Scenario

You work on a growth team testing multiple onboarding variants in a consumer fintech app. A stakeholder suggests using a multi-armed bandit instead of a standard A/B/n test so traffic can shift faster toward better-performing variants, but you are concerned about whether that is the right choice for learning and decision-making.

Question

How would you decide whether a multi-armed bandit approach makes sense for this growth experiment? What factors would make you prefer a bandit versus a fixed-horizon A/B/n test?