Bayesian Bandits: What color should that button be to convert more sales? What ad will most likely get clicked on? What movie recommendations should be displayed to keep subscribers engaged? What should we have for lunch? These are all examples of iterated decision problems — the same choice has to be made repeatedly with the goal being to arrive at an optimal decision strategy by incorporating the results of the previous decisions. In this talk I will describe the Bayesian Bandit solution to these types of problems, how it adaptively learns to minimize regret, how additional contextual information can be incorporated, and how it compares to the more traditional A/B testing solution.