Song Yao (姚松)
Associate Professor of Marketing, Washington University
Principal Economist, Amazon Core AI
Full-Time / Part-Time MBA / Undergraduates
The Internet and digital technologies have continued to alter the way consumers search information, make transactions, and share experiences, as well as the way firms market towards and engage with consumers. In today’s digital era, it is imperative for marketers to understand how to gain a competitive edge by leveraging digital media to set targeting strategies and implement the marketing mix. This course will provide a structured framework to introduce students to the most up-to-date tactics, applications, and trends in digital marketing.
Full-Time / Part-Time MBA / Master of Science in Analytics
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.
This course surveys quantitative research in marketing, with a focus on empirical models. The goal of the course is to a) raise students awareness of this literature and b) stimulate new research interests. By the end of the course, students should be familiar with the key issues and approaches in empirical quantitative marketing, the strengths of these research streams, and the opportunities to extend them.
Applied Econometrics II
The course develops the behavioral and statistical foundations of econometric approaches to testing causal models of individual, firm, and market behavior. The course aims to provide students with a specific and systematic approach for assessing the plausibility (and limitations) of causal inference. This includes assessing the role of a behavioral model in the formulation of an econometric model, accounting for the potential impact of unobservables on estimation and inference, and evaluating behavioral and statistical sources of identification.