Syllabus

Title
0753 Retail Marketing 2 (RM 2)
Instructors
ao.Univ.Prof. Dr. Andreas Mild, Dr. Martin Waitz
Contact details
martin.waitz@wu.ac.at, andreas.mild@wu.ac.at
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
09/23/19 to 09/27/19
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Course II - Retail Marketing
Dates
Day Date Time Room
Thursday 11/28/19 01:30 PM - 05:00 PM D2.0.038
Thursday 12/05/19 01:30 PM - 05:00 PM D2.0.038
Thursday 12/12/19 01:30 PM - 05:00 PM D2.0.038
Thursday 12/19/19 01:30 PM - 05:00 PM D2.0.038
Thursday 01/09/20 01:30 PM - 05:00 PM D2.0.038
Thursday 01/16/20 01:30 PM - 05:00 PM D2.0.038
Thursday 01/23/20 01:30 PM - 05:00 PM D2.0.038

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. In this part of the course, linear models, Data complexity reduction techniques, segmentation analysis and market basket analysis will be covered. 

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%)
  • homework assignment (25%)
  • presentation of a group project (25%)

 

Please note that there will be no possibility to retake the final exam. The assessment of the homework 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%

Prerequisite for passing the course: minimum performance of 40% in the final examination.

to top
Prerequisites for participation and waiting lists

Conditions:

  • Completion of Retail & Marketing 1.
  • Class attendance in all sessions is mandatory! 

 

Prerequisites:

  • Basic knowledge in statistics
  • Basic knowledge in R
  • Basic knowledge in operations management

to top
Readings
1 Author: Chapman/MCDonnell Feit
Title: R for Marketing Research and Analytics

Publisher: Springer
Year: 2015
Content relevant for class examination: Yes
Recommendation: Essential reading for all students
to top
Availability of lecturer(s)

martin.waitz@wu.ac.at

to top
Other
program's website: www.wu.ac.at/master/scm
Last edited: 2019-04-02



Back