Graduate Program Certificate:
Digital Marketing

This course covers development and management of digital marketing strategy, and the uses of digital media technology, including: social, mobile, and web to enhance customer equity, brand value, and ROI within the framework of an organization’s overall marketing strategy.

This course prepares students to develop the analytical skills marketers require to monitor, grow, and sustain competitive advantage. Students will develop abilities in aligning business objectives with metrics; utilizing data visualization, modeling, and text mining techniques; analyzing quantitative and qualitative data; and drawing data-driven consumer insights. The applications will emphasize the use of analytics to help make strategic marketing decisions.

This course explores how to use social media marketing to achieve strategic marketing goals. Using a mix of theoretical and practical exercises, students will learn to deploy social media as a strategic marketing asset. Objectives include learning and applying social media principles and evaluating how an organization’s social media presence adds strategic value. Students will also learn to implement a social media plan, connecting strategic goals to tactical objectives and the social media tools used to listen to and engage with consumers. The course also provides the skills needed to manage and measure social media activity.

Text Mining introduces students to methods of analyzing text data to provide business insights for firms with code-free text mining toolkits. The first part of the course focuses on strategic issues as well as learning basics on how to prepare text data for analyses. We will begin by framing text analytics questions, understanding sources of text data, and generating preliminary insights by coding text data. We will learn how to encode and preprocess text data for automated text analyses. Then, the course proceeds to focus on automated text analyses. We will cover explorative text analytics using word analyses (including word frequency analysis, keyword analyses, and text parsing), text visualization, and topic modeling. The last part of the course focuses on text classification and predictions; for example, understanding customer opinions from review comments with sentiment analysis. There are different methods for text classification and predictions, ranging from the very simple to the very sophisticated.  We will cover unsupervised dictionary approach as well as more advanced supervised machine learning approach. Prerequisite: 53:716:502 Business Analytics.

This course focuses on the use of customer databases to develop classification models with extensive use of SPSS and related software packages. The content includes a comparison of traditional RFM (regency/frequency/monetary) versus other more advanced approaches, such as logistic regression and decision trees, in maximizing the profitability of marketing campaigns. Students will learn how to build models that predict buyer behavior, as well as assess and improve model performance.

Note: Students must purchase SPSS Premium software package from Rutgers software portal.