Stephen J. Anderson, Leonardo Iacovone, Shreya Kankanhalli, Sridhar Narayanan (2022), Vol. 59, Issue 3, Pages 472-496.
Professor of Marketing, Stanford Graduate School of Business
Sridhar Narayanan is a Professor of Marketing at the Graduate School of Business, Stanford University. He received his PhD from the University of Chicago in 2005 and has been at Stanford since July 2005. Before his PhD, he worked as a Sales and Marketing manager at Unilever, after receiving a BE in Electrical Engineering and an MBA, both from the University of Delhi, India.
Sridhar’s research has been published in the leading journals of marketing, such as Marketing Science, Journal of Marketing Research, Quantitative Marketing and Economics and the Journal of Marketing. He serves as Associate Editor at Marketing Science and Quantitative Marketing and Economics.
Sridhar teaches an MBA core course in Marketing, PhD course on Bayesian methods and an elective course on scaling small businesses. Previously he also taught MBA electives like Marketing Analytics, Advanced Marketing Analytics, Digital Marketing and Green Marketing.
Gordon, Brett Kinshuk Jerath, Zsolt Katona, Sridhar Narayanan, Jiwoong Shin and Kenneth Wilbur (2021). Journal of Marketing, 85:1, pp 7-25
Navdeep Sahni, Sridhar Narayanan and Kirthi Kalyanam (2019). Journal of Marketing Research, Vol. 56, Issue 3, Pages 401-418
Sridhar Narayanan and Harikesh Nair (2013). Journal of Marketing Research, Vol. 50, Issue 1, Pages 70-94
Sridhar Narayanan (2013). Quantitative Marketing and Economics, Vol 11, Pages 39-81
Sridhar Narayanan and Puneet Manchanda (2012). Quantitative Marketing and Economics, Vol 10, Pages 27-62
Wesley Hartmann, Harikesh Nair and Sridhar Narayanan (2011). Marketing Science, Vol 30, Issue 6, Pages 1079-1097
Michaela Draganska, Sanjog Misra, Victor Aguirregabiria, Pat Bajari, Liran Einav, Paul Ellickson, Dan Horsky, Sridhar Narayanan, Yesim Orhun, Peter Reiss, Katja Seim, Vishal Singh, Raphael Thomadsen & Ting Zhu (2008).Marketing Letters, Vol. 19, Pages 399–416
Sridhar Narayanan, Puneet Manchanda, Pradeep K. Chintagunta (2005).Journal of Marketing Research,Vol. 42, Issue 3, Pages 278-290
Puneet Manchanda, Dick R. Wittink, Andrew Ching, Paris Cleanthous, Min Ding, Xiaojing J. Dong, Peter S. H. Leeflang, Sanjog Misra, Natalie Mizik, Sridhar Narayanan, Thomas Steenburgh, Jaap E. Wieringa, Marta Wosinska, Ying Xie (2005).Marketing Letters, Vol. 16, Issue 3-4, Pages 293-308
Sridhar Narayanan, Ramarao Desiraju, Pradeep K. Chintagunta (2004).Journal of Marketing, Vol. 68, Issue 4, Pages 90-105
Anuj Kapoor, Sridhar Narayanan, Puneet Manchanda (2023).
Justin Huang, Rupali Kaul, Sridhar Narayanan (2022).
Anuj Kapoor, Sridhar Narayanan, Amitt Sharma (2022).
Kirthi Kalyanam and Sridhar Narayanan (2021).
Jon Zeller, Sridhar Narayanan (2022).
Unnati Narang, Venkatesh Shankar, Sridhar Narayanan (2021).
Justin T. Huang and Sridhar Narayanan (2020).
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.