Mingyung Kim

Mingyung Kim
  • Doctoral Student

Contact Information

  • office Address:

    727.4 Jon M. Huntsman Hall
    3730 Walnut Street
    University of Pennsylvania
    Philadelphia, PA 19104


Mingyung Kim is a Ph.D. Candidate in Marketing at the Wharton School of the University of Pennsylvania. She is primarily interested in developing novel extensions of statistical and machine learning methods to address a broad array of marketing problems. These problems include statistical method issues in marketing, open issues arising due to the availability of new data types, and their implications for marketing decision makers. To tackle these issues, she adapts and extends modern Bayesian nonparametric and machine learning methods. She received M.A. in Statistics from the University of California at Berkeley and B.B.A. and B.A. in Applied Statistics from Yonsei University, Seoul, South Korea. 

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  • Mingyung Kim, Eric Bradlow, Raghuram Iyengar (Under Review), A Bayesian Dual-Network Clustering Approach for Selecting Data and Parameter Granularities.

    Abstract: While there are well-established methods for model selection (e.g., BIC, marginal likelihood), they generally condition on an a priori selected data (e.g., SKU-level data) and parameter granularity (e.g., brand-level parameters). That is, researchers think they are doing model selection, but what they are really doing is model selection conditional on their choices of data and parameter granularities. In this research, we propose a Bayesian dual-network clustering method as a novel way to make these two decisions simultaneously. To accomplish this, the method represents data and parameters as two separate networks with nodes being the unit of analysis (e.g., SKUs). The method then (a) clusters the two networks using a covariate-driven distance function which allows for a high degree of interpretability and (b) infers the data and parameter granularities that offer the best in-sample fit, akin to standard model selection methods. We apply our method to SKU-level demand analysis. The results show that the choices of data and parameter granularities based on our method as compared to those from extant approaches (e.g., latent class analysis) impact the demand elasticities and the optimal pricing of SKUs. We conclude by highlighting the generalizability of our framework to a broad array of marketing problems.

  • Mingyung Kim, Eric Bradlow, Raghuram Iyengar (2022), Selecting Data Granularity and Model Specification Using the Scaled Power Likelihood with Multiple Weights, Marketing Science, 41, pp. 848-866.


All Courses

  • MKTG1010 - Intro To Marketing

    The objective of this course is to introduce students to the concepts, analyses, and activities that comprise marketing management, and to provide practice in assessing and solving marketing problems. The course is also a foundation for advanced electives in Marketing as well as other business/social disciplines. Topics include marketing strategy, customer behavior, segmentation, customer lifetime value, branding, market research, product lifecycle strategies, pricing, go-to-market strategies, promotion, and marketing ethics.


Latest Research

Mingyung Kim, Eric Bradlow, Raghuram Iyengar (Under Review), A Bayesian Dual-Network Clustering Approach for Selecting Data and Parameter Granularities.
All Research