Randomized experiments with treatment and control groups are an important tool to measure the impacts of interventions. However, in experimental settings with one-sided noncompliance, extant empirical approaches may not produce the estimands a decision-maker needs to solve their problem of interest. For example, these experimental designs are common in digital advertising settings, but typical methods do not yield effects that inform the intensive margin -- how many consumers should be reached or how much should be spent on a campaign. We propose a solution that combines a novel multi-cell experimental design with modern estimation techniques that enables decision-makers to recover enough information to solve problems with an intensive margin. Our design is straightforward to implement and does not require any additional budget to be carried out. We illustrate our approach through a series of simulations that are calibrated using an advertising experiment at Facebook, finding that our method outperforms standard techniques in generating better decisions.