Boston University Questrom School of Business
Professor: Andrey Fradkin
Spring 2025
Wednesdays 12:00pm - 2:45pm
HAR 667
Table of Contents:
Note: This syllabus will evolve as we approach the start of the semester.
This class is designed to provide a forward-looking and opinionated tour of the field of quantitative marketing for students interested in pursuing research in marketing, and other related fields such as economics (especially industrial organization), information systems, strategy, and psychology.
Quantitative marketing is an interdisciplinary field and its boundaries are hard to define. Quantitative marketing concerns itself with helping firms, consumers, and policymakers make better decisions. Classic marketing topics include pricing and promotions, advertising and other customer acquisition strategies, and product design broadly construed. Understanding human and firm behavior is clearly synergistic with understanding the above topics.
Quantitative marketing is a field using a variety of methods, and quickly incorporating advancements in econometrics and machine learning. But the measure of the success of a field is not how quickly it adapts trendy methods, but whether it provides useful and correct insights, and has a positive impact on the world. On this, the record of quantitative marketing is mixed, although improving. Nowadays, most of the major tech firms, and many other firms, are using research developed and promoted by researchers within our and adjacent fields. I view this as at least a partial ‘market test’ of the academic field.
This is an opinionated and biased syllabus. My goal with this class is to highlight foundational topics, as well as those I think will be most fruitful for research in the future. It avoids areas of quantitative marketing I’m less familiar with. This means some topics, such as those relating to the social science of AI and digital platforms, will be included, while others will be excluded. For example, I won’t be covering salesforce management, bayesian estimation, dynamic discrete choice modeling, and customer based valuation. If there’s a topic you’re particularly interested in that is not on the syllabus, please let me know.