Vol. 4 No. 1 (2024): Journal of AI-Assisted Scientific Discovery
Articles

Unlocking the Potential of Customer 360 with Big Data and AI: A Strategic Framework for Customer Intelligence and Predictive Analytics in Industry 4.0

Fatima Ali
Professor of Healthcare Data Analytics, Sahara Institute of Science and Technology, Cairo, Egypt
Cover

Published 22-01-2024

Keywords

  • Customer 360,
  • Artificial Intelligence,
  • Customer Intelligence,
  • Predictive Analytics,
  • Personalized Marketing,
  • Data Integration
  • ...More
    Less

How to Cite

[1]
F. Ali, “Unlocking the Potential of Customer 360 with Big Data and AI: A Strategic Framework for Customer Intelligence and Predictive Analytics in Industry 4.0”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 1, pp. 18–35, Jan. 2024, Accessed: Nov. 23, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/8

Abstract

This paper proposes a strategic framework for leveraging the convergence of big data and artificial intelligence (AI) to construct robust Customer 360 solutions, tailored for the demands of Industry 4.0. The framework emphasizes the synthesis of customer intelligence, predictive analytics, and personalized marketing strategies to foster heightened customer engagement and catalyze business expansion within the contemporary industrial landscape. Through the amalgamation of advanced data analytics techniques and AI-driven algorithms, organizations can unlock the latent potential within their customer data repositories, thereby gaining profound insights into consumer behavior, preferences, and trends. By harnessing the power of AI, specifically machine learning and deep learning algorithms, businesses can transcend traditional data processing limitations, enabling the extraction of actionable insights in real-time. The strategic framework outlined herein delineates a systematic approach to deploying Customer 360 solutions, encompassing data acquisition, integration, analysis, and application stages, within the context of Industry 4.0. Furthermore, it elucidates the pivotal role of personalized marketing strategies in fostering enduring customer relationships and driving sustainable business growth. This paper underscores the transformative potential of Customer 360 solutions, underpinned by big data and AI technologies, in reshaping the contemporary business landscape, and offers actionable insights for organizations seeking to capitalize on these emerging trends.

Downloads

Download data is not yet available.

References

  1. Chen, Ming-Hui, et al. "Customer 360 Analytics: From Acquisition to Retention." Journal of Interactive Marketing, vol. 45, 2019, pp. 22-37.
  2. Davenport, Thomas H. Big Data at Work: Dispelling the Myths, Uncovering the Opportunities. Harvard Business Review Press, 2014.
  3. Reddy, Byrapu, and Surendranadha Reddy. "Evaluating The Data Analytics For Finance And Insurance Sectors For Industry 4.0." Tuijin Jishu/Journal of Propulsion Technology 44.4 (2023): 3871-3877.
  4. Gupta, Sanjeev, et al. "Leveraging Big Data and AI for Customer 360 Insights: Opportunities and Challenges." Journal of Big Data, vol. 7, no. 1, 2020, pp. 1-22.
  5. Hwang, Haewoon, and Kwon, Kyoung-Nan. "The Impact of Big Data and AI on Marketing: Theoretical and Practical Implications." Journal of Marketing Theory and Practice, vol. 27, no. 2, 2019, pp. 111-126.
  6. IBM. Understanding Customer 360 and Turning Data into Actionable Insights. IBM Corporation, 2020.
  7. Kim, Jong Woo, et al. "Deep Learning in Customer Analytics: Using AI for Customer 360 Insights." Journal of Business Research, vol. 128, 2021, pp. 193-204.
  8. Kotler, Philip, et al. Marketing 4.0: Moving from Traditional to Digital. Wiley, 2016.
  9. LaValle, Steve, et al. Big Data, Analytics and the Path From Insights to Value. MIT Sloan Management Review Research Report, 2011.
  10. Li, Xiuping, et al. "Customer Analytics in the Age of Big Data: A Literature Review and Research Agenda." Information & Management, vol. 54, no. 6, 2017, pp. 757-769.
  11. Marr, Bernard. Big Data in Practice: How 45 Successful Companies Used Big Data Analytics to Deliver Extraordinary Results. John Wiley & Sons, 2016.
  12. McAfee, Andrew, and Brynjolfsson, Erik. "Big Data: The Management Revolution." Harvard Business Review, vol. 90, no. 10, 2012, pp. 60-68.
  13. Microsoft. Customer 360 with Microsoft Dynamics 365. Microsoft Corporation, 2020.
  14. Nguyen, Thi L., et al. "Customer 360 and Big Data Analytics for Marketing Decision Support: A Review and a Research Agenda." Journal of Business Research, vol. 109, 2020, pp. 381-389.
  15. Pfeiffer, J. William. Customer-Centric Marketing: Supporting Sustainability in the Digital Age. Business Expert Press, 2018.
  16. Rathore, Siddharth, et al. "Big Data Analytics for Customer 360: A Review and Future Directions." Computers in Human Behavior, vol. 101, 2019, pp. 49-67.
  17. Reis, Renato M., et al. "Predictive Analytics and Customer 360 in the Banking Industry: A Review and Research Agenda." Journal of Retailing and Consumer Services, vol. 51, 2019, pp. 224-236.
  18. Alghayadh, Faisal Yousef, et al. "Ubiquitous learning models for 5G communication network utility maximization through utility-based service function chain deployment." Computers in Human Behavior (2024): 108227.
  19. Dutta, Ashit Kumar, et al. "Deep learning-based multi-head self-attention model for human epilepsy identification from EEG signal for biomedical traits." Multimedia Tools and Applications (2024): 1-23.
  20. Raparthi, Mohan, Sarath Babu Dodda, and Srihari Maruthi. "AI-Enhanced Imaging Analytics for Precision Diagnostics in Cardiovascular Health." European Economic Letters (EEL) 11.1 (2021).
  21. Kulkarni, Chaitanya, et al. "Hybrid disease prediction approach leveraging digital twin and metaverse technologies for health consumer." BMC Medical Informatics and Decision Making 24.1 (2024): 92.
  22. Raparthi, Mohan. "Biomedical Text Mining for Drug Discovery Using Natural Language Processing and Deep Learning." Dandao Xuebao/Journal of Ballistics 35
  23. Kumar, Mungara Kiran, et al. "Approach Advancing Stock Market Forecasting with Joint RMSE Loss LSTM-CNN Model." Fluctuation and Noise Letters (2023).
  24. Sati, Madan Mohan, et al. "Two-Area Power System with Automatic Generation Control Utilizing PID Control, FOPID, Particle Swarm Optimization, and Genetic Algorithms." 2024 Fourth International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies (ICAECT). IEEE, 2024.
  25. Raparthy, Mohan, and Babu Dodda. "Predictive Maintenance in IoT Devices Using Time Series Analysis and Deep Learning." Dandao Xuebao/Journal of Ballistics 35: 01-10.
  26. SAS Institute Inc. Customer 360: Realizing Customer 360 with AI and Analytics. SAS Institute Inc., 2021.
  27. Sharma, Alok, et al. "Transforming Customer Experience: From Customer 360 to Cognitive Engagement." International Journal of Information Management, vol. 47, 2019, pp. 131-141.
  28. Wang, Xing, et al. "Customer 360 Analytics: Data-Driven Insights for Enhanced Customer Engagement." Information Systems Frontiers, vol. 23, no. 1, 2021, pp. 1-21.
  29. Zaki, Mohammed J., et al. "Customer 360 Insights: Using Big Data and AI for a Comprehensive Customer View." Expert Systems with Applications, vol. 147, 2020, pp. 1-14.