  Complex network data problems are increasingly common in many fields of
application. Our motivation is drawn from strategic marketing studies
monitoring customer choices of specific products, along with co-subscription
networks encoding multiple purchasing behavior. Data are available for several
agencies within the same insurance company, and our goal is to efficiently
exploit co-subscription networks to inform targeted advertising of cross-sell
strategies to currently mono-product customers. We address this goal by
developing a Bayesian hierarchical model, which clusters agencies according to
common mono-product customer choices and co-subscription networks. Within each
cluster, we efficiently model customer behavior via a cluster-dependent mixture
of latent eigenmodels. This formulation provides key information on
mono-product customer choices and multiple purchasing behavior within each
cluster, informing targeted cross-sell strategies. We develop simple algorithms
for tractable inference, and assess performance in simulations and an
application to business intelligence.
