I joined Bocconi University in 2016 and my research lies in the field of quantitative marketing with a strong focus on empirical analysis of the inter-related decisions made by manufacturers, retailers and consumers.
I earned a PhD in marketing from London Business School, an MSc in economics from the London School of Economics, and a BA in economics from Sogang University. Prior to my doctoral studies, I gained industry experience in the marketing field at Unilever and Nielsen (KR) and I worked as a research fellow at International Growth Centre (UK).
Throughout the courses, I provide students with hands-on experience in analyzing various real-word data associated with marketing decision making.
My research efforts aim to apply theories and methods from economics and econometrics to interesting marketing problems. My early work investigates how grocers’ product assortment from multiple product categories in a common retail space influences consumers’ purchase decisions. The article has been published in Journal of Marketing and featured as “Journal Selection” by Marketing Science Institute in 2017.
My research domain has since been expanded to investigate; 1) firm’s product assortment decision as a response to a government intervention (e.g., taxation), 2) market competition in quality of products and services, and 3) a more complex set of supply and demand outcomes in a context of two-sided market. The empirical study of competition in product quality is particularly challenging as there is little reliable data available. To overcome this, my recent study extracts previously non-available information from various text data (e.g., online customer reviews) using latest text analysis methods.
Empirical contexts of my studies include students' choice over higher education, competition in fast-food industry, and the impact of policy shocks on the alcoholic beverage markets and on the two-sided markets such as crowdfunding platforms.
- Competition in product variety and quality
- Online reviews
- Two-sided markets
- Public policy
- Bayesian methods
- Natural language processing
- Machine learning