Michael Braun

Associate Professor
Marilyn and Leo F. Corrigan Research Professor

PhD, Operation & Information Management, University of Pennsylvania
MBA, Duke University
BA, Economics, Princeton University


View CV
  • Bio

    Michael Braun, Associate Professor of Marketing at the Cox School of
    Business, joined the SMU community in 2013 after seven years on the
    faculty of the MIT Sloan School of Management.  The core of his
    research is the statistical analysis of large and complex customer
    databases, with an emphasis on customer lifetime value, advertising
    effectives, and public policy issues. He has written on, spoken on,
    and taught about management topics such as sales forecasting, customer
    retention and valuation, marketing ROI, social networking models,
    segmentation and targeting strategies, online advertising and
    insurance decisions.  Professor Braun’s work has been published in top
    academic publications such as Marketing Science, Management Science,
    and the Journal of the American Statistical Association, and he is a
    member of the Marketing Science Editorial Review Board. He has held
    several leadership positions for the American Statistical
    Association's Section on Statistics and Marketing, including Section
    Chair in 2011 and Program Chair in 2009, 2013 and 2015.  In addition,
    he is the author of four software packages for the R statistical
    computing language.

    Professor Braun’s teaching interests are to train the next generation
    of business leaders on how to analyze, interpret and use marketing
    data to address real-world managerial problems. He takes a hands-on
    approach with his classes, believing that managers cannot effectively
    act on the volumes of customer data that they collect, unless they
    master a foundational set of quantitative, statistical tools. He
    teaches the Managerial Statistics in the Cox School’s M.B.A. and other
    graduate management programs, as well as Customer Analytics Using
    Probability Models in the M.S. in Business Analytics program.

    Michael Braun earned his Ph.D. from the Wharton School of the
    University of Pennsylvania.  He holds an A.B. with Honors in Economics
    from Princeton University, and an M.B.A. from the Fuqua School of
    Business at Duke University.  Before entering academia, he worked on
    the development and deployment of broadband Internet products for such
    companies as Comcast, Marcus Cable and Charter Communications. From
    1999 to 2002, he was Vice President for Global Affiliate Operations of
    Chello Broadband, the Amsterdam-based Internet arm of United
    Pan-Europe Communications. He also worked as a production assistant at
    ESPN, and as a researcher for NBC at the 1992 Summer Olympics in

  • Teaching

    MKTG 6201 Marketing Management
    MAST 6252 Applied Predictive Analytics II
    MNGT 6210 Global Leadership Program

  • Research

    Customer loyalty, retention, and lifetime value
    Empirical analysis of legal and policy issues
    Advertising targeting and response
    Bayesian methods and computation for marketing research and analytics

  • Select Publications

    Braun, Michael, Bart De Langhe, Stefano Puntoni, and Eric Schwartz (2024). “Leveraging Digital Advertising Platforms for Consumer Research.” Journal of Consumer Research, 50. https://doi.org/10.1093/jcr/ucad058 

    Braun, Michael (2024). “Revisiting Scalable Targeted Marketing with Distributed Markov Chain Monte Carlo.” Journal of Marketing Research. Accepted for publication. 

    Turner, Jenia Iontcheva, Ronald Wright, and Michael Braun (2024). “Neglected Discovery.” Duke Law Journal, 73(6): 1173–1228. https://scholarship.law.duke.edu/dlj/vol73/iss6/1 .

    Braun, Michael, Jeremy Rosenthal, and Kyle Therrian (2018). “Police Discretion and Racial Disparity in Organized Retail Theft Arrests: Evidence from Texas.” Journal of Empirical Legal Studies, 15(4). https://rdcu.be/8vJZ

    Braun, Michael (2017). “sparseHessianFD: Estimating Sparse Hessian Matrices in R.” Journal of Statistical Software, 82(10):1–22. https://doi.org/10.18637/jss.v082.i10

    Braun, Michael and Paul Damien (2016). “Scalable Rejection Sampling for Bayesian Hierarchical Models.” Marketing Science, 35(3):427–444. https://doi.org/10/b4bd.

    Braun, Michael, David A. Schweidel, and Eli M. Stein (2015). “Transaction Attributes and Customer Valuation.” Journal of Marketing Research, 52(6):848–864. https://doi.org/10/b4bb .

    Braun, Michael (2014). “trustOptim: An R Package for Trust Region Optimization with Sparse Hessians.” Journal of Statistical Software, 60(4):1–16. https://doi.org/10/b4bc

    Braun, Michael and Wendy W. Moe (2013). “Online Display Advertising: Modeling the Effects of Multiple Creatives and Individual Impression Histories.” Marketing Science, 32(5):753–767. https://doi.org/10/b4bf 

    Braun, Michael and David A. Schweidel (2011). “Modeling Customer Lifetimes with Multiple Causes of Churn.” Marketing Science, 30(5):881–902. https://doi.org/10/ddv975


    Working Papers

    Braun, Michael and Eric M. Schwartz (2024).  "Where A-B Testing Goes Wrong: How Divergent Delivery Affects What Online Experiments Cannot (and Can) Tell You About How Customers Respond to Advertising.”   https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3896024

    Braun, Michael, Jenia I. Turner, and Ronald F. Wright (2024).  "Defense Use of Digital Discovery in Criminal Cases: A Quantitative Analysis.”

    Braun, Michael (2018) sparseMVN: An R Package for Multivariate Normal Functions with Sparse Covariance and Precision Matrices. https://braunm.github.io/sparseMVN/ .