1942 Marketing Analytics
Ugurcan Dündar, MSc.
Contact details
Weekly hours
Language of instruction
09/16/22 to 09/19/22
Registration via LPIS
Notes to the course
Day Date Time Room
Monday 10/10/22 11:00 AM - 02:00 PM LC.-1.038
Monday 10/17/22 11:00 AM - 02:00 PM LC.-1.038
Monday 10/24/22 11:00 AM - 02:00 PM LC.-1.038
Monday 11/07/22 11:00 AM - 02:00 PM LC.-1.038
Monday 11/14/22 10:00 AM - 01:00 PM LC.2.064 PC Raum
Monday 11/28/22 10:00 AM - 01:00 PM Online-Einheit
Monday 12/05/22 10:00 AM - 01:00 PM LC.2.064 PC Raum
Monday 12/19/22 10:00 AM - 12:00 PM Online-Einheit

In this course, you will learn about statistical methods most commonly applied to typical problems in digital marketing:

· Exploring your data with graphs, like Histograms, boxplots, scatter plots etc.

· Analyzing group differences with e.g., Chi²-tests, t-tests, or analysis of variance (ANOVA)

· Analyzing correlation and relationships between variables through simple and multiple regression

For this, we will use the statistical software R. R is a language and environment for statistical computing and graphics, is highly extensible though various R packages. This course will softly introduce you to this language and steadily build up your R programming capabilities. With the gained knowledge, you will be ready to undertake your very first own data analysis including the statistical methods most commonly used in the field of marketing.

Learning outcomes

At the end of this course, you will be able to:

· Strategically approach a problem and solve it with the help of data

· Interpret statistical analyses used in the field of (digital) marketing

· Know how to perform exploratory data analyses through graphs using the statistical software R

· Know how to run a first confirmatory analysis on data sets by using the statistical software R

All methods are applied to digital marketing related problems, you might face later on a job in marketing.

Attendance requirements

This course is planned to be held in presence mode (i.e., students attend the lectures at WU). Should unforeseeable events (e.g., pandemic, fire on campus) make that impossible, we will adapt the teaching mode accordingly.

In general, attendance is compulsory for PIs. Attendance is a prerequisite for the completion of the course, and can affect your final grade through the graded participation during sessions. The attendance requirement is fulfilled if you attend at least 80% of the course (i.e., 6 out of 7 sessions). Your attendance will be recorded in every session.

In the exceptional case that you cannot attend a session because of important reasons (e.g., sick leave, quarantine), you should provide proof of it.

Teaching/learning method(s)

The course is taught using a combination of material presented by the lecturer and supported by practical examples and exercises during lectures.

During the sessions, students will apply all covered methods in R. You might consider bringing your personal device to class for your own convenience.


The performance of students is assessed based on various exercises (delivery via Learn@WU) and a final examination:

· Group project (30%)

· Individual programming exercises (20%; 5(+1) x 4%, best 5 out of 6)

· Participation (10%)

· Final exam (40%)

The results for all exercises should be clearly stated in the documents handed in. For a positive grade, students have to fulfill 60% of the requirements.

For this SBWL we have the following scale:

Overall Points                 Grade

< 60%                               fail (5)

60% bis 69,99%               sufficient (4)

70% bis 79,99%               satisfactory (3)

80% bis 89,99%               good (2)

>= 90%                             excellent (1)

Please note that copying/cheating on assignments (e.g., computer exercises, exam) will result in immediate exclusion from the course and a failing grade (5).

1 Author: Institute for Interactive Marketing and Social Media

Marketing Research

Publisher: Institute for Interactive Marketing and Social Media
Edition: 1st
Remarks: Online script
Year: 2020
Content relevant for class examination: Yes
Content relevant for diploma examination: No
Recommendation: Essential reading for all students
Type: Script
2 Author: Field, A., Miles, J., & Field, Z.

Discovering Statistics using R

Publisher: Sage Publications Ltd.
Edition: 1st Edition
Year: 2012
Content relevant for class examination: Yes
Recommendation: Strongly recommended (but no absolute necessity for purchase)
Type: Book
3 Author: DataCamp

Additional R Help can be found here:

Content relevant for class examination: No
Content relevant for diploma examination: No
Recommendation: Reference literature
Type: Script
Recommended previous knowledge and skills

All the material covered in the following courses is considered a prerequisite:

- Mathematik (STEOP)

- Statistik (CBK)

Last edited: 2022-07-07