Nicolas Padilla

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: np2506@columbia.edu

Columbia Business School | Marketing Division

Research

Working Papers

Research in Progress

Curriculum Vitae

Education

Expected 2020 Ph.D. in Marketing, Columbia Business School, US
2018 MPhil. in Marketing, Columbia Business School, US
2014 MSc. in Operations Management, University of Chile, Chile
2014 Industrial Engineering, University of Chile, Chile
2011 BSc. in Engineering Science, University of Chile, Chile

Teaching Experience

Fall 2014 Marketing II (Probability models)
Industrial Engineering, University of Chile

Professional Experience

2013–14 Center for Retail Studies (CERET), University of Chile, Santiago, Chile
Director of Studies
2011 Enjoy SA (Casinos and Hotels), Santiago, Chile
Digital Project Analyst

Columbia Business School

3022 Broadway

Uris Hall, room 5 East

New York, NY 10027-6902

np2506@columbia.edu