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Marketing Analytics

Marketing Analytics
type: Vorlesung (V)
semester: WS 18/19
time: 2018-10-17
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2


2018-10-24
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-10-31
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-11-07
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-11-14
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-11-21
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-11-28
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-11-28
15:45 - 17:15 täglich
20.30 SR -1.025 (UG)
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-12-05
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-12-12
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2018-12-19
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2019-01-09
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2019-01-16
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2019-01-23
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2019-01-30
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2

2019-02-06
09:45 - 11:15 wöchentlich
20.30 SR 2.67
20.30 Kollegiengebäude Mathematik, Englerstr. 2


lecturer: Prof. Dr. Martin Klarmann
sws: 2
lv-no.: 2572170
Bibliography
  • Hanssens, Dominique M., Parsons, Leonard J., Schultz, Randall L. (2003), Market response models: Econometric and time series analysis, 2nd ed, Boston.
  • Gelman, Andrew, Hill, Jennifer (2006), Data analysis using regression and multilevel/hierarchical models, New York.
  • Cameron, A. Colin, Trivedi, Pravin K. (2005), Microeconometrics: methods and applications, New York.
  • Chapman, Christopher, Feit, Elea M. (2015), R for Marketing Research and Analytics, Cham.
  • Ledolter, Johannes (2013), Data mining and business analytics with R, New York.
Content of teaching

In this course various relevant market research questions are addressed, as for example measuring and understanding customer attitudes, preparing strategic decisions and sales forecasting. In order to analyze these questions, students learn to handle social media data, panel data, nested observations and experimental design. To analyze the data, advanced methods, as for example multilevel modeling, structural equation modeling and return on marketing models are taught. Also, problems of causality are addressed in-depth. The lecture is accompanied by a computer-based exercise, in the course of which the methods are applied practically.

Entryrequirements

In order to attend Marketing Analytics, students are required to have passed the course Market Research.

Annotation

For further information please contact the Marketing and Sales Research Group (marketing.iism.kit.edu).

Exchange students can bypass the requirement of passing Market Research if they can prove that they possess sufficient statistical knowledge based on courses attended at their home institution. This will be examined individually by the Marketing & Sales Research Group.

Workload

Total workload for 4.5 ECTS: ca. 135 hours

Aim

Students

  • receive based on the course market research an overview of advanced empirical methods
  • learn in the course of the lecture to handle advanced data collection and data analysis methods
  • are based on the acquired knowledge able to interpret results and derive strategic implications