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Scalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes

Braun, Michael; Bonfrer, Andre

Description

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...[Show more]

dc.contributor.authorBraun, Michael
dc.contributor.authorBonfrer, Andre
dc.date.accessioned2015-12-07T22:27:36Z
dc.identifier.issn0732-2399
dc.identifier.urihttp://hdl.handle.net/1885/21961
dc.description.abstractUnder 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.
dc.publisherInstitute for Operations Research and the Management Sciences (INFORMS)
dc.sourceMarketing Science: the marketing journal of INFORMS
dc.subjectKeywords: Bayesian networks; Dirichlet processes; Homophily; Nonparametric bayes; Probability models; Social networks; Word of mouth
dc.titleScalable Inference of Customer Similarities from Interactions Data using Dirichlet Processes
dc.typeJournal article
local.description.notesImported from ARIES
local.identifier.citationvolumeOnline
dc.date.issued2010
local.identifier.absfor150505 - Marketing Research Methodology
local.identifier.ariespublicationu4602557xPUB19
local.type.statusPublished Version
local.contributor.affiliationBraun, Michael, Massachusetts Institute of Technology
local.contributor.affiliationBonfrer, Andre, College of Business and Economics, ANU
local.description.embargo2037-12-31
local.bibliographicCitation.startpage40
local.identifier.doi10.1287/mksc.1110.0640
local.identifier.absseo890104 - Mobile Telephone Networks and Services
dc.date.updated2016-02-24T11:12:58Z
local.identifier.scopusID2-s2.0-79957629269
CollectionsANU Research Publications

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