Bayesian Modelling of Networks in Complex Business Intelligence Problems
针对保险公司多产品客户数据，提出贝叶斯分层模型，通过聚类分析客户选择与共订阅网络，为单一产品客户制定精准交叉销售策略。
Summary 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.