Blake McShane
Blake McShane

MARKETING
Associate Professor of Marketing

Print Overview

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 .



Areas of Expertise
Bayesian Modeling
Data Analysis
Database Marketing
Marketing Research

Print Vita
Academic Positions
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

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

Print Research
Research Interests
Bayesian hierarchical modeling; statistical learning; generalized Markov models; probability models for marketing; developing new methodology for unique data structures with application to business problems

Articles
McShane, Blake and Ulf Bockenholt. 2017. Single Paper Meta-analysis: Benefits for Study Summary, Theory-testing, and Replicability. Journal of Consumer Research. 43(6): 1048-1063.
McShane, BlakeUlf Bockenholt and Karsten Hansen. 2016. Adjusting for Publication Bias in Meta-analysis: An Evaluation of Methods and Some Cautionary Notes. Perspectives on Psychological Science. 11(5): 730-749.
McShane, Blake, Chaoqun Chen, Eric T. Anderson and Duncan Simester. 2016. Decision Stages and Asymmetries in Regular Retail Price Pass-through. Marketing Science. 35(4): 619-639.
McShane, Blake and David Gal. 2016. Blinding Us to the Obvious? The Effect of Statistical Training on the Evaluation of Evidence. Management Science. 62(6): 1707-1718.
McShane, Blake and Ulf Bockenholt. 2016. Planning Sample Sizes When Effect Sizes Are Uncertain: The Power-Calibrated Effect Size Approach. Psychological Methods. 21(1): 47-60.
Rucker, Derek D.Blake McShane and Kristopher Preacher. 2015. A Researcher's Guide to Regression, Discretization, and Median Splits of Continuous Variables. Journal of Consumer Psychology. 25(4): 666-678.
McShane, Blake and Ulf Bockenholt. 2014. You Cannot Step in the Same River Twice: When Power Analyses are Optimistic. Perspectives on Psychological Science. 9(66): 612-625.
Bockenholt, Ulf and Blake McShane. 2014. Comments on: Latent Markov Models: A Review of the General Framework for the Analysis of Longitudinal Data with Covariates. TEST. 23(3): 469-472.
McShane, Blake, S. Jensen, A. J. Wyner and Pack A.I.. 2013. Rejoinder: Statistical Learning with Time Series Dependence: An Application to Scoring Sleep in Mice. Journal of the American Statistical Association. 108(504): 1165-1172.
McShane, Blake, S. T. Jensen, A. J. Wyner and Pack A.I.. 2013. Statistical Learning with Time Series Dependence: An Application to Scoring Sleep in Mice. Journal of the American Statistical Association. 108(504): 1147-1162.
McShane, Blake, Eric Bradlow and J. Berger. 2012. Visual Influence and Social Groups. Journal of Marketing Research. 49: 854-871.
McShane, Blake, O. P. Watson, T. Baker and S. J. Griffith. 2012. Predicting Securities Fraud Settlements and Amounts: A Hierarchical Bayesian Model of Federal Securities Class Action Lawsuits. Journal of Empirical Legal Studies. 9(3): 482-510.
Gal, David and Blake McShane. 2012. Can Fighting Small Battles Help Win the War? Evidence from Consumer Debt Management. Journal of Marketing Research. 49: 487-501.
Naidoo, N., M. Ferber, R. J. Galante, Blake McShane, J. H. Hu, J. Zimmerman, G. Maislin, J. Cater, A. J. Wyner, P. Worley and Pack A.I.. 2012. Role of Homer Proteins in the Maintenance of Sleep-Wake States. PLoS ONE. 7(4): e35174. doi:10.1371/journal.pone.0035174.
McShane, Blake, R. J. Galante, M. P. Biber, S. Jensen, A. J. Wyner and Pack A.I.. 2012. Assessing REM Sleep in Mice Using Video Data. Sleep. 35(3): 433-442.
McShane, Blake, A. Braunstein, J. Piette and S. Jensen. 2014. A Hierarchical Bayesian Variable Selection Approach to Major League Baseball Hitting Metrics. Journal of Quantitative Analysis in Sports. 7(4): 2.
McShane, Blake and A. J. Wyner. 2011. Rejoinder: A Statistical Analysis of Multiple Temperature Proxies: Are Reconstructions of Surface Temperatures Over the Last 1000 Years Reliable?. Annals of Applied Statistics. 5(1): 99-123.
McShane, Blake and A. J. Wyner. 2011. A Statistical Analysis of Multiple Temperature Proxies: Are Reconstructions of Surface Temperatures Over the Last 1000 Years Reliable?. Annals of Applied Statistics. 5(1): 5-44. (with discussion).
McShane, Blake, R. J. Galante, S. Jensen, N. Naidoo, Pack A.I. and A. J. Wyner. 2010. Characterization of the Bout Durations of Sleep and Wakefulness: New Metrics for Summarizing Sleep. Journal of Neuroscience Methods. 193(2): 321-333.
Piette, J., A. Braunstein, Blake McShane and S. Jensen. 2010. A Point-Mass Mixture Random Effects Model for Pitching Metrics. Journal of Quantitative Analysis in Sports. 6(3): Article 8.
McShane, Blake. 2009. Exploring a New Method for Classification with Local Time Dependence. Transactions of the Deming Conference on Applied Statistics.
Jensen, S., Blake McShane and A. J. Wyner. 2009. Rejoinder: Hierarchical Bayesian Modeling of Hitting Performance in Baseball. Bayesian Analysis. 4(4): 669-674.
Jensen, S., Blake McShane and A. J. Wyner. 2009. Hierarchical Bayesian Modeling of Hitting Performance in Baseball. Bayesian Analysis. 4(4): 631-652. (with discussion).
McShane, Blake,  M Adrian, Eric Bradlow and P. S. Fader. 2008. Count Models Based on Weibull Interarrival Times. Journal of Business and Economic Statistics. 26(3): 369-378.
Kiser, R., M. Asher and Blake McShane. 2008. Let’s Not Make a Deal: An Empirical Study of Decision Making in Unsuccessful Settlement Negotiations. Journal of Empirical Legal Studies. 5(3): 551-591.

 
Print Teaching
Teaching Interests
Marketing Research; Data Analysis; Computation Statistical Methods; Probability Models for Marketing
Full-Time / Evening & Weekend MBA
Marketing Research and Analytics (MKTG-450-0)
The broad objective of this course is to provide a fundamental understanding of marketing research methods employed by well-managed firms. The course focuses on integrating problem formulation, research design, questionnaire construction, sampling, data collection and data analysis to yield the most valuable information. The course also examines the proper use of statistical applications as well as qualitative methods, with an emphasis on the interpretation and use of results.

Customer Analytics (MKTG-482-0)

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

Marketing is evolving from an art to a science. Many firms have extensive information about consumers' choices and how they react to marketing campaigns, but few firms have the expertise to intelligently act on such information. In this course, students will learn the scientific approach to marketing with hands-on use of technologies such as databases, analytics and computing systems to collect, analyze, and act on customer information. While students will employ quantitative methods in the course, the goal is not to produce experts in statistics; rather, students will gain the competency to interact with and manage a marketing analytics team.

Quantitative Marketing: Statistical Modeling (MKTG-551-2)
This is a doctoral course on statistical models and topics alternate from year to year. Currently, in odd years the course is on Bayesian methods and computation and covers simple parametric models, regression models, hierarchical models, mixture models, optimization algorithms, Monte Carlo simulation algorithms, model checking, nonparametric models, and hidden Markov models while in even years the course is on applied and computational statistics and covers statistical graphics and exploratory data analysis, permutation tests, null tests, the bootstrap, smoothing, cross-validation, tree-based and linear regression, model selection, bagging, principal components analysis, and cluster analysis. Marketing applications include but are not limited to conjoint analysis, choice models, data minimization, perceptual maps, etc.

Doctoral
Topics in Quantitative Marketing (MKTG-552-0)
This seminar-style class exposes students to working papers in current areas of active research. 2nd, 3rd, and 4th year students are required to enroll. Participants will read, present, and discuss recent papers with the goal of improving their ability to evaluate a paper’s academic contribution and managerial relevance and to further extend their knowledge of models and methods. This class will reinforce the research environment and foster collaboration between students and faculty. One or more faculty can teach the course, and we expect to rotate it among the faculty on a consistent basis. Faculty who teach this course must be careful to coordinate the content relative to the three core classes.

Executive Education
Analytics for Better Marketing Decisions

In a business world where data analytics seems to be the answer to every question, marketing executives need to understand how to apply these tools strategically. In this program, participants will learn the most important marketing data analytics methods available and how to use them effectively.


View Program