Caio Waisman
Associate Professor of Marketing
Caio Waisman is an Associate Professor of Marketing at Kellogg, where he has been since 2019. He holds a PhD in Economics from Stanford. His research focuses on developing tools to measure the effectiveness of online advertising by leveraging the structure of advertising platforms and of ad auctions in general. Before joining Kellogg, he worked at JD.com as part of their business growth division.
- Quantitative Marketing
- Empirical Industrial Organization
- Applied Econometrics
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PhD, 2018, Economics, Stanford University
M.A., 2013, Economics, Pontifical Catholic University of Rio de Janeiro
B.A., 2010, Economics, University of Sao Paulo -
Dick Wittink Prize
Topics in Quantitative Marketing (MKTG-552-0)
This seminar required of 2nd-4th year students exposes students to working papers in current areas of active research. Students 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.
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.
Field Study (MKTG-498-0)
Field Studies include those opportunities outside of the regular curriculum in which a student is working with an outside company or non-profit organization to address a real-world business challenge for course credit under the oversight of a faculty member.
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.
Customer Analytics and AI (AIML-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.
