SchwartzEric

Eric Schwartz

By | | No Comments

Professor Eric Schwartz’s expertise focuses on predicting customer behavior, understanding its drivers, and examining how firms actively manage their customer relationships through interactive marketing. His research in customer analytics stretches managerial applications, including online display advertising, email marketing, video consumption, and word-of-mouth. The quantitative methods he uses are primarily Bayesian statistics, machine learning, dynamic programming, and field experiments. His current projects aim to optimize firms’ A/B testing and adaptive marketing experiments using a multi-armed bandit framework. As marketers expand their ability to run tests of outbound marketing activity (e.g., sending emails/direct mail, serving display ads, customizing websites), this work guides marketers to be continuously “earning while learning.” While interacting with students and managers, Professor Schwartz works to illustrate how today’s marketers bridge the gap between technical skills and data-driven decision making.

fred_feinberg_small

Fred Feinberg

By | | No Comments

My research examines how people make choices in uncertain environments. The general focus is on using statistical models to explain complex decision patterns, particularly involving sequential choices among related items (e.g., brands in the same category) and dyads (e.g., people choosing one another in online dating), as well as a variety of applications to problems in the marketing domain (e.g., models relating advertising exposures to awareness and sales). The main methods used lie primarily in discrete choice models, ordinarily estimated using Bayesian methods, dynamic programming, and nonparametrics. I’m particularly interested in extending Bayesian analysis to very large databases, especially in terms of ‘fusing’ data sets with only partly overlapping covariates to enable strong statistical identification of models across them.

Applying Bayesian Methods to Problems in Dynamic Choice

Applying Bayesian Methods to Problems in Dynamic Choice

 

manchanda-small

Puneet Manchanda

By | | No Comments

My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.

gonzophot0-small

Rich Gonzalez

By | | No Comments

My research makes use of state-of-the-art statistical learning and exploratory tools to answer questions at the interface of biology and behavioral science.