Bayesian modeling of networks in complex business intelligence problems

TL;DR

This paper introduces a Bayesian hierarchical model for analyzing complex co-subscription networks in business intelligence, enabling targeted marketing strategies by clustering agencies and modeling customer behavior.

Contribution

The paper develops a novel Bayesian hierarchical clustering approach combined with latent eigenmodels for co-subscription networks, tailored for business intelligence applications.

Findings

Effective clustering of agencies based on customer choices

Accurate modeling of multi-product purchasing behavior

Improved targeted advertising strategies

Abstract

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.