(spring semester lecture and exercises)
"Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful."Chip & Dan Heath
This course is an introduction to marketing analytics. Today, companies heavily rely on data-driven marketing to better understand the needs of their customers. Through various data collection methods, they gather data on, e.g., purchase behavior, social relationships, or attitudes. By analyzing such data, firms gain market insights and can enhance marketing decisions such as segmentation and targeting of customers, positioning of products based on customer preferences, or developing the right marketing mix. This class offers a practical introduction into understanding, applying, interpreting, and documenting quantitative research methods to analyze Marketing data.
Dr. Margot Löwenberg
Katherine Rother (TBC!)
Lecture and exercises in form of a 5 day course.
Bachelor students assigned to the “Wahlpflichtbereich" BWL 4.
Each spring semester
Work load statement:
|Course Preparation and R-Intro Exam Preparation||25h|
|Take home assignment||25h|
Practical introduction into understanding, applying, interpreting and documenting quantitative market research methods to analyze marketing data using R and R Studio.
Field A, Miles J, Field Z (2012), Discovering Statistics Using R, 1st ed., London: Sage.
Hair JF, Black WC, Babin BJ, Anderson RE (2014), Multivariate Data Analysis. A Global Perspective, 7th ed., Upper Saddle River: Pearson.
Stock JH, Watson MM (2012), Introduction to Econometrics, 3rd ed., Boston: Pearson.
Additional literature will be given in class.
Basic knowledge in R is required for the Marketing Analytics class. The required skills are on par with our class "R - A non-technical introduction". Participants that did not participate in the R course are required to complete online exercises from https://www.datacamp.com/ before the start of the course (which consist of a workload of approximately 20 - 25 hours for beginners).
To avoid any issues with updates on https://www.datacamp.com/, we will communicate the required exercises 3 weeks prior to the start of the course through the Olat platform. Students that register on Olat before this time will automatically receive free access to the DataCamp courses. Exchanges student that do not yet have access to Olat and students that register after this period should sent an e-mail to email@example.com to receive access.
An exam will take place during the first part of the course to examine the R skills of the participants. The grade of the exam will count for 15% of the final grade.
Installation of the necessary software: Please visit http://cran.r-project.org as well as http://www.rstudio.com and ensure that you have successfully installed the latest versions of R and R Studio on your personal laptop.
Officially register using the booking tool at the University of Zurich. An individual application for this class is not necessary.
In addition, students have to sign the official registration form for modules finished before the official module booking deadline to avoid cancellations after grading on the 2nd day of class. After this date, it is not possible anymore to cancel participation. Note: students who signed the form will receive an official grade, even when they have not registered through the official booking tool.
Grading: R exam, in-class exercise, take home assignment
15% R basics exam
35% in class exercise
50% take home assignment
Important information for the R-intro exam
To prepare for the R exam on the 2nd day of class, please go through the following exercises on datacamp.com before the start of the class:
Entire course: Introduction to R (link)
Entire course: data manipulation in R with data.table (link)
Chapter 1, introduction: data visualization with R (link)
Chapter 1, introduction: data visualization with ggplot2-1 (link)
In case you do not yet have access to the datacamp courses, send an e-mail to firstname.lastname@example.org
Dates and Location
Block-course: 10 - 14 February 2020, 09:00 - 17:00
Location:AND 4.06 Vorlesungsverzeichnis
This information in the syllabus supports the official information in the electronic university calendar (VVZ – Vorlesungsverzeichnis). In cases of doubt, the official information at the VVZ is valid.
The Syllabus can be downloaded here (PDF, 2 MB).