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Market Analysis (Strategic Analytics + Big Data Analysis)

Module name (EN):
Name of module in study programme. It should be precise and clear.
Market Analysis (Strategic Analytics + Big Data Analysis)
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Marketing Science, Master, ASPO 01.04.2016
Module code: MAMS-120
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P420-0019
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
4VU (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 1
Mandatory course: yes
Language of instruction:
German
Assessment:
Written exam (120 minutes / can be repeated semesterly)

[updated 20.11.2019]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

MAMS-120 (P420-0019) Marketing Science, Master, ASPO 01.04.2016 , semester 1, mandatory course
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 135 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
None.
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Stefan Selle
Lecturer:
Prof. Dr. Christian Liebig
Prof. Dr. Stefan Selle
Dozierende des Studiengangs
Nico Krivograd, M.Sc.


[updated 14.09.2021]
Learning outcomes:
  After successfully completing this module, students will:
- be familiar with the current state of the art in strategic market analysis as an interface between marketing and strategic management at an international level, and be able to identify trends and develop new approaches.
- be able to apply a toolkit for the qualitative and quantitative analysis of market participant data (especially target groups, customers, competitors) from primary and secondary sources to specific case studies in the context of the strategic management of international companies.
 
 
- beable to work in a group to structure strategic market analysis questions, analyze data and present and justify recommendations derived from it,
 
 
- be able to outline the principles, concepts and applications of Big Data,
 
- be able to apply data mining methods,
- be able to identify connections to topics such as Cloud Computing, Enterprise 2.0, Analytical CRM etc.,


[updated 20.11.2019]
Module content:
[1] Strategic market analysis
- Relationship and interaction between strategic management and marketing
- Status quo and challenges of market-oriented, strategic management along its process chain and common practice of strategy consulting with regard to new business models
- Cross-industry and specific data-driven management methods for companies
 
  (Concepts and case studies)
- Deepening current topics from the field of market-oriented corporate management, e.g. from the following areas:
  International market entry strategy, configuration and coordination of market development activities,
  approaches and methodology of international market segmentation
 
[2] Big data
- Data management, data protection and data security
- Business Intelligence (BI), Data Warehouse, Online Analytical Processing (OLAP)
- Data Mining (DM): Processes and special procedures (e.g. cluster analysis, association rules, decision tree, etc.)
- Big Data architectures and applications (e.g. Hadoop, MapReduce, NoSQL etc.)
- Cloud computing, social networks, Enterprise 2.0, analytical customer relationship management (CRM)


[updated 20.11.2019]
Teaching methods/Media:
Lecture with integrated practical exercises and case studies on the PC (using MS Excel, SAP BI 7 and Knime)

[updated 20.11.2019]
Recommended or required reading:
[1] Strategic market analysis
-        Evans, J.R.: Business Analytics, Global Edition. 2nd edition. Pearson, 2016.
-        Sharda, R., Delen, D., Turban, E.: Business Intelligence and Analytics. Pearson, 2014.
-        Sharda, R., Delen, D., Turban, E.: Business Intelligence : A Managerial Perspective on Analytics. Pearson, 2014.
-        Maisel, L., Cokins, G.: Predictive Business Analytics: Forward Looking Capabilities to Improve Business Performance. Wiley, 2014.
-
        Siegel, E.: Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die. John Wiley & Sons, Hoboken, 2013.
-
        Hauser, W.J.: Marketing analytics: the evolution of marketing research in the twenty-first century. In: Direct Marketing: An International Journal, Vol. 1 Iss: 1, pp.38 _ 54, 2007. http://dx.doi.org/10.1108/17505930710734125
-
 
        Sorger, S.: Marketing Analytics: Strategic Models and Metrics. CreateSpace Independent Publishing Platform, 2013.
-
        Stähle, W., Conrad, P., Sydow, J.: Management: Eine verhaltenswissenschaftliche Perspektive, 9. Auflage, 2016.
-        Venkatesan, R; Farris, P.; Wilcox, R. T.: Cutting Edge Marketing Analytics: Real World Cases and Data Sets for Hands On Learning. Pearson FT Press, Upper Saddle River, 2014.
[2] Big data
 
 
 
-        Bachmann, R., Kemper, G., Gerzer, T.: Big Data _ Fluch oder Segen?, mitp Verlag, Wachtendonk, 2014.
-        Freiknecht, J.: Big Data in der Praxis, Carl Hanser Verlag, München, 2014.
-        Kießwetter, M., Vahlkamp, D.: Data Mining in SAP NetWeaver BI, Galileo Press, Bonn, 2007.
-        King, S.: Big Data - Potential und Barrieren der Nutzung im Unternehmenskontext, Springer Fachmedien, Wiesbaden, 2014.
-
        Marx Gómez, J. M., Rautenstrauch, C., Cissek, P.: Einführung in Business Intelligence mit SAP NetWeaver 7.0, Springer Verlag, Berlin, 2009.
-
        Müller, R., Lenz, H.-J.: Business Intelligence, Springer Vieweg Verlag, Berlin, 2013.
-        Schmidt-Volkmar, P.: Betriebswirtschaftliche Analyse auf operationalen Daten, Gabler Verlag, Wiesbaden, 2008.
-        Runkler, T.A.: Data Mining, Vieweg+Teubner Verlag, Wiesbaden, 2010.
-        Witten, I.H., Frank, E., Hall, M.A.: Data Mining, 3. Auflage, Morgan Kaufmann, Burlington, 2011.


[updated 20.11.2019]
 
[Fri Apr 19 13:32:29 CEST 2024, CKEY=mmxaxbd, BKEY=msm2, CID=MAMS-120, LANGUAGE=en, DATE=19.04.2024]