Bayesian modelling of networks in complex business intelligence problems

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 behaviour. Data are available for several agencies within the same insurance company, and our goal is to exploit co-subscription networks efficiently to inform targeted advertising of cross-sell strategies to currently monoproduct customers. We address this goal by developing a Bayesian hierarchical model, which clusters agencies according to common monoproduct customer choices and co-subscription networks. Within each cluster, we efficiently model customer behaviour via a cluster-dependent mixture of latent eigenmodels. This formulation provides key information on monoproduct customer choices and multiple-purchasing behaviour 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.