Marketing research focuses on methods used to understand the changing needs of customers and markets to guide the decision-making of managers. The course emphasizes the data-driven elements of the marketing research process. Through hands-on methods, students will work through research problem formulation, data sources, data collection methods, and analysis.

Marketing analytics is the practice of measuring, managing and analyzing marketing performance to maximize its effectiveness and optimize return on investment (ROI). Understanding marketing analytics allows marketers to be more efficient at their jobs and minimize wasted online and offline marketing dollars. It also provides marketers with the information necessary to help support company investment in marketing strategy and tactics. This course provides the participant with the necessary knowledge and practical insights that will help a marketing manager get more out of available data and take strategic advantage of the analysis. This interactive, participatory course is designed to answer key questions: “What is marketing analytics, how can marketing analytics improve my marketing efforts and how can I integrate marketing analytics into my business?

This course introduces the student to the general theories of Social Media Marketing and its relevance and importance as a Marketing tool. The student will learn how to create campaigns and the strategies and tactics in the most popular social media platforms, as generate reports and actions based on social media analytics.

Current issues in marketing are the focus of the course. Topics have included direct and database marketing, pricing, advanced marketing research and other current issues in marketing based on student and faculty interest.

This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.