Examining the critical success factor in an intelligent marketing system based on digital technology
Keywords:
Intelligent Marketing System, Digital Technology, Success Parameters, Data-Driven Marketing PersonalizationAbstract
Integrating digital technology with intelligent marketing systems has revolutionized how organizations engage with customers, enabling data-driven strategies to optimize marketing outcomes. This study examines the critical parameters that contribute to the success of intelligent marketing systems, focusing on key factors such as data quality, algorithmic efficiency, personalization, user experience, and system adaptability. The research identifies best practices and emerging trends in deploying intelligent marketing systems through a comprehensive literature review and analysis of case studies. Findings highlight the importance of integrating real-time analytics, robust data governance frameworks, and seamless interoperability between marketing tools to enhance decision-making and customer satisfaction. The results provide actionable insights for businesses leveraging digital technology for sustainable competitive advantage in dynamic market environments.
References
Aliahmadi, M. H., Movahed, A. B., Movahed, A. B., Nozari, H., & Bayanati, M. (2024). Hospital 6.0 Components and Dimensions. In Advanced Businesses in Industry 6.0 (pp. 46-61). IGI Global.
Brown, T., Gupta, S., & Liu, Y. (2022). Enhancing Algorithmic Efficiency in Intelligent Marketing Systems. Journal of Marketing Technology, 45(2), 123-139.
Chen, X., Zhang, L., & Li, P. (2021). Data Quality and Its Impact on Intelligent Marketing Systems. International Journal of Data Science, 34(1), 89-105.
Gupta, S., Liu, Y., & Smith, K. (2020). Designing User-Friendly Interfaces for Intelligent Marketing Systems. Digital Marketing Review, 28(4), 45-67.
Kumar, R., Taylor, E., & Kent, L. (2019). Big Data Integration in Marketing Analytics. Marketing Intelligence & Planning, 37(6), 746-761.
Liu, Y., Smith, K., & Taylor, E. (2021). Real-Time Analytics in Personalized Marketing. Marketing Science Quarterly, 40(3), 99-115.
Mehrabi, N., Brown, T., & Gupta, S. (2021). Ethical AI in Intelligent Marketing Systems: Challenges and Opportunities. Journal of Ethics in AI, 14(2), 201-220.
Movahed, A. B., Movahed, A. B., & Nozari, H. (2024). Marketing 6.0 Conceptualization. In Advanced Businesses in Industry 6.0 (pp. 15-31). IGI Global.
Najafi, S. E., Nozari, H., & Edalatpanah, S. A. (2022). Artificial Intelligence of Things (AIoT) and Industry 4.0–Based Supply Chain (FMCG Industry). A Roadmap for Enabling Industry 4.0 by Artificial Intelligence, 31-41.
Nozari, H., & Aliahmadi, A. (2022). Lean supply chain based on IoT and blockchain: Quantitative analysis of critical success factors (CSF). Journal of Industrial and Systems Engineering, 14(3), 149-167.
Smith, K., Zhang, L., & Li, P. (2020). Personalization in Digital Marketing: Strategies and Implications. Journal of Marketing Research, 57(5), 405-423.
Tavakkoli-Moghaddam, R., Nozari, H., Bakhshi-Movahed, A., & Bakhshi-Movahed, A. (2024). A Conceptual Framework for the Smart Factory 6.0. In Advanced Businesses in Industry 6.0 (pp. 1-14). IGI Global.
Taylor, E., & Kent, L. (2020). Data Privacy and Governance in Intelligent Marketing Systems. Data Ethics Quarterly, 29(1), 19-35.
Zhang, L., & Li, P. (2020). Integrating Intelligent Marketing Systems with Digital Tools. Technology and Marketing Review, 33(7), 77-94.