Several empirical works have shown that social influence propagates beyond direct neighbors in relatively costless online decision-making settings mainly due to the exposure effect explained by simple or complex contagion. Yet precisely how influence plays a role in costly offline behaviors and propagates through a social network and especially what is the underlying mechanisms that drive such propagation remain unclear. In this study, we leverage a high-resolution mobile phone data and a new behavioral matching method, based on revealed preference theory, to study how social influence propagates and affects individual off-line behavior. Our results show that propagation within the network persists in shaping individual decision-making through up to more than three degrees of separation regarding attending an international cultural performance in Andorra degrees of separation and regarding visiting a newly opened retail store in Mérida, Mexico. We find that exposure to behavior does not suffice to explain this social influence’s ripple effect, but it can be rather explained by a compound mechanism based on local communication and information aggregation. Based on those ideas we propose a Bayesian network-based learning model that can better predict individual adoption behavior than exposure-based models. This means that social learning is a paramount ingredient to understand the diffusion of influence in social networks, and it might have far-reaching implications in such domains as viral marketing, public health management, political campaign, and social mobilization, where it is often desirable to trigger a costly behavioral change in large-scale populations. Shorter version.