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No. 7 (2025)
Marketing

Articles

DEVELOPMENT OF METHODOLOGICAL APPROACHES TO INCREASE THE EFFICIENCY OF USING COMPETITIVE INTELLIGENCE SYSTEMS IN ENTERPRISES

Published 2025-07-31

Authors
Tursunxoʻjayev Sardor Jamoliddin oʻgʻli
Affiliation
Scientific secretary of the Center for Competition Policy and Consumer Rights Research under the Committee for the Development of Competition and Protection of Consumer Rights of the Republic of Uzbekistan, PhD
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Abstract

The article explores the potential of advanced technologies for developing competitive intelligence systems in enterprises. The advantages of Attention -LSTM and transfer learning models in predicting customer behavior and forecasting demand for new product s are highlighted. It is shown that machine learning technologies and the FBS -SE methodology play a key role in increasing production efficiency and accelerating decision -making. Digital transformation and green innovations are found 305 to have a positive impact on sustainable development and competitiveness. The research results provide practical recommendations for enhancing the effectiveness of competitive intelligence systems.

Keywords

competitive intelligence, artificial intelligence, machine learning, transfer learning, Attention -LSTM, digital transformation, FBS -SE methodology

References

  1. Kasemrat, R., & Kraiwanit, T. (2025). Attention -enhanced LSTM for high - value customer behavior prediction: Insights from Thailandʼs e -commerce sector. Intelligent Systems with Applications, 26, 200523. https://doi.org/10.1016/j.iswa.2025.200523
  2. Ahmed, S., Chakrabortty, R. K., & Essam, D. L. (2025). A multi -source multi - layer -based transfer learning approach for forecasting customer demands of newly launched products. Intelligent Systems with Applications, 27, 200548. https://doi.org/10.1016/j.iswa.2025.200548
  3. Ahmed, Y . S., & Milani, A. S. (2025). A review of strategies, challenges, and ethical implications of machine learning in smart manufacturing. Decision Analytics Journal, 16, 100591. https://doi.org/10.1016/j.dajour.2025.100591
  4. Liu, Y ., Wang, J., Li, Y ., Dong, W., & Dang, L. (2025). Research on function - behavior -structure -based software engineering methodology for software defined systems. Space Habitation, 1, 100003 . https://doi.org/10.1016/j.spaceh.2025.100003
  5. Żywiołek, J., Wolniak, R., & Grebski, W. W. (2025). From traditional to digital: The paradigm shift in the energy sector through green innovation. Energy Reports, 14, 1–16. https://doi.org/10.1016/j.egyr.2025.05.080
  6. Nugroho, H. Y . S. H., Basuki, T. M., Pratiwi, E., Savitri, E., et al. (2025). Advancing air quality monitoring systems towards sustainable green development: Insight for metropolitan cities in Indonesia. Environmental and Sustainability Indicators, 26, 100649. https://doi.org/10.1016/j.indic.2025.100649
  7. Shokrollahi, M. (2025). Studying the role of organizational culture and artificial intelligence integration in business performance. Journal of Investment Banking and Finance. Retrieved from https://www.researchgate.net/publication/393902800
  8. Achanta, P. R. D. (2024). Operationalizing AI with MongoDB Atlas to streamline scalable NoSQL intelligence. ResearchGate. Retrieved from https://www.researchgate.net/publication/393844296

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