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.

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.

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.

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).

Alexander Kulumbeg, MSc (WU)

Vienna University of Economics and Business (WU), D2.1.494, Welthandelsplatz 1, 1020 Vienna,

email: alexander.kulumbeg@wu.ac.at, T +43 1 31 336 5539