Experimentation in online digital platforms is often used to inform decision making. Specifically, the goal of many experiments is to optimize a metric of interest. Null hypothesis statistical testing can be ill-suited to this task, as it is indifferent to the magnitude of effect sizes and opportunity costs. Moreover, in practice, related experiments can often be grouped into portfolios called experimentation programs where the goal is to optimize a single metric. Past tests in an experimentation program inform useful priors on treatment effects. In these settings, we discuss how experimentation practice should change when the goal is optimization. We survey the literature on Bayesian and empirical Bayes analyses of A/B test portfolios, and single out the A/B Testing Problem [Azevedo et al., 2020] as a starting point, which treats experimentation as a constrained optimization problem. We emphasize that the framework can be implemented by appropriately tuning p-value thresholds, a result which seems under-utilized by practitioners. Furthermore, we develop several extensions of the A/B testing problem to managing multiple experimentation programs, managing a single program over time, and allowing units to be enrolled in multiple tests. Each of these can be solved by dynamic programming, and together they extend the utility of the framework in practice. We also consider the case of new experimentation programs without a strong prior on treatment effects. Finally, we discuss the implications of these results on experimentation programs in industry. For example, under no-cost assumptions, firms should be testing many more ideas, reducing test allocation sizes, and relaxing p-value thresholds away from p = 0.05. These results are exemplified with an application to data from Netflix experimentation programs. A draft of the paper may be found at https://arxiv.org/abs/2412.05508.