Market Research & Customer Insights
Niveau
1. Study cycle, Bachelor
Learning outcomes of the courses/module
The students:
• know how important marketing is as a customer-oriented way of thinking and corporate philosophy.
• know and understand the basic concepts of marketing.
• are able to apply this knowledge to real issues.
• can name and interpret the elements of the marketing mix for products (4P) or services (7P).
• know about the relevance of the brand for the success of the company.
• can integrate brand-relevant aspects into the marketing mix of a company.
• know how important marketing is as a customer-oriented way of thinking and corporate philosophy.
• know and understand the basic concepts of marketing.
• are able to apply this knowledge to real issues.
• can name and interpret the elements of the marketing mix for products (4P) or services (7P).
• know about the relevance of the brand for the success of the company.
• can integrate brand-relevant aspects into the marketing mix of a company.
Prerequisites for the course
none
Course content
• Significance of market research and integration into the marketing process
• Customer and markets as objects of market research
• Planning and implementation of market research projects
• Creation and programming of questionnaires
• Basic qualitative and quantitative analysis methods
• Application of statistical software (SPSS, R, Stata) for data analysis
• Interpretation of results and derivation of recommendations for action
- Options and sources of secondary market research (e.g. Big Data)
• Customer and markets as objects of market research
• Planning and implementation of market research projects
• Creation and programming of questionnaires
• Basic qualitative and quantitative analysis methods
• Application of statistical software (SPSS, R, Stata) for data analysis
• Interpretation of results and derivation of recommendations for action
- Options and sources of secondary market research (e.g. Big Data)
Recommended specialist literature
• Mooi, Erik et al.: Market Research. Springer (latest edition)
• Runkler, Thomas: Data Analytics. Springer (latest edition)
• 1-2 current articles from professional journals
• Runkler, Thomas: Data Analytics. Springer (latest edition)
• 1-2 current articles from professional journals
Assessment methods and criteria
Portfolio
Language
German
Number of ECTS credits awarded
5
Share of e-learning in %
40
Semester hours per week
3.0
Planned teaching and learning method
Blended Learning
Semester/trimester in which the course/module is offered
4
Name of lecturer
Prof. (FH) Dr. Peter Dietrich
Academic year
Key figure of the course/module
IBS.BBB.04.05
Type of course/module
integrated lecture
Type of course
Compulsory