Modeling Marketing Strategies for Small and Medium-Sized Enterprises in the Food Industry Using Reinforcement Learning and Natural Language Processing
Keywords:
Artificial intelligence; Marketing strategy; Small and medium-sized enterprises; Food industry; Deep Q-Network; DistilBERTAbstract
Artificial intelligence (AI) has become a strategic enabler for redesigning marketing practices, particularly for small and medium-sized enterprises (SMEs) in the food industry, where firms face intense competition, resource constraints, heterogeneous customer preferences, and infrastructural limitations. This study developed a context-specific model for improving marketing strategies in food-sector SMEs by integrating Deep Q-Network (DQN) and DistilBERT algorithms. A mixed-methods design was used. In the qualitative phase, semi-structured interviews were conducted with 12 experts and analyzed through thematic network analysis. In the quantitative phase, a questionnaire developed from 25 organizing themes was administered to 384 managers and specialists. Reliability was acceptable to excellent (Cronbach's alpha = 0.78-0.88), and normality was confirmed using the Kolmogorov-Smirnov test (Sig. = 0.08-0.19). The highest mean scores were found for senior management technology adoption (4.25) and marketing-strategy personalization capability (4.25). DQN achieved accuracy of 0.94, MSE of 0.15, F1-score of 0.92, and mean cumulative reward of 98.5. DistilBERT achieved accuracy of 0.91, cross-entropy loss of 0.12, precision of 0.89, and recall of 0.90. The findings suggest that DQN is better suited to dynamic marketing optimization, whereas DistilBERT is more appropriate for text-based customer analytics. The proposed framework provides a practical AI-driven model tailored to Iranian food-industry SMEs. The algorithmic analyses were based on structured questionnaire-derived features and coded textual materials from expert and managerial narratives; therefore, the reported performance should be interpreted as internal model validation rather than evidence from deployed real-time marketing campaigns.
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References
Attride-Stirling, J. (2001). Thematic networks: An analytic tool for qualitative research. Qualitative Research, 1(3), 385-405. https://doi.org/10.1177/146879410100100307
Cochran, W. G. (1977). Sampling techniques (3rd ed.). Wiley.
Creswell, J. W., & Creswell, J. D. (2023). Research design: Qualitative, quantitative, and mixed methods approaches (6th ed.). SAGE.
Creswell, J. W., & Poth, C. N. (2018). Qualitative inquiry and research design: Choosing among five approaches (4th ed.). SAGE.
Davenport, T. H., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171-4186. https://doi.org/10.18653/v1/N19-1423
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT Press.
Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage.
Huang, M.-H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. https://doi.org/10.1007/s11747-020-00749-9
Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2024). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 75, 102716. https://doi.org/10.1016/j.ijinfomgt.2023.102716
Magableh, I. K., Mahrouq, M. H., Ta'Amnha, M. A., & Riyadh, H. A. (2024). The role of marketing artificial intelligence in enhancing sustainable financial performance of medium-sized enterprises through customer engagement and data-driven decision-making. Sustainability, 16(24), 11279. https://doi.org/10.3390/su162411279
Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., Graves, A., Riedmiller, M., Fidjeland, A. K., Ostrovski, G., Petersen, S., Beattie, C., Sadik, A., Antonoglou, I., King, H., Kumaran, D., Wierstra, D., Legg, S., & Hassabis, D. (2015). Human-level control through deep reinforcement learning. Nature, 518, 529-533. https://doi.org/10.1038/nature14236
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill.
OECD. (2021). The digital transformation of SMEs. OECD Studies on SMEs and Entrepreneurship. OECD Publishing. https://doi.org/10.1787/bdb9256a-en
Sanh, V., Debut, L., Chaumond, J., & Wolf, T. (2019). DistilBERT, a distilled version of BERT: Smaller, faster, cheaper and lighter. arXiv. https://arxiv.org/abs/1910.01108
Sharabati, A.-A. A., Ali, A. A. A., Allahham, M. I., Hussein, A. A., Alheet, A. F., & Mohammad, A. S. (2024). The impact of digital marketing on the performance of SMEs: An analytical study in light of modern digital transformations. Sustainability, 16(19), 8667. https://doi.org/10.3390/su16198667
Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction (2nd ed.). MIT Press.
van Hasselt, H., Guez, A., & Silver, D. (2016). Deep reinforcement learning with Double Q-learning. Proceedings of the AAAI Conference on Artificial Intelligence, 30(1), 2094-2100. https://ojs.aaai.org/index.php/AAAI/article/view/10295
Varian, H. R. (2014). Big data: New tricks for econometrics. Journal of Economic Perspectives, 28(2), 3-28. https://doi.org/10.1257/jep.28.2.3
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. https://arxiv.org/abs/1706.03762
