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

Allil, Kamaal (2024) Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success. Journal of Marketing Analytics, 12 (2). pp. 142-168. ISSN 2050-3318
Copy

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.


picture_as_pdf
Paper_-_AI-driven_Marketing_Analytics_Techniques_-_Final.pdf
subject
Submitted Version
copyright
Available under Unspecified

View Download
visibility_off picture_as_pdf

Published Version
lock

Atom BibTeX OpenURL ContextObject in Span OpenURL ContextObject Dublin Core MPEG-21 DIDL Data Cite XML EndNote HTML Citation METS MODS RIOXX2 XML Reference Manager Refer ASCII Citation
Export

Downloads