Samuel Levy is an Assistant Professor of Business Administration at the Darden School of Business, where he teaches the marketing core course for the full-time MBA program. His research focuses on solving marketing problems using empirical methods, particularly in the areas of customer analytics, customer relationship management (CRM), data fusion, and privacy in marketing. He develops innovative methodologies such as digital marketing twins, leveraging probabilistic machine learning techniques to provide detailed, individual-level counterfactual insights regarding brand affinity and service performance from multiple data sources. Additionally, his work on privacy-preserving data fusion combines multiple datasets while ensuring user privacy, addressing the challenges of merging customer survey data with CRM databases in the US telecommunications industry. This approach enables marketers and researchers to understand customer satisfaction and run effective customer retention campaigns without compromising privacy.