September 27, 2022
Virtually no business decision today is made without data backup. As a result of the digital revolution, our society is overwhelmed by a plethora of data, and thus it’s incumbent on marketing professionals to mine that data intelligently. To do so requires an intuition that can only be shaped by learning and practicing with the analytics tools that are appropriate for delivering a particular objective.
The curriculum of the Data-Driven Decision-Making (D3M) course in the MS Marketing & Retail Science (MSMRS) program focuses on building these exact skills. Taught by NYU Stern Professor of Marketing Vishal Singh, students learn how to use data and analytics to answer business questions related to the field of marketing and retail. The goal of the course, as outlined by the course title, is twofold: first, understanding the latest technologies and techniques (data), and second, to develop both a data analytics mindset and also the ability to use that knowledge strategically (decision-making).
Following a PhD in marketing from Northwestern University and with 15 years of teaching experience at Stern, including his new appointment as the director of Stern’s Center for Research Computing, Singh has spent his academic career in the data-driven business strategy domain, with a focus on retail competition, competitive pricing, database marketing, and customer management.
Throughout the course, Singh makes clear to his students that the key to success is not just knowing how to use a particular tool or algorithm, but moreover a solid understanding of why that particular tool or algorithm is the right one to use. “This implies conceptualizing the problem carefully and analyzing the sanctity of your input data,” Singh says. “There is an old saying in this field, ‘GIGO’ (garbage in, garbage out). So rather than jumping to a sophisticated tool or model, it is important first to pay attention to the problem you are trying to solve and the quality of information you are using in the models.”
Students in the MSMRS program appreciated the professor’s emphasis on using data strategically to address real-world problems. The professor’s teaching style is very relaxed, according to Alice Li (MSMRS ’23), but at the same time, she adds, “The class is also very practical; the analytics tools taught in the class are much more practical than software taught in my undergraduate courses. We don’t need to memorize statistical concepts, but instead focus on understanding fundamental operation logic.”
For his part, Singh finds his D3M students “terrific academically and eager to learn.” Many in the MSMRS program may have some internship experience, but most do not have full-time work experience and are coming straight from undergrad, which he points out is in fact easier for delving into technical aspects of models and algorithms. Singh also made an effort to incorporate ideas and cases from the different business sectors and cultural domains as represented in the MSMRS program. Though the language of data is universal, “It is important to clearly articulate and show with examples how a particular method or tool is used in practice,” Singh emphasizes.
To help students understand such real-world applications, assignments in Singh’s MSMRS class are typically based on case studies, many of which come from his own research. Students are assigned to read a non-technical version of the research—for instance, coverage from popular press or Harvard Business Review—and then analyze a simplified version of the data used in the research to provide the main insights.
The material is challenging, D3M student Yujie He (MSMRS ’23) acknowledged, but “the professor is very patient,” answering questions clearly and encouraging class discussion. “The course is very practical, and the in-class exercises with real cases help us learn and gain the technical skills immediately. When we have questions on how to use R or Python, he would check the data, helping us understand the data analysis process.”
Adds the professor, “This is a really exciting time to learn analytics and machine-learning algorithms since there are great tools out there to automate the analysis.” This foundation will serve the MSMRS students well as they head to NYU Shanghai for the fall semester and deepen their understanding of analytics tools and decision-making skills in the field of marketing and retail.