Nicolas Padilla

Nicolas Padilla is Assistant Professor of Marketing at London Business School. His research develops probabilistic machine learning methods for marketing problems, organised around two main streams. The first investigates preference measurement and customer journey modelling: how firms can overcome the cold-start problem to infer consumer preferences under data scarcity, and how behavioural sequences along the customer journey serve as a source of information about latent preferences. The second addresses modern marketing measurement, with a focus on the identification of nonlinear and time-varying effects in marketing mix models and the design of unified measurement frameworks that incorporate experimental evidence. Beyond these streams, his work extends to the effects of large language model adoption on online user behaviour and to preference formation in non-market settings. Methodologically, Nicolas' research is grounded in Bayesian econometrics and probabilistic machine learning.

Nicolas Padilla holds a PhD in Quantitative Marketing from Columbia Business School, where he developed expertise in advanced analytical methods. He also earned an MSc in Operations Management and a BSc in Engineering Science from Universidad de Chile, reflecting a strong foundation in quantitative disciplines that informs his research and teaching at London Business School.