This course covers a set of quantitative research methods considered to be important for both marketing researchers and practitioners. Such methods include classical techniques like linear and logistic regression analysis as well as contemporary data science methods from the field of data mining and artificial intelligence focusing supervised and unsupervised learning methods as well as market basket analysis and recommender systems.

The course builds on the content already covered in the "Marketing Research and Data Analysis" and "Marketing Engineering" courses, but expands them along two dimensions: (1) The conceptual and statistical assumptions underlying methods discussed in previous classes are challenged and the portfolio of available data analytical methods is extended. (2) The course offers opportunities to train the application of these methods in various marketing decision making contexts and to derive managerial conclusions.

The emphasis in this course is on a more thorough understanding of advanced marketing research techniques and to demonstrate how these techniques are necessary and useful in specific marketing applications.

By the end of this course, students will be able to:

• Be comfortable with R and RStudio
• Understand the assumptions, limitations and possible extensions of standard marketing research methods
• Integrate the components discussed in this course into the topics covered in the basic marketing research course
• Understand the practical importance and relevance of the discussed methods in real-world marketing decision making contexts
• Further improve their presentation and team working skills

Full attendance is compulsory. This means that students should attend at least 80% of all lectures, at most one lecture can be missed.

The course is taught using a mix of interactive lectures, business cases and class discussions and student presentations.

The structure of this course consists of two building blocks:

The first part contains sessions on various quantitative research methods. Students will have to be prepared for class discussion. While in the first half of each session we will discuss the conceptual idea, the statistical properties, and interpretative aspects of the focused method, the second half trains students to use and apply it in a marketing context. The latter is accomplished by data case assignments, which are to be completed as a homework assignment.

The second part builds on the introductory sessions but is organized in breakout groups. Each group of students will work on an empirical marketing research project which mimics a real-world marketing research issue using the concepts and methods learned throughout the program. The findings will be presented in a final presentation session.

The course covers practical applications of data analytics using the open source language R. Students should have R and RStudio installed before the start of the class:

• Download R: https://cran.r-project.org/
• Download RStudio: https://www.rstudio.com/products/rstudio/download/

To facilitate the learning process you will obtain full access to the entire DataCamp (www.datacamp.com) course curriculum for the duration of the course. 

Grading is based on the following components:

• Empirical marketing research project (group work) [weight: 35%]
• Data assignments (analysis of data sets, group work) [weight: 20%]
• Final exam (concepts & methods) [weight: 35%]
• Class participation (quantity & quality of contributions during the weekly sessions) [weight: 10%]

To successfully pass this course, your weighted final grade needs to exceed 60%.