Marketing Analytics is the discipline of analyzing and synthesizing multiple data sources to uncover meaningful insights and actionable information to reach audiences and customers. It’s marketing intelligence—using data to uncover your customer’s voice—and it’s a rapidly growing job market.
The Marketing Research, Insights and Analytics (MRIA) program at Rutgers Business School will prepare you for a career in a data-driven industry that leverages Big Data to make marketing decisions. You’ll learn how to draw conclusions about data and be positioned for jobs in marketing research, marketing analytics and data handling.
What you’ll learn:
- Practical applications to solve actual marketing problems
- Analytical techniques to understand customer needs and markets
- How to synthesize information to influence and shape business decisions
Advanced Marketing Research [22:630:677]
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.
Analytics for Business Intelligence [22:960:641]
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.
Applied AI in Marketing [22:630:685]
Over the last five years many industries have gone through a transformational change powered by machine learning and artificial intelligence. Automation and human-like decision making has expanded the range of possible through a significant increase in productivity. Some say that AI will disrupt our lives and replace most jobs as we know them today, while others argue that AI would elevate people and their skills allowing them to focus on creative aspects of their jobs. No matter what school of thought you belong to, AI is here to stay, so understanding how it is applied to the real-world setting is critical for anyone who plans to join the modern workforce.
This course will introduce you to the industry application of the most common machine learning and artificial intelligence techniques in marketing using industrialized statistical software. By the end of this course, you would learn how to optimize marketing spend, measure customer attitudes towards a product using unsupervised learning, and predict customer purchase behavior with supervised learning. You will also learn how to choose the right method for the most frequent business problems and will obtain hands-on experience in solving these problems.
Business Forecasting [22:960:608]
Innovative businesses are using data to make better predictions about their business environment, their business future, and the future of their global competitors. “Big Data” is a business term frequently used these days. Businesses are storing and collecting more data than ever before to gain a competitive edge. McKinsey predicts that data will grow 10-fold by 2015 and 100-fold by 2020. This will result in businesses looking for better data scientists to help them leverage “Big Data” and gain a competitive edge.
In this class, students will use the level R programming language to become data scientists and business forecasters. Specifically, students will learn how to:
- Understand Data
- Analyze Data
- Apply various forecasting methods
- Leverage forecasts to make decisions
- Communicate forecasts and recommendations to management
No prior knowledge of R programming is required. You will learn and become proficient in R and obtain hands-on experience of its forecasting package through case studies and real-life examples during each lecture. You will also learn to better communicate your forecast and strengthen your analytical skills. The practical knowledge gained upon completion of this course will help in careers ranging from business analytics to marketing, accounting, financial services, and more.
Consumer Behavior [22:630:610]
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.
Marketing Insights [22:630:678]
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.
Marketing Models (PhD level) [26:799:675]
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]
Marketing Research [22:630:604]
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.
Marketing Strategy [22:630:609]
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.
Pharmaceutical Marketing Research [22:630:617]
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.
Survey Sampling [16:960:576]
Introduction to the design, analysis, and interpretation of sample surveys. Sampling types covered include simple random, stratified random, systematical, cluster, and multistage. Methods of estimation described to estimate means, totals, ratios, and proportions. Development of sampling designs combining a variety of types of sampling and methods of estimation, and detailed description of sample size determinations to achieve goals of desired precision at least cost.
Web Analytics [22:630:679]
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.
The MRIA concentration is guided by an active advisory board of accomplished marketing research professionals and faculty who offer mentorships, networking and career opportunities, internships and other opportunities to enrich your academic experience. Our advisory board members are marketing leaders across a variety of industries, from pharma to finance.