PV250 Marketing Information Systems

Faculty of Informatics
Autumn 2023
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: k (colloquium). Other types of completion: z (credit).
Teacher(s)
Dalia Kriksciuniene, Ph.D. (lecturer), Ing. Leonard Walletzký, Ph.D. (deputy)
prof. RNDr. Tomáš Pitner, Ph.D. (lecturer)
Mgr. Bc. Klára Kubíčková (assistant)
Mgr. Zuzana Schwarzová (assistant)
Ing. Leonard Walletzký, Ph.D. (assistant)
Guaranteed by
Ing. Leonard Walletzký, Ph.D.
Department of Computer Systems and Communications – Faculty of Informatics
Supplier department: Department of Computer Systems and Communications – Faculty of Informatics
Timetable
Tue 21. 11. 8:00–11:50 A321, 16:00–19:50 C511, Wed 22. 11. 8:00–11:50 A321, Tue 5. 12. 8:00–11:50 A321, 16:00–19:50 C511, Wed 6. 12. 8:00–9:50 B411, 10:00–11:50 A321
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
there are 33 fields of study the course is directly associated with, display
Course objectives
The objective of the study module is to provide theoretical knowledge, applied abilities and practical skills for supporting information needs of marketing specialists for distributing content, managing customer relationships and analysing performance in the online environment, by using technologies, computerized methods and systems.
Learning outcomes
Will be able to discuss, evaluate and select an efficient form of marketing content presentation for its online visibility; Will get theoretical knowledge and practical skills to analyse marketing information by applying online analytical tools; Will understand the principals of business intelligence and will learn skills of marketing reporting; Will gain an overview of application of generative artificial intelligence for marketing processes Will get knowledge, skills able to apply artificial intelligence (AI) methods for marketing data analysis and insights
Syllabus
  • 1.Concepts of marketing, marketing information systems, digital marketing and MARTECH in business and research; 2. Data sources for Marketing information systems, their features and tasks of analytics; 3. Content marketing principles, content dissemination media; 4. Marketing online, analytics, decisions, insights (Task 1: Google Analytics 4, lab work training) 5. Digital marketing technologies, search engine optimization (SEO) and paid advertising (PPC) (Google Ads overview, skills building); 6. Reporting, performance measurement and business intelligence in marketing information systems (Task 2: Power BI for marketing, lab work training); 7. Expert analysis for marketing decisions. Using experience, intuition types of knowledge. Expert analysis methods (lab work training). 8. Machine learning for marketing. Big data approaches (Task 3: Google Big Query environment lab work training and task) 9. Marketing automation, applying generative AI for marketing tasks. Marketing automation tools, functions, case and demo analysis. 10. Marketing information systems research insights.
Literature
    recommended literature
  • Data mining techniquesfor marketing, sales, and customer relationship management. Edited by Gordon S. Linoff - Micahel J. A. Berry. 3rd ed. Indianapolis, Ind.: Wiley Pub., Inc., 2011, xl, 847 p. ISBN 9781118087503. info
Teaching methods
Lectures, seminars, lab work training, problem-based learning, case analysis, acquiring hands-on skills on operational and analytical software.
Assessment methods
The assessment methods: k (3+1 –completion with colloquium) – all course assignments submitted and defended, the scientific paper prepared according to the requirements for inclusion to the scientific conference for students.
Language of instruction
English
Further comments (probably available only in Czech)
The course is taught once in two years.
Teacher's information
Dalia Krikščiūnienė is the professor dr. of Vilnius University (Lithuania) and Researcher at Masaryk University, Faculty of Informatics, Lasaris lab., ERCIM “Alain Bensoussan” fellowship program The detailed profile at: http://www.muni.cz/people/118098 http://web.vu.lt/khf/d.kriksciuniene
The course is also listed under the following terms Autumn 2012, Autumn 2013, Autumn 2014, Autumn 2015, Autumn 2018, Autumn 2019, Autumn 2022.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/fi/autumn2023/PV250