Multi-armed bandit
A multi-armed bandit allocates traffic adaptively toward variants that are performing well while continuing to sample the others. It reduces the cost of testing inferior options, at the price of a less clean estimate of each variant's true effect.
Why it matters for ARPU
Bandits suit high-volume, low-stakes choices such as creative selection. Pricing, incentives and retention offers usually deserve a clean experiment instead.
Related terms
A/B testAn A/B test randomly assigns customers to two or more variants and compares a pre-declared metric between them.Reinforcement learning for marketingReinforcement learning treats customer decisions as a sequence, optimising cumulative long-term reward rather than the next click.Sequential testingSequential testing allows results to be monitored continuously and stopped early without inflating false positives, using always-valid confidence intervals instead of a single fixed-horizon check.Novelty effectThe novelty effect is a temporary response lift caused by a change being new rather than better.