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 an important tool to 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 marketing research and by 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. your 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.
Topic 8: Database Marketing/Data Mining/CRM
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. We will also cover two extremely useful techniques for data reduction: Cluster and Factor analysis.