Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes
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 similar...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Article |
| Language: | en_US |
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Institute for Operations Research and the Management Sciences (INFORMS)
2012
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| Online Access: | http://hdl.handle.net/1721.1/74624 |
| _version_ | 1826216438100131840 |
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| author | Braun, Michael Bonfrer, Andre |
| author2 | Sloan School of Management |
| author_facet | Sloan School of Management Braun, Michael Bonfrer, Andre |
| author_sort | Braun, Michael |
| collection | MIT |
| 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 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. |
| first_indexed | 2024-09-23T16:47:20Z |
| format | Article |
| id | mit-1721.1/74624 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T16:47:20Z |
| publishDate | 2012 |
| publisher | Institute for Operations Research and the Management Sciences (INFORMS) |
| record_format | dspace |
| spelling | mit-1721.1/746242022-09-29T21:30:28Z Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes Braun, Michael Bonfrer, Andre Sloan School of Management Braun, Michael 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. 2012-11-13T15:26:55Z 2012-11-13T15:26:55Z 2011-05 2009-05 Article http://purl.org/eprint/type/JournalArticle 0732-2399 1526-548X http://hdl.handle.net/1721.1/74624 Braun, M., and A. Bonfrer. “Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes.” Marketing Science 30.3 (2011): 513–531. en_US http://dx.doi.org/ 10.1287/mksc.1110.0640 Marketing Science Creative Commons Attribution-Noncommercial-Share Alike 3.0 http://creativecommons.org/licenses/by-nc-sa/3.0/ application/pdf Institute for Operations Research and the Management Sciences (INFORMS) arXiv |
| spellingShingle | Braun, Michael Bonfrer, Andre Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes |
| title | Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes |
| title_full | Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes |
| title_fullStr | Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes |
| title_full_unstemmed | Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes |
| title_short | Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes |
| title_sort | scalable inference of customer similarities from interactions data using dirichlet processes |
| url | http://hdl.handle.net/1721.1/74624 |
| work_keys_str_mv | AT braunmichael scalableinferenceofcustomersimilaritiesfrominteractionsdatausingdirichletprocesses AT bonfrerandre scalableinferenceofcustomersimilaritiesfrominteractionsdatausingdirichletprocesses |