NYU Stern School of Business

Undergraduate College

MKTG-UB.0054.001 (C55.0054): Data-Driven Decision Making

Fall 2011

Instructor Details

Singh, Vishal



Tuesday, 1:30 - 3:00 PM and by appointment

Tisch Hall, Room 911


Course Meetings

MW, 2:00pm to 3:15pm

Tisch T-UC11

Final Exam:

Schedule exceptions
    Class will not meet on:
    Class will meet on:


Course Description and Learning Goals

Course Information

“Every two days we now create as much information as we did from the dawn of civilization up until 2003”

Eric Schmidt (CEO Google).

“Data are widely available; what is scarce is the ability to extract wisdom from them”

Hal Varian (UC Berkeley and Chief Economist, Google).

The two quotes above summarize the main theme of this course. In every aspect of our daily lives, from the way we work, shop, communicate, or socialize; we are both consuming and creating vast amounts of information. More often than not, these daily activities create a trail of digitized data that is being stored, mined, and analyzed by firms hoping to create valuable business intelligence. With technological advances and developments in customer databases, firms have access to vast amounts of high-quality data which allows them to understand customer behavior, and customize business tactics to increasingly fine segments or even segments of one. However, much of the promise of such data-driven policies has failed to materialize because managers find it difficult to translate customer data into actionable policies. The general objective of this course is to fill this gap by providing students with tools and techniques that can be utilized for making business decisions. Note that this is not a statistics or mathematics course.The emphasis of the class will be on applications and interpretation of the results for making real life business decisions. We will focus less on the mathematical and statistical properties of the techniques used to produce these results.

The pedagogic philosophy in this course embraces the principle of learning-by-doing. Each concept that we cover has a software implementation and a problem or case whose resolution can be enhanced through use of the data. In the process of learning-by-doing, students will find out what the tools and software can do, as well as what they cannot do, because they will be using and adapting them to solve problems. Extracting useful insights from the vast amount of information involves a combination of analytical skills and intuition. It is both an art and science. Statistical tools covered in the class will range from simple data analysis and visualization, to advanced regression and multivariate statistics. Some of the quantitative methods and concepts are fairly advanced and may seem intimidating at the beginning. Regardless of your prior background, an objective of the course is to remove any fear of data analysis, and provide you with the toolkits to become an accomplished empirical analyst. In order to providea broad intuition of the concepts and methods, we will use data/problems/example/case studies from different fields such as Finance & Economics, Psychology & Sociology, Politics & Public Policy, and Medicine & Biology. However, since this is primarily a Marketing course, emphasis will be given to quantitative aspects of marketing decision making such as segmentation, estimating market potential and forecasting demand, developing optimal pricing policies, and designing and positioning new products. In addition we will address some emerging areas such as the role of corporate social responsibility, customer relationship management (CRM), and consumer privacy issues.

Although there are no specific prerequisites for the class, an introductory class in statistics/regression and working knowledge of MS Excel would be helpful. However, the most important prerequisite for the class is a positive attitude towards learning. Regardless of your chosen field or major, it is virtually impossible to survive in the business world without a working knowledge of basic data analysis and use of some statistical software (at least Excel). The course is designed to train you in a wide spectrum of quantitative problems that you are likely to encounter in your workplace. More generally, I hope to instill a general analytical intuition that enables you to analyze and comprehend contemporary issues such as the housing bubble, projections of government deficits, or climate change. In other words, become an educated consumer of news, issues, and challenges facing the society.

The specific objectives of this course are to: 

  1. Help you understand how analytical techniques and statistical models can help enhance decision making by converting data to information and insights for decision-making;
  2. Provide intuition for data driven decision making by using practical examples from a wide spectrum of fields;
  3. Provide insight into how to choose and use the most effective statistical tool based on the problem at hand;
  4. Provide you with a software tool kit that will enable you to apply statistical models to real decision problems;
  5. Most importantly, remove any fear of data analysis and increase your comfort level with analyzing databases most commonly used in the business world.

In summary, unlike most marketing and statistics classes that focus on conceptual material, this course will provide skills to translate conceptual understanding into specific operational plans – a skill in increasing demand in the business world.


Required Course Materials

Textbook & Software

There is no required text book. Your old statistics/regression text + free online resources should suffice. For specific topics/methods, I will provide extensive notes.

REQUIRED: SPSS PASW Statistics 18 (Cost about $35 for a 6-month student license) & MS Excel.


Assessment Components

Basis for Final Grade



Grading & Due Dates

 I grade your assignments and exams with help from the TAs. You can only appeal a grade if there is a clear misreading of what you wrote. I can give you suggestions for improving your work but will not respond to emotional appeals. Due dates will be strictly enforced without valid reason and prior permission. Only 50% Credit after the due date.


Professional Responsibilities For This Course




In-class contribution is a significant part of your grade and an important part of our shared learning experience. Your active participation helps me to evaluate your overall performance.
You can excel in this area if you come to class on time and contribute to the course by:




Classroom Norms


Stern Policies

General Behavior
The School expects that students will conduct themselves with respect and professionalism toward faculty, students, and others present in class and will follow the rules laid down by the instructor for classroom behavior.  Students who fail to do so may be asked to leave the classroom. 


Collaboration on Graded Assignments
Students may not work together on graded assignment unless the instructor gives express permission. 


Course Evaluations
Course evaluations are important to us and to students who come after you.  Please complete them thoughtfully.


Academic Integrity

Integrity is critical to the learning process and to all that we do here at NYU Stern. As members of our community, all students agree to abide by the NYU Stern Student Code of Conduct, which includes a commitment to:

The entire Stern Student Code of Conduct applies to all students enrolled in Stern courses and can be found here:

Undergraduate College: http://www.stern.nyu.edu/uc/codeofconduct
Graduate Programs: http://w4.stern.nyu.edu/studentactivities/involved.cfm?doc_id=102505

To help ensure the integrity of our learning community, prose assignments you submit to Blackboard will be submitted to Turnitin.  Turnitin will compare your submission to a database of prior submissions to Turnitin, current and archived Web pages, periodicals, journals, and publications.  Additionally, your document will become part of the Turnitin database.


Course Outline

Tentative Topics

Topic 1: An intuitive introduction to data-driven decision making

We will begin the course with a general introduction on what we mean by data driven strategy and why it is important. We will use several examples and mini-case studies to illustrate the role of statistical analysis in managerial decision making. These lectures will provide an overview of the course including the main topics covered, grading criterion, and road map for rest of the semester.

Topic 2: Basic Data Analysis & Intro to SPSS

In this session we will discuss various types of data that are commonly collected by firms. What methods to use and what inferences/insights can be obtained depend on the type of data that are available (stated versus revealed preference, level of aggregation, cross-sectional, time series, panel data and so forth). We will cover some of the nuts and bolts of preparing data for analysis, and use several mini-cases to review some basic yet extremely useful techniques such as data visualization, frequency distributions, mean comparisons, and cross tabulation. Statistical inferences using chi-square, t-test and ANOVA will be discussed. We will also look at the basics of the SPSS software.

Topic 3: Experimental Design and Natural Experiments

Experimental designs are often regarded as the "gold standard" for making causal or cause-effect inferences. We will discuss the issues of design of experiments and internal and external validity. Several case studies in marketing, economics, and medicine that range from controlled lab and field experiments, to circumstances that provide us with “natural” experiments will be discussed.

Topic 4: Opinion Polls and Survey Based Analysis

Survey research is animportanttoolto assess attitudes and opinions on a wide range of issues. It is one of the most common forms of data you will encounter in the industry as it is used extensively in marketingresearchandby virtually all firms.We will briefly discuss issues of survey design and sampling, but focus primarily on analysis of survey data using examples from a variety of industries/topics such as customer satisfaction, debate on health care reform, and politics. Appropriate use of descriptive statistics (what's going on in our data) and inferential statistics (how to make inferences from our data to general population) will be discussed.

Topic 5: Regression Analysis

In this topic we will turn our attention to the relationships among variables. Regression is by far the most useful tool for analyzing relationships between a phenomenon of interest (independent variable) and one or more predictor variables. We will spend a fair amount of time on regression and its applications. Emphasis will be on use of regression output in forecasting, elasticity analysis, and various applications such as promotional planning and optimal pricing.

Topic 6: Advanced Regression Models

This session covers some important aspects of regression modeling including measures to control for seasonality and trend, capture non-linear effects, interactions, and use of appropriate functional forms (liner, semi-log, log-log).

Topic 7: Discrete Choice Models

Typical regression analysis is suitable when the dependent variable is continuous (e.g automobile sales, price of crude oil, stock prices). Often we encounter situations where the phenomenon of interest (i.e. you dependent variable) is discrete (e.g. vote or not, buy or don’t buy). In these circumstances, use of linear regression may be inappropriate. This class will discuss Logit models that are appropriate for discrete choice analysis. We will use a comprehensive case study on optimal targeting.

Topic 8: Database Marketing and Customer Relationship Management

It is often thought that the value of a firm can be computed using the metric of life time value of its customer base. This topic will cover the important and growing area of CRM and customer equity. We will discuss various tools in database/direct marketing used to model customer acquisition and retention. Analytical tools to compute customer lifetime value (CLV) will be discussed using a comprehensive case study.

Additional Topics: Note some of these are covered in other classes such as marketing research and new products. We will cover one or more of these depending on the time available and your interest.

Topic 9: New Product Design

Should iPad include a digital camera? Is there a large enough segment of customer base that desires this feature? If so, how much extra would they be willing to pay for it?Conjoint analysis is one of the most important and frequently used approaches for measuring consumers’ preference and determining the optimal product design. We will discuss consumers’ willingness to trade off and how to aggregate data across consumers and predict market shares using a comprehensive case study.

Topic 10: Factor Analysis

Is there any structure to the way people feel about a product or concept? Are there any overarching factors that affect the way in which people respond? Factor Analysis is a tool that can group correlated variables to form factors or indices. It is widely used in survey analysis and as a general tool for data reduction. We will discuss the basics of this technique and its applications.

Topic 11: Cluster Analysis

Do all of my customers have the same preferences? Are there any natural ways to group them based on their buying behavior or attitudes? Cluster analysis is typically used to group similar consumers (or countries, species, diseases) together based on observed characteristics. We will discuss the main methods used in cluster analysis using data from a consumer survey.

Topic 12: Multidimensional Scaling

Multidimensional scaling (MDS) encompasses a collection of methods which allow us to gain insight in the underlying structure of relations between entities by providing a visual representation of these relations.This technique is also commonly used in the industry for the purpose of creating perceptual maps for product positioning. We will discuss the basic methods for creating perceptual maps using two case studies. 


Recording of Classes

Your class may be recorded for educational purposes


Students with Disabilities

If you have a qualified disability and will require academic accommodation of any kind during this course, you must notify me at the beginning of the course and provide a letter from the Moses Center for Students with Disabilities (CSD, 998-4980, www.nyu.edu/csd) verifying your registration and outlining the accommodations they recommend.  If you will need to take an exam at the CSD, you must submit a completed Exam Accommodations Form to them at least one week prior to the scheduled exam time to be guaranteed accommodation.


Printer Friendly Version