This article discusses the diffusion process in an online social network given the individual connections between members. The authors model the adoption decision of individuals as a binary choice affected by three factors: (1) the local network structure formed by already adopted neighbors, (2) the average characteristics of adopted neighbors (influencers), and (3) the characteristics of the potential adopters. Focusing on the first factor, the authors find two marked effects. First, an individual who is connected to many adopters has a greater adoption probability (degree effect). Second, the density of connections in a group of already adopted consumers has a strong positive effect on the adoption of individuals connected to this group (clustering effect). The article also records significant effects for influencer and adopter characteristics. For adopters, specifically, the authors find that position in the entire network and some demographic variables are good predictors of adoption. Similarly, in the case of already adopted individuals, average demographics and global network position can predict their influential power on their neighbors. An interesting counterintuitive finding is that the average influential power of individuals decreases with the total number of their contacts. These results have practical implications for viral marketing, a context in which a variety of technology platforms are increasingly considering leveraging their consumers' revealed connection patterns. The model performs particularly well in predicting the next set of adopters.