PV250 Marketing Information Systems

Fakulta informatiky
podzim 2023
Rozsah
2/1/0. 3 kr. (plus ukončení). Doporučované ukončení: k. Jiná možná ukončení: z.
Vyučující
Dalia Kriksciuniene, Ph.D. (přednášející), Ing. Leonard Walletzký, Ph.D. (zástupce)
prof. RNDr. Tomáš Pitner, Ph.D. (přednášející)
Bc. et Bc. Klára Kubíčková (pomocník)
Mgr. Zuzana Schwarzová (pomocník)
Ing. Leonard Walletzký, Ph.D. (pomocník)
Garance
Ing. Leonard Walletzký, Ph.D.
Katedra počítačových systémů a komunikací – Fakulta informatiky
Dodavatelské pracoviště: Katedra počítačových systémů a komunikací – Fakulta informatiky
Rozvrh
Út 21. 11. 8:00–11:50 A321, 16:00–19:50 C511, St 22. 11. 8:00–11:50 A321, Út 5. 12. 8:00–11:50 A321, 16:00–19:50 C511, St 6. 12. 8:00–9:50 B411, 10:00–11:50 A321
Omezení zápisu do předmětu
Předmět je nabízen i studentům mimo mateřské obory.
Mateřské obory/plány
předmět má 33 mateřských oborů, zobrazit
Cíle předmětu
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.
Výstupy z učení
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
Osnova
  • 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.
Literatura
    doporučená literatura
  • 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
Výukové metody
Lectures, seminars, lab work training, problem-based learning, case analysis, acquiring hands-on skills on operational and analytical software.
Metody hodnocení
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.
Vyučovací jazyk
Angličtina
Informace učitele
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
Další komentáře
Předmět je vyučován jednou za dva roky.
Předmět je zařazen také v obdobích podzim 2012, podzim 2013, podzim 2014, podzim 2015, podzim 2018, podzim 2019, podzim 2022.
  • Statistika zápisu (nejnovější)
  • Permalink: https://is.muni.cz/predmet/fi/podzim2023/PV250