I am Ph.D. candidate in Marketing at Columbia Business School.
As a marketing modeler, I apply Bayesian econometric and machine learning models to help firm’s infer consumers’ preference from information collected throughout the customer journey. Substantively, my main research interests are in understanding customer dynamics, leverage customer search and inform customer management decisions. My job market paper focuses on how firms can use the customer journey path from search to transaction as a source of information, to complement often thin historical data, in order to identify customers’ preferences, assess conversion likelihood, predict products' choice, and inform the firm on product recommendation. Another of my papers proposes a solution to the so-called “cold start” problem of customer management whereby firms need to manage just-acquired customers for whom the information is limited.
Methodologically, I apply Bayesian econometrics, probabilistic machine learning, and Bayesian non-parametric models to estimate customer preferences and guide firms' actions.
Email: firstname.lastname@example.orgColumbia Business School | Marketing Division