Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes

Forthcoming in Marketing Science

40 Pages Posted: 21 May 2009 Last revised: 20 Aug 2011

See all articles by Michael Braun

Michael Braun

Southern Methodist University (SMU) - Marketing Department

Andre Bonfrer

Deakin University; Australian National University (ANU)

Date Written: December 21, 2010

Abstract

Under the sociological theory of homophily, people who are similar to one another are more likely to interact with one another. Marketers often have access to data on interactions among customers from which, with homophily as a guiding principle, inferences could be made about the underlying similarities. However, larger networks face a quadratic explosion in the number of potential interactions that need to be modeled. This scalability problem renders probability models of social interactions computationally infeasible for all but the smallest networks. In this paper we develop a probabilistic framework for modeling customer interactions that is both grounded in the theory of homophily, and is flexible enough to account for random variation in who interacts with whom. In particular, we present a novel Bayesian nonparametric approach, using Dirichlet processes, to moderate the scalability problems that marketing researchers encounter when working with networked data. We find that this framework is a powerful way to draw insights into latent similarities of customers, and we discuss how marketers can apply these insights to segmentation and targeting activities.

Keywords: Dirichlet processes, nonparametric Bayes, social networking, homophily, diffusion, word of mouth

JEL Classification: M31, C11, C44, C5

Suggested Citation

Braun, Michael and Bonfrer, Andre, Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes (December 21, 2010). Forthcoming in Marketing Science. Available at SSRN: https://ssrn.com/abstract=1407866

Michael Braun (Contact Author)

Southern Methodist University (SMU) - Marketing Department ( email )

United States

Andre Bonfrer

Deakin University ( email )

Australia

Australian National University (ANU) ( email )

Canberra, Australian Capital Territory 2601
Australia

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