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Scalable inference of customer similarities from interactions data using Dirichlet processes

journal contribution
posted on 2011-05-01, 00:00 authored by M Braun, Andre BonfrerAndre Bonfrer
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.

History

Journal

Marketing science

Volume

30

Issue

3

Pagination

513 - 531

Publisher

The Institute of Management Sciences and the Operations Research Society of America

Location

Providence, R.I.

ISSN

0732-2399

eISSN

1526-548X

Language

eng

Publication classification

C Journal article; C1.1 Refereed article in a scholarly journal

Copyright notice

2011, INFORMS

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