Caio Waisman joined the marketing faculty at the Kellogg School of Management in 2019. Professor Waisman's research interests include online platforms, mechanism design and dynamic decision-making. His recent work evaluates the strategic use of selling mechanisms by event ticket sellers, competitive interference between online ad experiments and the use of adaptive experimentation for concurrent optimization and inference.

Professor Waisman holds a PhD in Economics from Stanford University, an MA in Economics from the Pontifical Catholic University of Rio de Janeiro and a BA in Economics from the University of São Paulo. Before joining Kellogg, he worked in the Business Growth division of JD.com.

Quantitative Marketing, Empirical Industrial Organization, Applied Econometrics

Quantitative Marketing: Structural Modeling (MKTG-551-3)
This course provides a foundational understanding of static and dynamic discrete-choice models, with applications drawn from quantitative marketing and economics. The course takes a "hands on" approach to research, with class being a mix of lectures, discussion of articles, and hands-on empirical analysis. Coding assignments are the bulk of the grade.

Customer Analytics and AI (MKTG-482-0)
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, machine learning, 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 and AI team. We will use the statistics program R in Customer Analytics and AI. 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. Before you start the course, you will need to learn how to use R using tutorials and online course. There will be an assignment that is due at the beginning of the first class to make sure that you are sufficiently proficient in R before the course starts. Please do not take this class if you are not willing or able to make this investment. The course consists of lectures, in-class exercises, group work, and case discussions. You will use R throughout the class to work with individual-level customer data. The course has no final; instead, students are evaluated on their performance on weekly assignments. This course has no overlap with other existing analytics or AI courses at Kellogg. The course is an excellent companion to Retail Analytics.