Adaptive sampling in experiments with multiple waves can improve learning for “policy choice problems” where the goal is to select the optimal intervention or treatment among several options. This paper uses a real-world policy choice problem to demonstrate the advantages of adaptive sampling and propose solutions to common issues in applying the method. The application is a test of six formats for automated calls to parents in Kenya that encourage reading with children at home. The adaptive ‘exploration sampling’ algorithm is used to efficiently identify the call with the highest rate of engagement. Simulations show that adaptive sampling increased the posterior probability of the chosen arm being optimal from 86 to 93 percent and more than halved the posterior expected regret. The paper discusses a range of implementation aspects, including how to decide about research design parameters such as the number of experimental waves.