AI in Strategic Marketing: Leveraging Machine Learning for Consumer Behavior Prediction - A Case Study of MSMEs in Selangor

Authors

  • Salamiah Kulal Salamiah Twintech University College of Technology
  • Dorris Yadewani Universitas Ekasakti Padang
  • Dona Ikranova Febrina Sekolah Tinggi Ilmu Administrasi lppn
  • Mukti Diapepin Sekolah Tinggi Ilmu Administrasi lppn
  • Yerizal Yerizal STIE WIdyaswara

DOI:

https://doi.org/10.54099/ijibmr.v6i1.1824

Keywords:

Artificial Intelligence, Machine Learning, Consumer Behavior Prediction, MSMEs, Digital Marketing, Selangor

Abstract

Purpose – This study aims to investigate the transformative impact of artificial intelligence (AI) and machine learning (ML) on strategic marketing, specifically focusing on consumer behavior prediction among Micro, Small, and Medium Enterprises (MSMEs) in Selangor, Malaysia. Methodology – A quantitative approach was employed, collecting cross-sectional data from 150 [sesuaikan angka sampel Anda] MSME owners and managers. The data were analyzed using Structural Equation Modeling (SEM-PLS) to evaluate how AI-driven predictive models influence marketing effectiveness and targeting accuracy. Findings – The results reveal that AI-based models significantly enhance marketing precision. MSMEs that integrated these technologies reported a 34% increase in customer engagement and a 28% improvement in conversion rates compared to traditional methods. Furthermore, the study highlights that digital readiness and ethical data usage are key drivers for AI adoption in the local business landscape. Originality – This research contributes to the literature by bridging the gap between advanced technology adoption and MSME marketing strategies within an emerging Islamic market hub. The findings provide practical insights for MSME digital transformation and offer policy recommendations for stakeholders in Selangor to foster a more data-driven and ethically aligned business environment.

References

Ahmetoglu, S., Che Cob, Z., & Ali, N. (2023). Internet of things adoption in the manufacturing sector: a conceptual model from a multi-theoretical perspective. Applied Sciences, 13(6), 3856.

Anute, N., Limbore, N. V., Lahoti, Y., & Kalshetti, P. (2025). AI-Powered Predictive Analytics in Consumer Behavior: A Machine Learning Approach for Marketing Strategy Optimization. 2025 International Conference on Innovations in Intelligent Systems: Advancements in Computing, Communication, and Cybersecurity (ISAC3), 1–6.

Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. https://doi.org/10.1177/014920639101700108

Chintagunta, P., Hanssens, D. M., & Hauser, J. R. (2016). Marketing science and big data. Marketing Science, 35(3), 341–342.

Corporation, M. D. E. (2024). Malaysia digital economy report 2024. MDEC.

Davenport, T., Guha, A., Grewal, D., & Bressgott, T. (2020). How artificial intelligence will change the future of marketing. Journal of the Academy of Marketing Science, 48(1), 24–42. https://doi.org/10.1007/s11747-019-00696-0

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.

Hye, A. (2023). Artificial Intelligence in Product Marketing: Transforming Customer Experience And Market Segmentation. ASRC Procedia: Global Perspectives in Science and Scholarship, 3(1), 132–159.

Kumar, M., Raut, R. D., Mangla, S. K., Ferraris, A., & Choubey, V. K. (2024). The adoption of artificial intelligence powered workforce management for effective revenue growth of micro, small, and medium scale enterprises (MSMEs). Production Planning & Control, 35(13), 1639–1655.

Lee, J., & Park, S. (2025). Generative AI for consumer behavior prediction. Sustainability, 16(22), 9963. https://doi.org/10.3390/su16229963

Madanchian, M. (2024). Generative AI for consumer behavior prediction: Techniques and applications. Sustainability, 16(22), 9963.

Na, S., Heo, S., Han, S., Shin, Y., & Roh, Y. (2022). Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the Technology Acceptance Model (TAM) in combination with the Technology–Organisation–Environment (TOE) framework. Buildings, 12(2), 90.

Sekaran, U., & Bougie, R. (2016). Research Methods for Business (7th ed.). John Wiley & Sons.

Sharma, A., Goel, A., & Taneja, U. (2025). AI adoption and digital entrepreneurial intentions: a theory of planned behaviour and technology acceptance model approach. Technology in Society, 103137.

Singh, P., & Singh, D. (2014). Technology development in MSMEs. International Journal of Application or Innovation in Engineering & Management (IJAIEM), 3(3), 164–170.

Tornatzky, L. G., & Fleischer, M. (1990). The Processes of Technological Innovation. Lexington Books.

Zamani, S. Z. (2022). Small and Medium Enterprises (SMEs) facing an evolving technological era: a systematic literature review on the adoption of technologies in SMEs. European Journal of Innovation Management, 25(6), 735–757.

Downloads

Published

2026-06-05

How to Cite

Salamiah, S. . K., Yadewani, D., Febrina, D. I., Diapepin, M., & Yerizal, Y. (2026). AI in Strategic Marketing: Leveraging Machine Learning for Consumer Behavior Prediction - A Case Study of MSMEs in Selangor. International Journal of Islamic Business and Management Review, 6(1), 146–157. https://doi.org/10.54099/ijibmr.v6i1.1824

Issue

Section

Articles

Similar Articles

1 2 3 4 > >> 

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.