Advanced Search
MyIDEAS: Login

Scalable Inference of Customer Similarities from Interactions Data Using Dirichlet Processes

Contents:

Author Info

  • Michael Braun

    ()
    (MIT Sloan School of Management, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139)

  • André Bonfrer

    ()
    (School of Management, Marketing and International Business, Australian National University, Canberra, Australian Capital Territory 0200, Australia)

Registered author(s):

    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.

    Download Info

    If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
    File URL: http://dx.doi.org/10.1287/mksc.1110.0640
    Download Restriction: no

    Bibliographic Info

    Article provided by INFORMS in its journal Marketing Science.

    Volume (Year): 30 (2011)
    Issue (Month): 3 (05-06)
    Pages: 513-531

    as in new window
    Handle: RePEc:inm:ormksc:v:30:y:2011:i:3:p:513-531

    Contact details of provider:
    Postal: 7240 Parkway Drive, Suite 300, Hanover, MD 21076 USA
    Phone: +1-443-757-3500
    Fax: 443-757-3515
    Email:
    Web page: http://www.informs.org/
    More information through EDIRC

    Related research

    Keywords: social networks; nonparametric Bayes; Dirichlet processes; word of mouth; homophily; probability models; Bayesian networks;

    References

    No references listed on IDEAS
    You can help add them by filling out this form.

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as in new window

    Cited by:
    1. Shriver, Scott K. & Nair, Harikesh S. & Hofstetter, Reto, 2011. "Social Ties and User-Generated Content: Evidence from an Online Social Network," Research Papers 2083, Stanford University, Graduate School of Business.

    Lists

    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:inm:ormksc:v:30:y:2011:i:3:p:513-531. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Mirko Janc).

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.