Sridhar Narayanan

Professor of Marketing, Stanford Graduate School of Business


Working Papers


The objective of this course is to introduce you to modern marketing practice at an accelerated level. Marketing is key to the success of an organization and requires an ability to design and execute a coherent strategy across a number of different dimensions. Specifically, we study in depth each of the tactical P’s “price, promotion, product, and place (distribution)” and do so through the structural lens of the three C’s “customer, competition, and company, with a particular focus on the customer.” Going beyond the fundamentals, the course emphasizes two specific areas of specialization and learning throughout. First, it focuses on data-driven techniques for assessing markets and teaches you which of these techniques apply to different marketing decision problems. Second, the course takes seriously the idea that consumers often want different things. It therefore focuses on how you can generate company value by understanding and serving heterogeneous consumer wants and needs.

The course aims to develop a thorough understanding of Bayesian inference, with a special focus on empirical applications in marketing. The course will start with a brief theoretical foundation to Bayesian inference and will subsequently focus on empirical methods. Initial topics would include Bayesian linear regression, multivariate regression, importance sampling and its applications. Subsequently, the course will focus on Markov Chain Monte Carlo (MCMC) methods including the Gibbs Sampler and the Metropolis-Hastings algorithm and their applications. The overall focus of the course will be on applying these methods for empirical research using a programming language such as R.


Faculty Assistant

Sandra Davis