Modern Studies in Management and Organization

Modern Studies in Management and Organization

New Applications of Artificial Intelligence in Sports Marketing (A Systematic Review of Trends, Opportunities, and Challenges)

Document Type : Review article

Authors
1 PhD in Sports Management, Kermanshah Branch, Azad University, Kermanshah, Iran
2 PhD Student, Sports Management, Kurdistan University, Sanandaj, Iran
10.22034/jmsmo.2025.537681.1036
Abstract
This study aims to systematically review new applications of artificial intelligence in sports marketing and attempts to identify and explain technological trends, benefits, challenges, and opportunities in this field through a comprehensive literature analysis. In order to achieve this goal, 42 scientific articles published between 2013 and 2024 from reputable databases were selected using a systematic review method in accordance with the PRISMA guidelines and reviewed using the content analysis method. The findings show that technologies such as machine learning, natural language processing, machine vision, and recommender systems play a key role in optimizing the fan experience, content personalization, targeted advertising, sentiment analysis, and dynamic pricing. These technologies have not only helped increase digital engagement and fan loyalty, but have also improved the effectiveness of marketing strategies. In contrast, challenges such as the lack of data-driven infrastructure, ethical concerns about privacy, poor data quality, high costs, and cultural resistance to change have created significant barriers to the effective use of AI. This study addresses the gaps in the literature by providing a comprehensive conceptual framework and emphasizes the need to adopt interdisciplinary approaches, invest in new technologies, train expert human resources, and formulate transparent and ethical policies. Ultimately, AI is not only an auxiliary tool, but also a transformative driver in the future of sports marketing that can create a sustainable competitive advantage for sports organizations.
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Akbari, M., Naderi Nasab, M., & Biniaz, S. A. (2024). Entrepreneurial strategies in AI-enhanced sports marketing platforms. Management Strategy and Engineering Science, 6(2), 59–67.http://193.36.85.187:8092/index.php/mses/article/view/62
Alhitmi, H. K., Mardiah, A., Al-Sulaiti, K. I., & Abbas, J. (2024). Data security and privacy concerns of AI-driven marketing in the context of economics and business field: An exploration into possible solutions. Cogent Business & Management, 11(1). https://doi.org/10.1080/23311975.2024.2393743
Bai, Y., Jia, S., Wang, S., & Tan, B. (2020). Customer Loyalty Improves the Effectiveness of Recommender Systems Based on Complex Network. Information, 11(3), 171. https://doi.org/10.3390/info11030171
Basal, M., Saraç, E., & Özer, K. (2024). Dynamic pricing strategies using artificial intelligence algorithm. Open Journal of Applied Sciences, 14, 1963–1978. https://doi.org/10.4236/ojapps.2024.148128
Berry, L. L. (2002). Relationship marketing of services: Perspectives from 1983 and 2000. Journal of Relationship Marketing, 1(1), 59–77. https://doi.org/10.1300/J366v01n01_05
Campaniço, A. T., Valente, A., Serôdio, R., & Escalera, S. (2018). Data’s hidden data: Qualitative revelations of sports efficiency analysis brought by neural network performance metrics. Motricidade, 14, 94–102. https://doi.org/10.6063/motricidade.15984
Chatterjee, R., Roy, S., Islam, S. H., & Samanta, D. (2021). An AI approach to pose-based sports activity classification. In Proceedings of the 8th International Conference on Signal Processing and Integrated Networks (SPIN) (pp. 156–161). IEEE. https://doi.org/10.1109/SPIN52536.2021.9565996
Chen, Y., Prentice, C., Weaven, S., & Hisao, A. (2022). The influence of customer trust and artificial intelligence on customer engagement and loyalty in the home-sharing economy. Frontiers in Psychology, 13, Article 912339. https://doi.org/10.3389/fpsyg.2022.912339
DeZao, T. (2024). Enhancing transparency in AI-powered customer engagement. Journal of AI Research and Web Applications, 3(2), 134–141. https://doi.org/10.69554/PPJE1646
Drayer, J., Shapiro, S. L., & Lee, S. (2012). Dynamic ticket pricing in sport: An agenda for research and practice. Human Movement Studies & Special Education Faculty Publications, 17. https://digitalcommons.odu.edu/hms_fac_pubs/17
Gao, T., Zhang, M., Zhu, Y., Zhang, Y., Pang, X., Ying, J., & Liu, W. (2024). Sports Video Classification Method Based on Improved Deep Learning. Applied Sciences, 14(2), 948. https://doi.org/10.3390/app14020948
Glebova, E., Madsen, D. Ø., Mihaľová, P., Géczi, G., Mittelman, A., & Jorgič, B. (2024). Artificial intelligence development and dissemination impact on the sports industry labor market. Frontiers in Sports and Active Living, 6, 1363892. https://doi.org/10.3389/fspor.2024.1363892
Glebova, E., Su, Y., Desbordes, M., & Schut, P.-O. (2025). Editorial: Emerging digital technologies as a game changer in the sport industry. Frontiers in Sports and Active Living, 7, 1605138. https://doi.org/10.3389/fspor.2025.1605138
Hu, W. (2023). The application of artificial intelligence and big data technology in basketball sports training. ICST Transactions on Scalable Information Systems, 10, e2. https://doi.org/10.4108/eetsis.v10i3.3046
IBM. (2024). Fan engagement and consumption of sports shifting: New opportunities for AI. IBM Corporation. https://newsroom.ibm.com/2024-06-26-IBM-Study-Fan-Engagement-and-Consumption-of-Sports-Shifting,-Reveals-New-Opportunities-for-Technology-Integrations-including-AI
IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems. (2019). Ethically aligned design: A vision for prioritizing human well-being with autonomous and intelligent systems. IEEE. https://globalpolicy.ieee.org/wp-content/uploads/2019/06/IEEE19002.pdf
Jain, R., & Kumar, A. (2024). Artificial Intelligence in Marketing: Two Decades Review. NMIMS Management Review, 32(2), 75-83. https://doi.org/10.1177/09711023241272308 (Original work published 2024)
Karg, A., Shilbury, D., Westerbeek, H., Funk, D.C., & Naraine, M.L. (2022). Strategic Sport Marketing (5th ed.). Routledge. https://doi.org/10.4324/9781003270522
Lin, X., Wang, X., Shao, B., & Taylor, J. (2024). How Chatbots Augment Human Intelligence in Customer Services: A Mixed-Methods Study. Journal of Management Information Systems, 41(4), 1016–1041. https://doi.org/10.1080/07421222.2024.2415773
Mukherjee, A. (2024). Safeguarding marketing research: The generation, identification, and mitigation of AI-fabricated disinformation. arXiv. https://doi.org/10.2139/ssrn.4739488
Nagovitsyn, R., Valeeva, L., & Latypova, L. (2023). Artificial intelligence program for predicting wrestlers’ sports performances. Sports, 11(196). https://www.mdpi.com/2075-4663/11/10/196
Pota, M., Ventura, M., Catelli, R., & Esposito, M. (2021). An Effective BERT-Based Pipeline for Twitter Sentiment Analysis: A Case Study in Italian. Sensors, 21(1), 133. https://doi.org/10.3390/s21010133
Principe, V., Silva, G., Vale, R., & Nunes, R. (2024). Blockchain and sports industry: A systematic literature review of fan tokens and their implications. Retos, 60, 823–840. https://doi.org/10.47197/retos.v60.107125
Rodrigues, A. C. N., Pereira, A. S., Mendes, R. M. S., Araújo, A. G., Couceiro, M. S., & Figueiredo, A. J. (2020). Using artificial intelligence for pattern recognition in a sports context. Sensors, 20(10), 3040. https://www.mdpi.com/1424-8220/20/11/3040
Sarna, B. (2023, August 27). Revolutionizing the sports domain: The impact of natural language processing. Medium. https://medium.com/@bhaveesarna/revolutionizing-the-sports-domain-the-impact-of-natural-language-processing-41805b7ffb32
Sedky, D., Kortam, W., & AbouAish, E. (2022). The role of sports marketing in attracting audiences towards less popular sports. Journal of Humanities and Applied Social Sciences, 4, 113–131. https://doi.org/10.1108/JHASS-04-2020-0059
Xu, T., & Baghaei, S. (2025). Reshaping the future of sports with artificial intelligence: Challenges and opportunities in performance enhancement, fan engagement, and strategic decision-making. Engineering Applications of Artificial Intelligence, 142, 109912. https://doi.org/10.1016/j.engappai.2024.109912
Yu, X., Chai, Y., Chen, M., Zhang, G., Fei, F., & Zhao, Y. (2024). AI-embedded motion sensors for sports performance analytics. In 2024 IEEE 3rd International Conference on Micro/Nano Sensors for AI, Healthcare, and Robotics (NSENS) (pp. 116–119). IEEE.https://ieeexplore.ieee.org/iel8/10560475/10561258/10561438.pdf
Zhao, L. (2023). A hybrid deep learning-based intelligent system for sports action recognition via visual knowledge discovery. IEEE Access, 11, 46541–46549. https://doi.org/10.1109/ACCESS.2023.3275012
Zhou, D., Keogh, J. W. L., Ma, Y., Tong, R. K. Y., Khan, A. R., & Jennings, N. R. (2025). Artificial intelligence in sport: A narrative review of applications, challenges and future trends. Journal of sports sciences, 1–16. Advance online publication. https://doi.org/10.1080/02640414.2025.2518694

  • Receive Date 05 May 2025
  • Revise Date 28 July 2025
  • Accept Date 27 August 2025
  • Publish Date 23 August 2025