Massive transaction data sets are recorded in a routine manner in telecommunications, retail commerce, and Web site management. In this paper we address the problem of inferring predictive in- dividual proflles from such historical transaction data. We de- scribe a generative mixture model for count data and use an an approximate Bayesian estimation framework that efiectively com- bines an individual’s speciflc history with more general population patterns. We use a large real-world retail transaction data set to illustrate how these proflles consistently outperform non-mixture and non-Bayesian techniques in predicting customer behavior in out-of-sample data.