Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success

Kamaal Allil ()
Additional contact information
Kamaal Allil: University of Hertfordshire

Journal of Marketing Analytics, 2024, vol. 12, issue 2, No 4, 142-168

Abstract: Abstract This paper outlines a practical pedagogical framework for integrating AI-driven analytics into marketing education, tailored to equip students for the fast-evolving industry. Central to this approach is an iterative model that adapts teaching strategies to keep pace with technological advancements and industry demands. The framework emphasizes practical application, steering curriculum development towards the inclusion of AI tools like machine learning and predictive analytics, and crafting experiential learning opportunities. A focused examination of current teaching methods reveals gaps and introduces actionable solutions for fostering analytical skills essential for the AI-enhanced marketing landscape. The model not only advocates for a balance between theory and practice but also addresses challenges such as resource accessibility and the necessity of ethical considerations in AI education. By promoting interdisciplinary collaboration and continual curriculum refreshment, the paper positions the model as an essential blueprint for nurturing future marketing professionals capable of leveraging AI analytics for strategic decision-making. The conclusion calls for academia-industry partnerships to further enrich marketing education and underscores the importance of this framework in preparing students for successful careers in AI-driven marketing.

Keywords: AI-driven marketing analytics; Marketing education; Pedagogical strategies; Curriculum integration; Classroom activities; Industry-academia collaboration (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41270-023-00281-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:jmarka:v:12:y:2024:i:2:d:10.1057_s41270-023-00281-z

Ordering information: This journal article can be ordered from
http://www.springer. ... gement/journal/41270

DOI: 10.1057/s41270-023-00281-z

Access Statistics for this article

Journal of Marketing Analytics is currently edited by Maria Petrescu and Anjala Krishnen

More articles in Journal of Marketing Analytics from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().