Seung-Wook Kim

Seung-Wook Kim is an Assistant Professor of Marketing at Bentley University. He is an empirical modeler interested in marketing analytics, digital marketing, and unstructured data (image and text) analysis with strong technical foundations in machine learning and statistics. His research goal has been to utilize recent advances in machine learning to explore marketing-related problems that cannot be solved through traditional models and provide novel information that generates insights for managers and consumers. He previously taught Marketing Analytics and Statistics for Business at the University of Iowa.

Education

• Ph.D. University of Iowa
• MS University of Iowa
• BS Seoul National University

Teaching Interests

Marketing Analytics, Digitial Marketing, Marketing Research

Research Interests

Marketing Analytics, Digital Marketing, User-Generated Content, Choice Modeling, Interpretable Machine Learning, Causal Inference

Awards and Honors

• 2022, Tippie College of Business Research Excellence Grant, University of Iowa
• 2021, Graduate College Post-Comprehensive Research Award, University of Iowa
• 2021, Graduate College Summer Research Award, University of Iowa
• 2019, ALLEN T. CRAIG Award for Teaching Excellence, University of Iowa

Presentations

• Moon, S., Kim, S., Iacobucci, D. (2023). “Do the Relationships between Product Reviewers and Consumers Change with Time?: The Evolving Impacts of Reviewers” Presented at the 45th ISMS Marketing Science Conference
• Kim, S., Russell, G. (2023). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the 45th ISMS Marketing Science Conference
• Kim, S., Russell, G. (2022). “Uncovering Consumer Heterogeneity in Big Data: A Hybrid Marketing Science – Deep Learning Approach” Presented at the Seoul National University Seminar
• Kim, S., Gruca, T., Lee, H. (2022). “Transformative Consumer Research in the Age of Algorithms: Consumer Credit Score Insights from Interpretable Machine Learning” Presented at the Boulder Summer Conference on Consumer Financial Decision Making
• Kim, S., Gruca, T., Lee, H. (2022). “Transformative Consumer Research in the Age of Algorithms: Consumer Credit Score Insights from Interpretable Machine Learning” Presented at the INFORMS Iowa Knowledge Workshop
• Kim, S., Russell, G. (2022). “Deep Learning Based Choice Model for Market Segmentation” Presented at the Seoul National University Seminar
• Kim, S., Gruca, T., Lee, H. (2021). “Using Interpretable Machine Learning to Understand Consumer Credit Scores” Presented at the 43rd ISMS Marketing Science Conference
• Kim, S. (2021). “Application of Machine Learning Algorithms to Marketing Research” Presented at the State University of New York- New Paltz Machine Learning Seminar
• Kim, S., Gruca, T., Lee, H. (2021). “Using interpretable machine learning to understand consumer credit score” Presented at the State University of New York- New Paltz Machine Learning Seminar
• Kim, S., Gruca, T., Lee, H. (2020). “Using Interpretable Machine Learning to Understand Consumer Credit Scores” Presented at the NYU-Temple-CMU Conference on Artificial Intelligence, Machine Learning, and Business Analytics