Syllabus

Title
5515 Digital Marketing Group B
Instructors
Simon Stiebellehner, M.Sc., Univ.Prof. Dr. Nadia Abou Nabout, Sila Ada, M.S., Assoz.Prof PD Dr. Baris Pascal Güntürkün
Contact details
Type
PI
Weekly hours
2
Language of instruction
Englisch
Registration
02/05/18 to 02/23/18
Registration via LPIS
Notes to the course
Dates
Day Date Time Room
Tuesday 03/13/18 10:00 AM - 01:00 PM TC.2.02
Tuesday 03/20/18 03:30 PM - 06:30 PM TC.4.03
Tuesday 04/10/18 04:00 PM - 07:00 PM TC.5.12
Tuesday 04/17/18 03:00 PM - 06:00 PM TC.4.01
Tuesday 04/24/18 03:00 PM - 06:00 PM TC.2.02
Tuesday 05/15/18 03:00 PM - 06:00 PM D1.1.074
Tuesday 05/22/18 03:00 PM - 06:00 PM TC.4.13
Tuesday 06/05/18 10:00 AM - 01:00 PM TC.2.02
Contents

*** Please read the syllabus (pdf document) uploaded under >>Learning Activities>> carefully! ***

This class covers the most important digital marketing instruments (i.e., traditional banner advertising, targeted banner advertising, real-time bidding, search engine advertising, affiliate marketing, viral marketing, and influencer marketing) and aims to teach students the skills needed to analyze digital marketing campaigns using state-of-the-art software tools such as Tableau with the aim to make better marketing decisions.

Learning outcomes

After successful completion of the course, the learned is expected to be able to:

  1. Use digital marketing jargon (i.e., CPC, CPM, CPO, CTR, CR, etc.) and comfortably participate in discussions with digital marketing professionals.
  2. Explain how digital marketing instruments work (i.e., traditional banner advertising, targeted banner advertising, real-time bidding, search engine advertising, affiliate marketing, viral marketing, and influencer marketing) and how they differ in their mechanics and revenue models.
  3. Understand how data is crucial to the success of digital marketing and know how to use it in order to make better marketing decisions.
  4. Analyze digital marketing campaigns and assess their profitability using data.
  5. Analyze and visualize data using software tools such as Tableau.
Teaching/learning method(s)

The course is taught using a combination of interactive lectures, class discussions, and student presentations.

Assessment

The course has 4 ECTS, which translates into a workload of 100 hours, 24 hours of which are contact hours in the classroom. That means that you’ll need to work for about 76 hours outside the classroom. The workload (in hours) that you can anticipate for every grading component is approximately proportional to the weight that each grading component has for your final grade (i.e., expect a workload of approx. 7.5 hours for a case that has a 10% weight for your final grade).

Weights for each grading component used to compute your final grade: 

  • 40% of the final grade is based on the final exam
  • 25% of the final grade is based on the case presentation (Air France or Star Digital)
  • 20% of the final grade is based on the case write-ups, i.e.,
    • 10% of the final grade is based on MedNet
    • 10% of the final grade is based on Ford Fiesta
  • 10% of the final grade is based on completion of exercises
  • 5% of the final grade is based on in-class participation

    Please note that to ensure an equal contribution of group members for the group assignment, a peer assessment will be conducted among group members, which enters into the computation of the individual grades for your group work. This means that the members of a group are required to assess other students regarding their relative contribution.
Availability of lecturer(s)

I am happy to answer your questions so feel free to send me a short email if you would like to talk to me in person. I will also try to be available in the classroom after each class or during the breaks of each class.

Please do not expect that I answer long emails with numerous questions to topics of the class! It is usually much more productive to personally meet and discuss your questions.

Other

 

Last edited: 2017-10-30



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