Teaching
Product Management - MGT 561 (MBA + Yale Graduate Students)
This course addresses the design and management of new products. We will focus primarily on technology-driven digital products in B2C settings—traditionally, software applications, but increasingly physical products such as electric vehicles and smart home devices. This course will emphasize quantitative methods to analyze consumer feedback and product usage data to inform product management decisions.
The course will primarily provide hands-on opportunities for students to apply course material through a team-based Course Project. Tactical product management skills we will cover include understanding the voice of the customer and capturing customer needs, translating needs to product features, and prioritizing and roadmapping product features. To aid in these tactical product management skills, we will use a number of software tools used at leading tech firms, and bring in a number of leading speakers in the field.
The class sessions and assignments are aimed at covering both theoretical and applied aspects of product management. We will cover basics of technology-driven products (e.g., the software stack and modern cloud ecosystem); the interplay of product management with design and UI/UX, engineering, and go-to-market; and how to evaluate features by defining statistical hypotheses and conducting A/B tests. We will also cover topics in product strategy, including evaluating and expanding product-market fit; tradeoffs between acquisition, retention, and monetization; product growth considerations; offensive and defensive product strategy; marketplace products and network effects; and machine learning for product management.
As you begin your career as a product manager, you will most likely be joining a firm that has already established product-market fit and is instead looking to develop new product features, grow the product, or make decisions with product strategy. Your Course Project will cover topics in-line with the daily product management tasks you may encounter; however, the product fundamentals we cover are relevant to all types and stages of a product career.
Attention: This is an elective class designed for graduate students at the Yale School of Management. This year we will not be admitting undergraduates due to capacity constraints. We also do not allow auditing of this class due to its team-based nature.
If you are a Yale graduate student * not * affiliated with SOM (Yale Masters/Ph.D students), you should apply directly to me, not through the bidding process (it will not work due to capacity constraints). Your application should include sending an e-mail with a 1-2 paragraph description of your interests for this course, as well as your resume/C.V. by e-mail: alex.burnap@yale.edu.
Machine Learning - MGT 764 (Ph.D Course)
This is a short but fast-paced course in applied machine learning for Ph.D. students in quantitative marketing, especially those who have taken the intro and intermediate ML course sequence through Yale S&DS. The positioning of this course against other (fantastic) courses at Yale University is its emphasis on connections between modeling and inference in machine learning and marketing science.
The goal is to help students advance their Ph.D. research by helping ask:
- Which off-the-shelf ML models are suited for my marketing science research?
- What are connections between seminal models in marketing science and ML?
- What are key assumptions and limitations of current ML models, particularly in marketing contexts, and how might I develop models better suited to the science?
We will cover a "roadmap" of seminal-yet-increasing-in-complexity machine learning models, from linear models to recent advances in deep learning. While this course is primarily designed to survey models, we will cover selected ML fundamentals and a few topics less covered in management science curricula (e.g., Bayesian inference, high-dimensional optimization).
Previous Courses
Marketing Innovation (MBA)
This MBA course aims to help students understand innovation through case studies and modern analytics methods. I have acted as a course advisor for three semesters now, focusing on topics such as conjoint analysis and perceptual mapping.
I have also helped put together new open access teaching materials for designing a new product using real conjoint analysis and product design data. We are in the process of releasing it publically.
Optimization and Machine Learning for Product Design (Graduate Level)
This graduate-level optimization and mathematical modelling course is based in the engineering college and cross-listed among disciplines such as manufacturing, design, and mechanical engineering. This course covers mathematical formulation of optimization problems, analytic and data-driven modelling, and subsequent solution methods.
I focus on building intuition using analytic methods, followed by practical usage with iterative constrained linear and quadratic approximation methods, and later progress towards more sophisticated methods involving constraint handling and deriviative-free methods. I have introduced a number of modelling methods, including linear and logistic regression, neural networks, and Gaussian processes (kriging), which appears in the new edition of the book.
Analytic Product Design (Undergraduate Seniors)
This is a product design project course offered to undergraduate seniors and first-year graduate students. Although this course emphasizes engineering design and system integration, I introduce elements of marketing and consumer behaviour to develop products better suited to the end user. In teaching this course, I received a student-nominated award across the entire the University of Michigan College of Engineering. Here are examples of some projects I supervised that left the classroom:
