Syllabus

Title
0544 Marketing Research and Analytics (MRA)
Instructors
ao.Univ.Prof. Dr. Andreas Mild, Dr. Martin Waitz
Contact details
andreas.mild@wu.ac.at ; martin.waitz@wu.ac.at
Type
PI
Weekly hours
4
Language of instruction
Englisch
Registration
09/27/22 to 09/27/22
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Course - Marketing Research and Analytics
Dates
Day Date Time Room
Thursday 10/06/22 03:00 PM - 06:30 PM D2.0.030
Thursday 10/13/22 03:00 PM - 06:30 PM D2.0.030
Thursday 10/20/22 03:00 PM - 06:30 PM D2.0.030
Thursday 10/27/22 03:00 PM - 06:30 PM D2.0.030
Thursday 11/03/22 03:00 PM - 06:30 PM D2.0.030
Thursday 11/10/22 03:00 PM - 06:30 PM D2.0.030
Thursday 11/17/22 03:00 PM - 06:30 PM D2.0.030
Thursday 11/24/22 03:00 PM - 06:30 PM D2.0.030
Thursday 12/01/22 03:00 PM - 06:30 PM D2.0.030
Thursday 12/15/22 03:00 PM - 06:30 PM D2.0.030
Thursday 12/22/22 03:00 PM - 06:30 PM D2.0.030
Thursday 01/12/23 03:00 PM - 06:30 PM D2.0.030
Thursday 01/19/23 03:00 PM - 06:30 PM D2.0.030
Thursday 01/26/23 03:00 PM - 06:30 PM D2.0.030

On this page:

to top
Contents

The course gives an introduction into Marketing Research and Analytics with an emphasis on problems in Retailing. All analyses will be done using R.

 

Topics:

1: Course overview

2: Self study unit (Chapters: 1-3)

3: Correlation (4)

4: Comparing Groups (5,6)

5: Linear models (7)

6: Neural networks

7: Segmentation (11)

8: Positioning

9: Reducing data complexity (8)

10: Conjoint analysis (13)

11: Final Exam (oral)

12: Presentations

 

 

to top
Learning outcomes

After completing this course students will have a basic knowledge of fundamentals of Data Analysis for Marketing problems. They are able to describe and visualize data with R and apply models for advanced marketing applications.  Students will learn about decision problems in retailing like pricing, assortment or advertising planning. Beside an understanding of the problem structure, students will learn to apply mathematical and statistical tools to support decision making. Apart from that, completing this course will contribute to the students’ ability to efficiently work and communicate in a team, work on solutions for complex practical problems by using modern statistical software.

to top
Attendance requirements

According to the examination regulation full attendance is intended for a PI. Absence in one unit is tolerated if a proper reason is given.

to top
Teaching/learning method(s)
The course will combine alternative ways to deliver the different topics to the students. One the one hand, a classical lecture style approach where the instructor presents theoretical insight into this topic will be used; one the other hand, students will have to solve assignments as homeworks and work on the simulation within the teams.
 
to top
Assessment

The final grade of the course will depend on

  • final oral exam (50%)
  • group project (20%)
  • presentation of a group project (10%)
  • in-class assignments (2x 10%)

 

Please note that there will be no possibility to retake the final exam. The assessment of assignments is based on a regular grading scheme that is indicated with the sample problems.

 

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

 

 
to top
Readings

Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

to top
Recommended previous knowledge and skills
  • Basic knowledge in statistics
  • Basic knowledge in R
  • Basic knowledge in operations management
to top
Availability of lecturer(s)

andreas.mild@wu.ac.at

martin.waitz@wu.ac.at

 

to top
Other

program's website: www.wu.ac.at/master/scm

Last edited: 2022-08-31



Back