Smart data-driven marketing in Industry 5.0
Keywords:
Data driven marketing, Smart marketing, Customer data analysis, Marketing 5.0Abstract
Customer data, which is usually collected with the help of various tools, has a significant impact on knowing more customers, improving relationships and providing better services. Data-driven marketing is an approach to optimize brand communication based on customer information. Data-driven marketers use customer data to predict their needs, wants, and behavior. Such information helps develop personalized marketing strategies. Data-based marketing allows the most suitable offer to be presented to the customer at the most suitable time and to establish an effective relationship with them. In the era of transformative technologies and considering the basic challenges of receiving, maintaining and using a large volume of customer data, understanding The basic features of these marketing systems have multiple importance. Therefore, in this chapter, in addition to explaining the difference between data-driven marketing and traditional marketing, an effort is made to examine the basic elements and components of this type of effective retrieval.
Downloads
References
Aliahmadi, A., & Nozari, H. (2023, January). Evaluation of security metrics in AIoT and blockchain-based supply chain by Neutrosophic decision-making method. In Supply Chain Forum: An International Journal (Vol. 24, No. 1, pp. 31-42). Taylor & Francis.
Fallah, M., & Nozari, H. (2021). Neutrosophic mathematical programming for optimization of multi-objective sustainable biomass supply chain network design. Computer Modeling in Engineering & Sciences, 129(2), 927-951.
Mohammadi, H., Ghazanfari, M., Nozari, H., & Shafiezad, O. (2015). Combining the theory of constraints with system dynamics: A general model (case study of the subsidized milk industry). International journal of management science and engineering management, 10(2), 102-108.
Nahr, J. G., Nozari, H., & Sadeghi, M. E. (2021). Green supply chain based on artificial intelligence of things (AIoT). International Journal of Innovation in Management, Economics and Social Sciences, 1(2), 56-63.
Nozari, H. (2024). Investigating Key Dimensions and Key Indicators of AIoT-Based Supply Chain in Sustainable Business Development. In Artificial Intelligence of Things for Achieving Sustainable Development Goals (pp. 293-310). Cham: Springer Nature Switzerland.
Nozari, H. (2024). Supply Chain 6.0 and Moving Towards Hyper-Intelligent Processes. In Information Logistics for Organizational Empowerment and Effective Supply Chain Management (pp. 1-13). IGI Global.
Nozari, H., & Ghahremani-Nahr, J. (2022). Assessing Key performance indicators in Blockchain-Based Supply Chain Financing: Case Study of Chain Stores. International Journal of Innovation in Engineering, 2(3), 42-58.
Nozari, H., Fallah, M., & Szmelter-Jarosz, A. (2021). A conceptual framework of green smart IoT-based supply chain management. International journal of research in industrial engineering, 10(1), 22-34.
Nozari, H., Ghahremani-Nahr, J., & Szmelter-Jarosz, A. (2024). AI and machine learning for real-world problems. In Advances In Computers (Vol. 134, pp. 1-12). Elsevier.
Nozari, H., Rahmaty, M., & Szmelter-Jarosz, A. (2024). A framework for AIoT-based smart sustainable marketing system. In Artificial Intelligence of Things for Achieving Sustainable Development Goals (pp. 255-271). Cham: Springer Nature Switzerland.
Nozari, H., Szmelter-Jarosz, A., & Rahmaty, M. (2024). Smart Marketing Based on Artificial Intelligence of Things (AIoT) and Blockchain and Evaluating Critical Success Factors. In Smart and Sustainable Interactive Marketing (pp. 68-82). IGI Global.
Obaid, H. S., & Nozari, H. (2022). Examining Dimensions and Components and Application of Supply Chain Financing (In Chain Stores). International Journal of Innovation in Management, Economics and Social Sciences, 2(4), 81-88.
Tavakkoli-Moghaddam, R., Ghahremani-Nahr, J., Samadi Parviznejad, P., Nozari, H., & Najafi, E. (2022). Application of internet of things in the food supply chain: a literature review. Journal of applied research on industrial engineering, 9(4), 475-492.
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Aminmasoud Bakhshi Movahed, Ali Bakhshi Movahed
This work is licensed under a Creative Commons Attribution 4.0 International License.
