Bayesian Modelling of Networks in Complex Business Intelligence Problems. Issue 3 (27th July 2016)

• Record Type: Journal Article
• Authors: Durante, Daniele; Paganin, Sally; Scarpa, Bruno; Dunson, David B.
• Is Part Of: Journal of the Royal Statistical Society. Volume 66:Issue 3(2017:May)
• Page Start: 555
• Page End: 580
• Publication Date: 2016-07-27
• Subjects: Business intelligence -- Chinese restaurant process -- Co-subscription networks -- Cross-sell marketing strategies -- Mixture of latent eigenmodels -- Monoproduct choices; Statistics -- Periodicals; 519.5
• DOI: 10.1111/rssc.12168
• Languages: English
• ISSNs: 0035-9254

Abstract

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.