Syllabus

Title
0446 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
09/27/24 to 09/27/24
Registration via LPIS
Notes to the course
Subject(s) Master Programs
Course - Marketing Research and Analytics
Dates
Day Date Time Room
Thursday 10/03/24 03:00 PM - 06:30 PM D2.0.030
Thursday 10/10/24 03:00 PM - 06:30 PM D2.0.030
Thursday 10/17/24 03:00 PM - 06:30 PM D2.0.030
Thursday 10/24/24 03:00 PM - 06:30 PM D2.0.030
Thursday 10/31/24 03:00 PM - 06:30 PM D2.0.030
Thursday 11/07/24 03:00 PM - 06:30 PM D2.0.030
Thursday 11/14/24 03:00 PM - 06:30 PM D2.0.030
Thursday 11/21/24 03:00 PM - 06:30 PM D2.0.030
Thursday 11/28/24 03:00 PM - 06:30 PM D2.0.030
Thursday 12/05/24 03:00 PM - 06:30 PM D2.0.030
Thursday 12/12/24 03:00 PM - 06:30 PM D2.0.030
Thursday 12/19/24 03:00 PM - 06:30 PM D2.0.030
Thursday 01/09/25 03:00 PM - 06:30 PM D2.0.030
Thursday 01/16/25 03:00 PM - 06:30 PM D2.0.030

On this page:

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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 units (Chapters: 1-4)

3: The Tidyverse & the Data Science Workflow

4: Comparing groups (5-6)

5: Linear models (7)

6: Non-Linear models and conjoint-analysis (13)

7: Text mining for marketing Applications 

8: Segmentation (11)

9: Positioning

10: Reducing data complexity (8)

11: Final Exam (oral)

12: Presentations

 

 

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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.

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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.

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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.
 
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Assessment

The final grade of the course will depend on

  • in-class coding quiz (10%)
  • presentation of a research paper (15%)
  • conjoint-study project (25%)
  • STP-project (25%)
  • pricing/text-mining/ project (25%)

 

Grading scale:

(1) Excellent: 90% - 100%

(2) Good: 80% - <90%

(3) Satisfactory: 70% - <80%

(4) Sufficient: 60% - <70%

(5) Fail: <60%

 

 
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Readings

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

andreas.mild@wu.ac.at

martin.waitz@wu.ac.at

 

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Other

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

Last edited: 2024-09-17



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