The course provides a rigorous coverage of a broad range of theories, frameworks, concepts, and tools to truly get into the hearts, minds, and brains of consumers and uncover insights that are relevant for business and policy.

Students will learn to integrate marketing communication elements (e.g., advertising, public relations, publicity, sales promotion, event marketing, direct marketing, e-communication, and selling) to advance an organization's success and brand equity. Students will also learn how to manage a brand on social media - developing a comprehensive social media strategy. Through lecture, discussions, and case studies, students will learn to develop various communication and social media strategies for specific industries.

The course examines digital marketing strategy, implementation and executional considerations for brands and provides a detailed understanding of all digital channels and platforms. Participants will complete the course with a comprehensive knowledge of and experience with how to develop an integrated digital marketing strategy, from formulation to implementation. Web Analytics is the discipline of analysis that focuses on data.

Branding is a course that provides students with an overview of key strategic branding decisions faced by organizations. The objectives of the course are to prepare students to 1) address important issues in planning and evaluating brand strategies; 2) provide the appropriate theories, models and tools to make better branding decisions; and 3) provide a forum for students to apply these principles. Particular emphasis is placed on understanding psychological principles at the customer level that will improve managerial decision-making regarding brands.

This course is designed to provide students with an understanding of the role of marketing analytics in making key marketing decisions. It covers methods geared towards understanding consumers, such as segmentation, cluster, and customer lifetime value analysis, and those geared towards assessing the effectiveness of the marketing-mix. Besides analyzing data, the class further focuses on skills involving visualizing said data to convey information to marketing decision-makers.

The course will explore the dimensions of the social and environmental responsibilities of business. Far from a course in philosophy, this course is grounded in the realities of the for-profit enterprise. We will explore the relationship between CSR and business performance, and how CSR can create value for a range of stakeholder and, subsequently, the firm.

This course is designed to provide and augment an understanding of consumer behavior research within the marketing field. The course has several specific goals. First, you will become familiar with the literature examining consumer behavior. Second, you will develop the ability to identify and research questions with potential to add to this literature. Third, you will develop ability to critically consider others' contributions to the literature. Fourth, you will develop the ability to present your research ideas in both written and oral form.

This is an advanced graduate seminar involving intensive reading and criticism of current research drawn primarily from the top experimental psychology, marketing, and consumer research journals. The instructor assumes student familiarity with basic consumer behavior theory (i.e., material covered in MKTG 710 and 711), philosophy of science issues, and essential data analysis tools (e.g., ANOVA, regression). The focus of the course is on the review of recent consumer psychology empiricism with an emphasis on the cognitive mechanisms underlying consumer behavior phenomena.

In specific locations around the globe, revenue generated from mining certain minerals has been used to fund controversial activities, exacerbating regional conflicts. This course focuses on these 'Conflict Minerals,' past and present.

An introduction to experimental design. Topics include a review of statistical inference on a single sample, statistical inference on two samples, analysis of variance, randomized blocks, Latin squares, two- factor factorial design, the general factorial design, 2k factorial design, blocking and confounding in the 2k factorial design, two-level fractional factorial design, response surface methods, experiments with random factors. Course emphasizes mining applications.