Marketing decisions in the era of big data and artificial intelligence (AI) are based on a statistical analysis of large amounts of transaction and customer data. Using such an analysis we can predict the profitability or ROI of different marketing decisions, such as pricing, customer targeting, or digital advertising.
The goal of this class is to introduce modern data-driven marketing techniques and train the students as data scientists who can analyze data and make marketing decisions using state-of-the-art tools employed in the industry. We will cover a wide range of topics, including demand modeling, the analysis of household-level data, customer relationship management (CRM), and digital marketing.
The focus is on predicting the impact of marketing decisions, including pricing, advertising, and customer targeting, on customer profitability and the ROI from a customer interaction. The students will get immersed in a workflow that begins with the initial processing of the raw data and ends with the implementation of the marketing decision. First, we will learn how to manage and process large databases. The key tools used include some key packages in R that are designed for big data processing. Second, we will discuss and apply modern statistical tools building on regression analysis, including some key tools from the machine learning literature. Finally, we will learn how to implement key marketing decisions based on the statistical analysis of the data.
Note: The broad set of topics in this class overlaps with the topics covered in 37103 (Data-Driven Marketing). However, we will cover these topics at a faster pace and emphasize state-of-the-art techniques that are only briefly surveyed or not covered in 37103. Also, the main goal of the data assignments in 37103 is to make the students familiar with some key concepts in data-driven marketing. This class goes above and beyond this goal and introduces the students to a professional data scientist’s workflow used for marketing decision-making.
