Blake McShane joined the marketing faculty at the Kellogg School of Management in 2010 as a Donald P. Jacobs Scholar. He has developed and applied statistical methodology to topics ranging from optimizing internet ad-serving algorithms to forecasting home runs in baseball. His specific research interests include Bayesian hierarchical modeling, statistical learning, and generalized Markov models. More generally, he seeks to develop statistical methods to accommodate the rich and varied data structures encountered in business problems and to use these methods to glean insight about individual behavior so as to test and supplement existing theories. Blake earned his PhD and MA in Statistics, MA and BA in Mathematics, and BS in Economics from the University of Pennsylvania .

• M.A., 2010, Statistics, Wharton School, University of Pennsylvania
• Ph.D., 2010, Statistics, Wharton School, University of Pennsylvania
• M.A., 2003, Mathematics, College of Arts and Sciences, University of Pennsylvania
• B.A., 2003, Mathematics, College of Arts and Sciences, University of Pennsylvania
• B.S., 2003, Economics, Wharton School, University of Pennsylvania

• Associate Professor, Marketing, Kellogg School of Management, Northwestern University, 2014-present
• Assistant Professor, Marketing, Kellogg School of Management, Northwestern University, 2011-2014
• Donald P. Jacobs Scholar, Marketing, Kellogg School of Management, Northwestern University, 2010-2011

• Marketing Science Institute Young Scholar
• Richard M. Clewett Professorship in Marketing, 2014-2016

• Editorial Board, Advances in Methods and Practices in Psychological Science, 2017
• Editorial Board, Psychological Bulletin, 2017
• Consulting Editor, Perspectives on Psychological Science, 2015-Present
• Associate Editor, Journal of American Statistical Association, 2013-Present

**This course was formerly known as MKTG-953-0**

We will use the statistics program R in Customer Analytics. R is harder to use that Stata but has become the industry standard (together with Python) and is extremely good for data management, visualization, and Machine Learning.