  Can we predict top-performing products, services, or businesses by only
monitoring the behavior of a small set of individuals? Although most previous
studies focused on the predictive power of "hub" individuals with many social
contacts, which sources of customer behavioral data are needed to address this
question remains unclear, mostly due to the scarcity of available datasets that
simultaneously capture individuals' purchasing patterns and social
interactions. Here, we address this question in a unique, large-scale dataset
that combines individuals' credit-card purchasing history with their social and
mobility traits across an entire nation. Surprisingly, we find that the
purchasing history alone enables the detection of small sets of ``discoverers"
whose early purchases offer reliable success predictions for the
brick-and-mortar stores they visit. In contrast with the assumptions by most
existing studies on word-of-mouth processes, the hubs selected by social
network centrality are not consistently predictive of success. Our findings
show that companies and organizations with access to large-scale purchasing
data can detect the discoverers and leverage their behavior to anticipate
market trends, without the need for social network data.
