Behavioral interventions are a valuable tool for policymakers to tackle persistent societal problems such as climate change. However, researchers have increasingly challenged predominant one-size-fits-all approaches that use the same interventions for everyone. I propose a scalable deep machine learning approach that personalizes behavioral interventions. The approach first predicts the effectiveness of behavioral interventions and then identifies the optimal intervention mix and timing for each individual. Understanding differences in individuals’ susceptibilities to interventions will enable researchers to offer more nuanced guidance to policymakers. Policymakers can use the proposed approach to trigger behavioral change more effectively and increase welfare.