We analyze data from a service guarantee program implemented by a mid-priced hotel chain. Using a multi-site regression discontinuity quasi-experimental design developed for these data from 85,321 guests and 81 hotels over 16 months, we control for unobserved heterogeneity among guests and treatments across hotels, and develop Bayesian posterior estimates of the varying program effect for each hotel. Our results contribute to theory and practice. First, we provide new insights into how service guarantee programs operate in the field. Specifically, we find that the service guarantee was more effective at hotels with a better prior service history and an easier-to-serve guest population, both of which are consistent with signaling arguments, but do not comport with the incentive argument that guarantees actually improve service quality. Second, our study offers managers better decision rules. Specifically, we devise program continuation rules that are sensitive to both observed and unobserved differences across sites. In addition, we devise policies to reward hotels for exceeding site-specific expectations. By controlling for observed and unobserved differences across sites, these policies potentially reward even sites with negative net program effects, which is useful in reducing the organizational stigma of failure. Finally, we identify sites that should be targeted for future program rollout by computing their odds of succeeding.