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MBA Marketing Analytics Specialization

Coveted expertise for a high-demand field

Referred to as the “sexiest job of the 21st century” by Harvard Business Review, the field of marketing analytics is witnessing worldwide growth.

Demand for knowledgable managers with ability to use big data analysis to make effective decisions is growing rapidly. McKinsey & Company forecast a shortage of 1.5 million such managers in the United States alone and PwC suggests that firms will therefore need to compete fiercely for individuals with strong analytics skills and business knowledge.

Marketing and customer analytics are becoming instrumental to the functioning of companies in our data-filled age. Having students prepared with an understanding of the fields, and how to use modern tools such as R, is extremely helpful to companies like Lenati. These are the same techniques and tools we use when we at Lenati consult with Fortune 500 companies.
Jonathan Nolis, Director of Insights and Analytics, Lenati

Foster’s Seattle Connection makes marketing undergrads particularly well suited to explore real-world opportunities and challenges from the countless innovative companies using the most innovative practices in business analytics, including Amazon, Microsoft, Expedia, Starbucks, and beyond.

Marketing and customer analytics are becoming instrumental to the functioning of companies in our data-filled age. Having students prepared with an understanding of the fields, and how to use modern tools such as R, is extremely helpful to companies like Lenati. These are the same techniques and tools we use when we at Lenati consult with Fortune 500 companies.
Jonathan Nolis, Director of Insights and Analytics, Lenati

Marketing Analytics courses

To prepare students for these opportunities, the Marketing Analytics Specialization trains students how to use cutting edge analytics to better direct a wide variety of marketing decisions. The specialization in Marketing Analytics consists of four courses: Customer Analytics (MKTG 562), Analytics for Marketing Decisions (MKTG 564), Digital Marketing Analytics (MKTG 566) and Strategic Pricing (MKTG 515). To complete the specialization, students are encouraged to complete at least three of the four courses.

  • Customer Analytics (MKTG 562) introduces statistical modeling and coding techniques that help individuals manage the customer relationship from acquisition to development to retention. Special attention is directed to models that help firms appropriately value customers and target them with the right offer at the right time.
  • Analytics for Marketing Decisions (MKTG 564) identifies analytic models that can be applied to real, large-scale databases to improve and automate firm-level marketing decisions. In particular, analytics are used to improve decisions around product design, pricing, promotion/advertising, and digital and mobile channel management.
  • Digital Marketing Analytics (MKTG 566) covers search and display advertising, email marketing, attribution models, social media strategies, and two-sided platforms. The course takes a quantitative and data-driven approach for analyzing and improving digital marketing strategies.
  • Strategic Pricing (MKTG 515) blends marketing strategy, micro-economic theory, and data analytics to formulate actionable pricing strategies. The course combines cases and data analytics assignments to teach students how to design and execute pricing decisions and co-ordinate these decisions with other marketing decisions.

Note that while these courses are designed to be taken in sequence, they can also be taken as standalone courses (i.e., they are not required as prerequisites for each other).

A common theme throughout the specialization is the use of real data and the implementation of models using the free programming language R. R is quickly becoming the standard in this space and, like Wikipedia, provides an adept user with a very large set of existing code and packages to be used in the quest to extract insights from marketing data. A key benefit of learning to use R is that students can take the models they learned in the specialization to their careers without the need to buy costly software.