Marketing Research Insights and Analytics (MRIA) MBA Course Descriptions
Unless specified otherwise, all courses are offered on the Livingston Campus
This course aims to equip students with quantitative techniques for making marketing decisions. Univariate and multivariate statistics techniques are applied to secondary and primary data to find descriptive and causal relations among consumer and marketing variables. Building upon the basic marketing research, this course introduces advanced statistical techniques to analyze marketing data. The topics include multiple regression, ANOVA, discriminant analysis, factor analysis, cluster analysis, multidimensional scaling, and conjoint analysis. Major marketing issues include, but not limited to, marketing segmentation, targeting, and positioning. New product concept testing techniques will also be discussed. In addition, other contemporary research techniques and tools are addressed.
Prerequisite: Marketing Research (22:630:604) and Data Analysis & Decision Making (22:960:575)
This seminar-based course is designed to familiarize doctoral and advanced master’s students with the more commonly used qualitative research methods. The course will prepare them to utilize these methods in their own research, or to evaluate the qualitative work that others have done. [Downtown New Brunswick]
This course is intended for business students of data mining techniques with these goals: 1) To provide the key methods of classification, prediction, reduction, and exploration that are at the heart of data mining; 2) To provide business decision-making context for these methods; 3) Using real business cases, to illustrate the application and interpretation of these methods.
Understanding the behavior of consumers and the factors that influence their behavior. Topics covered include: consumer decision models, psychological processes, and social and environmental forces that shape consumer behavior. Explores historical development of consumer behavior and current societal issues. Sources include texts, readings, and case studies.
Prerequisite: Marketing Management (22:630:550 (FT) / 22:630:586 (PT))
Introduces statistics as applied to managerial problems. Emphasis is on conceptual understanding as well as conducting statistical analyses. Students learn the limitations and potential of statistics, gain hands-on experience using Excel, as well as comprehensive packages, such as SPSS®. Topics include descriptive statistics, continuous distributions, confidence intervals for means and proportions, and regression. Application areas include finance, operations, and marketing. Introduces the basic concepts of model building and its role in rational decision making. Knowledge of specific modeling techniques, such as linear and nonlinear programming, decision analysis, and simulation, along with some insight into their practical application is acquired. Students are encouraged to take an analytic view of decision making by formalizing trade-offs, specifying constraints, providing for uncertainty, and performing sensitivity analyses. Students form groups to collect and analyze data, and to write and present a final report.
Prerequisite: Statistics for Managers (22:135:572) with grade of B or better
The key objectives of this course are two-fold: (1) to teach the fundamental concepts of data mining and (2) to provide extensive hands-on experience in applying the concepts to real-world applications. The core topics to be covered in this course include classification, clustering, association analysis, and anomaly/novelty detection. This course consists of about 13 weeks of lecture, followed by 2 weeks of project presentations by students who will be responsible for developing and/or applying data mining techniques to applications such as network intrusion detection, Web usage analysis, business/financial data analysis, text mining, bioinformatics, systems management, Earth Science, and other scientific and engineering areas. [PhD, Newark]
This course focuses on the applications of forecasting models and methodologies throughout supply chains, for use in business related activities, including operations, sales, marketing and finance. The course aims to help students understand the significance of matching supply and demand and the development of managerial insights aimed at improving that balance. Several relevant techniques for forecasting, inventory management, and production planning are developed and illustrated. The students are asked to build forecasting and inventory models in Microsoft Excel. The course will focus on by combining theory, examples, practical applications and case studies and consist of a combination of lectures, case presentations, and class discussion.
Fundamental principles of experimental design; completely randomized variance component designs; randomized blocks; Latin squares; incomplete blocks; partially hierarchic mixed-model experiments; factorial experiments; fractional factorials; and response surface exploration. [Busch campus]
Prerequisite: Basic Applied Statistics (01:960:484) or Basic Statistics for Research (01:960:401) or equivalent
Modern methods of data analysis with an emphasis on statistical computing: univariate statistics, data visualization, robust statistics, nonlinear models, logistic regression, generalized linear models (GLM), and smooth regression (including GAM models). Expect to use statistical software packages, such as SAS (or SPSS) and Splus (or R) in data analysis. [Busch campus]
Prerequisite: Level IV statistics. Corequisite: Regression Analysis (16:960:563).
This is a capstone course for Marketing Research which combines all aspects of marketing research process in cased-based projects. As future marketing researchers, students will be trained to integrate results from exploratory, descriptive, and causal research processes and combine both qualitative and quantitative results to make persuasive presentation of the finding. In addition, the course will cover issues of client-vendor communication during the research process. The course will be based on textbooks, assigned readings, case analyses, and student projects.
Prerequisites: Advanced Marketing Research (22:630:677); and at least 2 marketing electives
This is a PhD level course on empirical marketing models. The objective is to learn how to construct and estimate marketing models using data and to critically evaluate models in the literature. The class will meet once a week for 3 hours. Class sessions will be a mixture of lecture, demonstration and discussion. The first half of the course is devoted to developing a basic understanding of marketing empirical models, including hands-on exercises to develop computer codes to estimate these basic models. The second half of the course will focus on applications and extensions of these models in various areas. [PhD, Newark]
Provides insight into the nature and assumptions of marketing research conducted by corporations and commercial research companies. Provides practical experience in planning and implementing marketing research. Covers the sale of marketing research in business management; survey research and questionnaire design; scientific marketing research design and planning; data collection; basic statistical tools for analysis; and report writing and communication of research results.
Prerequisite: Marketing Management (22:630:550 (FT) / 22:630:586 (PT));
Provides concepts and methods essential to (a) identifying and analyzing marketing threats and opportunities; and (b) developing and evaluating marketing strategies. Focuses on business-level marketing strategy. Special attention given to market structure analysis, segmentation and positioning, and international market extension strategies.
Prerequisite: Marketing Management (22:630:550 (FT) / 22:630:586 (PT));
This course examines the role of Marketing Research in four fundamental ways: (1) identification of a marketing problem and translation of that problem into an appropriate scientific question, (2) selections of the most appropriate data collection procedures using the most appropriate sample, (3) the development of analytics for reporting the “whats” of the data but more importantly answering the “whys” behind the relationships among the data, and (4) how to report the results in the most meaningful way that translates the insights into actionable recommendations.
This course teaches web analytics through practical applications, with a focus on deriving actionable insights with web analytics. It provides a broad overview of key web analytics strategies, concepts, issues, challenges and tools. Topics covered include how to choose a web analytics tool. Metrics and Key Performance Indicators. Best ways to analyze effectiveness of blogs, marketing campaigns, SEO, SEM, emails. How to utilize quantitative, qualitative and competitive tools to derive actionable insights. How to optimize web sites by incorporate testing and experimentation. How to create and manage an analytics culture for your organization. Emerging analytics in social, mobile and video. Best practices and pitfalls in web analytics. Best practices on creating a data-driven culture and process.
Prerequisite: Marketing Management (22:630:550 (FT) / 22:630:586 (PT)); Statistics Proficiency