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
Rutgers STEM MBA
You can now choose to earn a STEM degree with any of our MBA concentrations. To qualify, you must complete 50% of the total required degree credits for your program with courses that fall under STEM. The Core Curriculum provides 9 STEM credits. I you are seeking the STEM certification, you should take Data Analysis & Decision Making as a Foundation course, at least 3 STEM-designated Concentration Courses, and additional STEM Foundation or Elective courses.
(*) Indicates a STEM-designated course
Advanced Marketing Analytics [22:630:677] *
Today’s managers typically have access to large quantities of data. Careful analyses of such data lead to an improved understanding of the marketplace and, in turn, improve the quality of marketing decisions. This course will cover statistical models and techniques that can be effectively used by managers on marketing data sets. This course emphasizes data situations that students are likely to face in marketing and consulting jobs. The main topics covered in this course are customer value measurement, segmentation & targeting analysis, positioning analysis, new product design decisions, and new product forecasting models. Students will learn to use several statistics software packages such as MEXL, SPSS, and Number Analytics.
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.
Customer Journey Analytics [22:630:679] *
This course introduces the concept of Customer Journey in the Digital world which spans digital channels (web, mobile, app) and non-digital touchpoints (1:1, call center etc.). Customer Journey Analytics is the process of tracking and analyzing the way customers use combinations of channels to interact with an organization (also known as omnichannel). The focus of the course is to understand every step of the customer journey in today’s digital world using analytics in order to give that customer a much better experience of how we market to them in a channel of their choice. The course combines practical applications and analytics platforms with an end goal of developing skills that help to derive actionable insights that will impact the organization’s acquisition, experience and retention strategies. It provides a broad overview of key digital analytics strategies, concepts, issues, challenges and tools.
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.