Nicolas Padilla

Before joining London Business School, Dr Nicolas Padilla obtained his BSc in Industrial Engineering, a MSc in Operations Management from Universidad de Chile and a PhD Candidate in Marketing at Columbia Business School.

His research on using quantitative methods to inform marketing decisions focuses on studying the customer journey, and applying machine learning methods to infer consumers’ preferences and inform their customer management decisions.

Specifically, Dr Padilla studies 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 his 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, Dr Padilla applies Bayesian econometrics, probabilistic machine learning, and Bayesian non-parametric models to estimate customer preferences and guide firms' actions.