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| Module code: MAMS-120 |
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4VU (4 hours per week) |
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6 |
| Semester: 1 |
| Mandatory course: yes |
Language of instruction:
German |
Assessment:
Written exam (120 minutes / repeated semesterly)
[updated 05.06.2025]
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MAMS-120 (P420-0019) Marketing Science, Master, ASPO 01.04.2016
, semester 1, mandatory course
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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.
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Recommended prerequisites (modules):
None.
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Recommended as prerequisite for:
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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]
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Learning outcomes:
[1] Strategic market analysis
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.
- be able to work in a group to structure strategic market analysis questions, analyze data and present and justify recommendations derived from it.
[2] Big data / Data science
After successfully completing this module, students will be able to use suitable methods of data analysis to gain knowledge for decision-making in practical questions.
They will have fundamental knowledge of various types of machine learning: unsupervised learning, supervised learning, reinforcement learning.
Students will be able to apply the Cross Industry Standard Process for Data Mining (CRISP-DM) methodology to carry out data analysis in a structured and systematic manner.
They will be familiar with different types of characteristics (e. g. nominal, ordinal, metric) and can preprocess data appropriately (e. g. by One Hot Encoding, normalization or standardization).
Students will be able to select appropriate decision-making procedures (e.g. regression or classification in the field of supervised learning) for specific problems.
Students will be able to implement the methods they have learned using a suitable tool (e.g., KNIME Analytics Platform), carry out parameter studies, and critically evaluate the results obtained using defined quality criteria.
Students will be able to prepare the insights gained from the data in a suitable manner (e.g., in the form of a visualization) and document them (e.g., in a project report or project diary) so that they can ultimately be presented to a selected audience (e.g., decision-makers within the company) in an understandable way.
[updated 05.06.2025]
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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 science
1. Introduction
2. Basics
3. Data analysis process
4. Preprocessing data
5. 5.3 Association rule learning
6. Cluster analysis
7. Classification
8. Regression
9. Ensemble learning
10. Artificial neural networks
[updated 05.06.2025]
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Teaching methods/Media:
Inverted / flipped classroom with eLearning support (for example: LMS Moodle): Specially prepared documents (for example: lecture notes) / self-study media (for example: videos) on technical and methodological knowledge.
Lab course with exercises: Independent work on the PC to solve business-related tasks by applying the methods learned with the help of suitable tools (for example: KNIME Analytics Platform).
[updated 05.06.2025]
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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 / Data science
Udo Bankhofer und Jürgen Vogel: Datenanalyse und Statistik – Eine Einführung für Ökonomen im Bachelor, Gabler Springer Verlag, Wiesbaden, 2008.
Michael R. Berthold, Christian Borgelt, Frank Höppner, Frank Klawonn, Rosario Silipo: Guide to Intelligent Data Science – How to Intelligently Make Use of Real Data, 2nd edition, Springer, Berlin, 2020.
Uwe Haneke, Stephan Trahasch, Michael Zimmer, Carsten Felden: Data Science – Grundlagen, Architekturen und Anwendungen, dpunkt Verlag, Heidelberg, 2019.
Annalyn Ng und Kenneth Soo: Data Science – Was ist das eigentlich?! – Algorithmen des maschinellen Lernens verständlich erklärt, Springer Verlag, Berlin, 2018.
Forster Provost & Tom Fawcett: Data Science for Business. What you need to know about Data Mining and Data-Analytic Thinking, O’Reilly Verlag, Sebastopol, 2013.
Thomas A. Runkler: Data Mining – Methoden und Algorithmen intelligenter Datenanalyse, Vieweg+Teubner Verlag, Wiesbaden, 2010.
Ian H. Witten, Eibe Frank, Mark A. Hall, Christopher J. Pal: Data Mining – Practical Machine Learning Tools and Techniques, 4th edition, Morgan Kaufmann, Burlington, 2016.
[updated 05.06.2025]
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