainty and balancing arm allocation in scenarios with no unique optimal arms.