Managerial Decision Making in Marketing (in English)
- Credits: 8
- Ending: Examination
- Range: 2P + 2C
- Semester: summer
- Year: 1
- Faculty of Commerce
Teachers
Included in study programs
Teaching results
Knowledge: Graduates of the course will learn to work with the most complex and at the same time the most important stage of marketing management, which is a decision-making. The graduate of the course will gain knowledge about qualitative and quantitative methods used in marketing management. The subject is taught in seminars on the basis of solving practical situations with a focus on the chosen industry (e.g., decide on which segments the company should focus on, whether advertising will be effective for the company in relation to costs; similar situations). Graduates will also be introduced to simple predictions in marketing.
Competences: The graduate will be able to formulate a research question and design a research for its examination using empirical methods. They will be able to make decisions based on data analysis and will be familiar with individual marketing areas and tools.
Skills: The graduate can decide on the choice of method of data collection and their detailed analysis. They will master the methods of data processing, can use R software for their decision-making in empirical research. They will be able to independently develop their knowledge in statistical methods and to use modern software, understand empirical studies in marketing and be able to use them in new areas.
Indicative content
The concept and essence of decision making, situations in decision making. Qualitative methods: brainstorming and its modifications, creative techniques, Delphi method, scenarios. Quantitative methods: logistic regression, decision trees, factor analysis, cluster analysis. Predictions in time series.
Support literature
1. BLACK, Ken. Business statistics: for contemporary decision making. Danvers : John Wiley & Sons, 2023. 832 s. ISBN 978-11-199-0546-2.
2. HAIR, Joseph et al. Essentials of Business Research Methods. New York : Routledge, 2023. 508 s. ISBN 978-10-324-2628-0.
3. HEUMANN, Christian – SCHOMAKER, Michael. Introduction to Statistics and Data Analysis. Cham : Springer Nature. 584 s. ISBN 978-30-311-1833-3.
4. PACZKOWSKI, Walter. Business Analytics. Data Science for Business Problems. Cham : Springer Nature, 2021. 387 s. ISBN 978-30-3087-023-2.
5. WICKHAM, Hadley – CETINKAYA-RUNDEL, Mine – GROLEMUND, Garrett. R for data science. Sebastopol : O'Reilly Media, 2023. 578 s. ISBN 978-14-920-9736-5.
Syllabus
1. Introduction to decision-making. The concept and essence of decision making and its importance in the process of marketing management. Situations in decision making. Decision styles. Conflicts in decision making. 2. Qualitative methods in decision making, concept, essence, typology. Delphi method. Scenario method. Panel discussion. 3. Creative techniques in decision making, concept, essence, typology. Brainstorming and its modification. Mind map. Thought chairs. Thought hats. 4. Data mining, machine learning, statistical classification – basic concepts, goals, tasks and methods. 5. Introduction to the program R. Data preparation for analysis (coding, work with missing values, standardization). 6. Decision trees (entropy, information gain, Gini index), branching based on chi-square test. 7. Principal component analysis (use, interpretation of results). 8. Exploratory and confirmatory factor analysis (introduction, use, interpretation of results). 9. Cluster analysis (introduction, clustering procedures and clustering methods). 10. Cluster analysis (introduction, clustering procedures and clustering methods). 11. Logistic regression (introduction, binary logistic regression, model with continuous variables, multiple logistic regression). 12. Logistic regression (introduction, binary logistic regression, model with continuous variables, multiple logistic regression. 13. Introduction to time series forecasting.
Requirements to complete the course
20% continuous semester assessment
20% semester work
60% written exam
Student workload
Workload: 208 hours
Attendance at lectures: 26 hours
Attendance at seminars: 26 hours
Preparation for seminars: 26 hours
Elaboration of a semester project: 26 hours
Preparation of literary research: 19 hours
Preparation for written verification of knowledge: 20 hours
Preparation for the exam: 65 hours
Language whose command is required to complete the course
English
Date of approval: 06.03.2024
Date of the latest change: 28.02.2024