%0 Journal Article
%T Bayesian modeling of networks in complex business intelligence problems
%A Daniele Durante
%A Sally Paganin
%A Bruno Scarpa
%A David B. Dunson
%J Statistics
%D 2015
%I arXiv
%X Complex network data problems are increasingly common in many fields of application. Our motivation is drawn from strategic marketing studies monitoring customer preferences for specific products, along with co-subscription networks encoding multi-buying behavior. Data are available for multiple agencies within the same insurance company, and our goal is to efficiently exploit co-subscription networks to inform targeted advertising of cross-selling campaigns to currently mono-product customers. We address this goal by developing a Bayesian hierarchical model, which groups agencies according to common customer preferences and co-subscription networks. Within each cluster, we efficiently model customer behaviors via a cluster-dependent mixture of latent eigenmodels. This formulation allows efficient targeting, while providing key information on mono- and multi-product buying behaviors within clusters, informing cross-selling marketing campaigns. We develop simple algorithms for tractable inference, and assess the performance in simulations and an application to business intelligence.
%U http://arxiv.org/abs/1510.00646v1