IDEAS home Printed from https://ideas.repec.org/a/pal/jmarka/v12y2024i2d10.1057_s41270-023-00281-z.html
   My bibliography  Save this article

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

Author

Listed:
  • Kamaal Allil

    (University of Hertfordshire)

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.

Suggested Citation

  • Kamaal Allil, 2024. "Integrating AI-driven marketing analytics techniques into the classroom: pedagogical strategies for enhancing student engagement and future business success," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 142-168, June.
    • Handle: RePEc:pal:jmarka:v:12:y:2024:i:2:d:10.1057_s41270-023-00281-z
    DOI: 10.1057/s41270-023-00281-z
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1057/s41270-023-00281-z
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1057/s41270-023-00281-z?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Kirk Plangger & Dhruv Grewal & Ko Ruyter & Catherine Tucker, 2022. "The future of digital technologies in marketing: A conceptual framework and an overview," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1125-1134, November.
    2. Sumit Maheshwari & Prerna Gautam & Chandra K. Jaggi, 2021. "Role of Big Data Analytics in supply chain management: current trends and future perspectives," International Journal of Production Research, Taylor & Francis Journals, vol. 59(6), pages 1875-1900, March.
    3. Di Gregorio, Angelo & Maggioni, Isabella & Mauri, Chiara & Mazzucchelli, Alice, 2019. "Employability skills for future marketing professionals," European Management Journal, Elsevier, vol. 37(3), pages 251-258.
    4. Bose, Indranil & Chen, Xi, 2009. "Quantitative models for direct marketing: A review from systems perspective," European Journal of Operational Research, Elsevier, vol. 195(1), pages 1-16, May.
    5. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
    6. Adriana Barbeito‐Caamaño & Ricardo Chalmeta, 2020. "Using big data to evaluate corporate social responsibility and sustainable development practices," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 27(6), pages 2831-2848, November.
    7. Ballou, Brian & Heitger, Dan L. & Stoel, Dale, 2018. "Data-driven decision-making and its impact on accounting undergraduate curriculum," Journal of Accounting Education, Elsevier, vol. 44(C), pages 14-24.
    8. Thomas Davenport & Abhijit Guha & Dhruv Grewal & Timna Bressgott, 2020. "How artificial intelligence will change the future of marketing," Journal of the Academy of Marketing Science, Springer, vol. 48(1), pages 24-42, January.
    9. Vlačić, Božidar & Corbo, Leonardo & Costa e Silva, Susana & Dabić, Marina, 2021. "The evolving role of artificial intelligence in marketing: A review and research agenda," Journal of Business Research, Elsevier, vol. 128(C), pages 187-203.
    10. Libai, Barak & Bart, Yakov & Gensler, Sonja & Hofacker, Charles F. & Kaplan, Andreas & Kötterheinrich, Kim & Kroll, Eike Benjamin, 2020. "Brave New World? On AI and the Management of Customer Relationships," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 44-56.
    11. Ming-Hui Huang & Roland T. Rust, 2021. "A strategic framework for artificial intelligence in marketing," Journal of the Academy of Marketing Science, Springer, vol. 49(1), pages 30-50, January.
    12. Dawn Iacobucci & Maria Petrescu & Anjala Krishen & Michael Bendixen, 2019. "The state of marketing analytics in research and practice," Journal of Marketing Analytics, Palgrave Macmillan, vol. 7(3), pages 152-181, September.
    13. Shaila M. Miranda & Carol S. Saunders, 2003. "The Social Construction of Meaning: An Alternative Perspective on Information Sharing," Information Systems Research, INFORMS, vol. 14(1), pages 87-106, March.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Erik Hermann, 2022. "Leveraging Artificial Intelligence in Marketing for Social Good—An Ethical Perspective," Journal of Business Ethics, Springer, vol. 179(1), pages 43-61, August.
    2. Mari, Alex & Mandelli, Andreina & Algesheimer, René, 2024. "Empathic voice assistants: Enhancing consumer responses in voice commerce," Journal of Business Research, Elsevier, vol. 175(C).
    3. Giacomo Zatini, 2025. "Conditions of use and impacts of artificial intelligence in marketing practices: a mixed-method literature review," Italian Journal of Marketing, Springer, vol. 2025(3), pages 293-329, September.
    4. Vinay Singh & Brijesh Nanavati & Arpan Kumar Kar & Agam Gupta, 2023. "How to Maximize Clicks for Display Advertisement in Digital Marketing? A Reinforcement Learning Approach," Information Systems Frontiers, Springer, vol. 25(4), pages 1621-1638, August.
    5. Wenkai Zhou & Chi Zhang & Linwan Wu & Meghana Shashidhar, 2023. "ChatGPT and marketing: Analyzing public discourse in early Twitter posts," Journal of Marketing Analytics, Palgrave Macmillan, vol. 11(4), pages 693-706, December.
    6. Andrea Mauro & Andrea Sestino & Andrea Bacconi, 2022. "Machine learning and artificial intelligence use in marketing: a general taxonomy," Italian Journal of Marketing, Springer, vol. 2022(4), pages 439-457, December.
    7. Morgan M. Bryant & Jen Riley & Tiffanie Turner-Henderson & Dexter Purnell, 2024. "Ready, set, go! Deploying the social listening stoplight activity to teach marketing analytics using qualitative techniques," Journal of Marketing Analytics, Palgrave Macmillan, vol. 12(2), pages 169-181, June.
    8. V. G. P. Lakshika & B. T. K. Chathuranga & P. G. S. A. Jayarathne, 2025. "The evolving role of AI and ML in digital promotion: a systematic review and research agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(2), pages 288-307, June.
    9. Jeon, Yongwoog Andrew, 2022. "Let me transfer you to our AI-based manager: Impact of manager-level job titles assigned to AI-based agents on marketing outcomes," Journal of Business Research, Elsevier, vol. 145(C), pages 892-904.
    10. Rituparna Basu & Md. Nayeem Aktar & Satish Kumar, 2025. "The interplay of artificial intelligence, machine learning, and data analytics in digital marketing and promotions: a review and research agenda," Journal of Marketing Analytics, Palgrave Macmillan, vol. 13(2), pages 267-287, June.
    11. Herhausen, Dennis & Bernritter, Stefan F. & Ngai, Eric W.T. & Kumar, Ajay & Delen, Dursun, 2024. "Machine learning in marketing: Recent progress and future research directions," Journal of Business Research, Elsevier, vol. 170(C).
    12. Jing Chen & Saeed Tajdini, 2025. "A moderated model of artificial intelligence adoption in firms and its effects on their performance," Information Technology and Management, Springer, vol. 26(3), pages 407-419, September.
    13. Erik Hermann & Gizem Yalcin Williams & Stefano Puntoni, 2024. "Deploying artificial intelligence in services to AID vulnerable consumers," Journal of the Academy of Marketing Science, Springer, vol. 52(5), pages 1431-1451, October.
    14. Sagarika Mishra & Michael T. Ewing & Holly B. Cooper, 2022. "Artificial intelligence focus and firm performance," Journal of the Academy of Marketing Science, Springer, vol. 50(6), pages 1176-1197, November.
    15. Paola Cillo & Gaia Rubera, 2025. "Generative AI in innovation and marketing processes: A roadmap of research opportunities," Journal of the Academy of Marketing Science, Springer, vol. 53(3), pages 684-701, May.
    16. Nur Areena Aqilah Mohd Sapri & Asma Nadia Zanol Rashid & Wan Ainol Mursyida Ahmad Tarmizi, 2024. "Leveraging AI for Effective Content Marketing in Libraries: Maximizing User Engagement," International Journal of Research and Innovation in Social Science, International Journal of Research and Innovation in Social Science (IJRISS), vol. 8(9), pages 3510-3521, September.
    17. Hermann, Erik & Puntoni, Stefano, 2024. "Artificial intelligence and consumer behavior: From predictive to generative AI," Journal of Business Research, Elsevier, vol. 180(C).
    18. Leah Warfield Smith & Randall Lee Rose & Alex R. Zablah & Heath McCullough & Mohammad “Mike” Saljoughian, 2023. "Examining post-purchase consumer responses to product automation," Journal of the Academy of Marketing Science, Springer, vol. 51(3), pages 530-550, May.
    19. Li, Chia-Ying & Zhang, Jin-Ting, 2023. "Chatbots or me? Consumers’ switching between human agents and conversational agents," Journal of Retailing and Consumer Services, Elsevier, vol. 72(C).
    20. Ding, Bin & Li, Yameng & Miah, Shah & Liu, Wei, 2024. "Customer acceptance of frontline social robots—Human-robot interaction as boundary condition," Technological Forecasting and Social Change, Elsevier, vol. 199(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pal:jmarka:v:12:y:2024:i:2:d:10.1057_s41270-023-00281-z. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.palgrave-journals.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.