Course Detail

Data Science for Marketing Decision Making (37105)

Course Description by Faculty

  • Hitsch, Guenter
  • Content
    Marketing decisions in the era of big data are increasingly based on a statistical analysis of large amounts of transaction and customer data that provides the basis for profitability and ROI predictions. 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 some of the state-of-the-art tools that are 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 database marketing, and elements of digital marketing. The focus throughout is on predicting the impact of marketing decisions, including pricing, advertising, and customer targeting, on customer profitability and the return on investment (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 tools that we will use include SQL and some key packages in R that are designed for big data processing. Second, we will discuss and apply some modern statistical tools building on regression analysis, including Bayesian hierarchical models and 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.

    Format
    • Lectures

    • Discussion

    • Case Studies

  • Prerequisites
    Business 37000 and 41000 (or 41100). Cannot enroll in 37105 if 37103 taken previously: strict.

    Throughout the class the students will write scripts in R, and correspondingly some programming experience (in R or some other language) is necessary.

  • Materials
    Resources
    • Canvas Site Available

  • Grades
    Based on a final take-home exam/project, homework assignments, and class participation. Cannot be taken pass/fail.
    Grades
    • Graded homework assignments

    • Graded attendance/participation

    Assessment & Testing
    • Final exam (take home)

    Restrictions
    • No pass/fail grades

  • Syllabus
  • Winter 2018Section: 37105-01W 1:30PM-4:30PMHarper CenterC01
  • Winter 2018Section: 37105-81W 6:00PM-9:00PMGleacher Center408
Description and/or course criteria last updated: December 13 2016