. REAL-TIME CUSTOMER PROFILING WITH GENERATIVE AI
Keywords:
Customer Profiling, Generative AI, Machine Learning, Real-time Analytics, System ArchitectureAbstract
Real-time customer profiling with Generative AI represents a transformative approach in how businesses understand and serve their customers in today's digital landscape. This technical article explores the comprehensive architecture for implementing customer profiling systems that combine traditional data processing with advanced AI capabilities. The system incorporates multiple components including data source integration, real-time data ingestion, advanced processing pipelines, dynamic profile creation, advanced modeling and scoring, and real-time decision engines. Through the integration of structured and unstructured data sources, coupled with sophisticated AI models, organizations can achieve significant improvements in customer understanding, operational efficiency, and service delivery. The implementation considerations address crucial aspects of scalability, security, and performance optimization, providing a blueprint for organizations seeking to enhance their customer engagement capabilities.
References
Ernesto Diaz-Aviles, et al., "Towards real-time customer experience prediction for telecommunication operators," IEEE International Conference on Big Data (Big Data) 2015. Available: https://ieeexplore.ieee.org/abstract/document/7363860
Fangyuan, et al., "AI-driven customer relationship management for sustainable enterprise performance," Sustainable Energy Technologies and Assessments, Volume 52, Part B, August 2022, 102103. Available: https://www.sciencedirect.com/science/article/abs/pii/S2213138822001552
Ning Niu, et al., "Integrating multi-source big data to infer building functions," International Journal of Geographical Information Science , Volume 31, 2017 - Issue 9. Available: https://www.tandfonline.com/doi/abs/10.1080/13658816.2017.1325489
Benymol Jose, et al., "Unstructured Data Mining for Customer Relationship Management: A Survey," International Journal of Management, Technology And Engineering, vol. 2, no. 12, pp. 31-37, 2019. Available: https://www.ijamtes.org/gallery/128-dec.pdf
Matei Zaharia, et al., "Apache Spark: a unified engine for big data processing," Communications of the ACM, vol. 59, no. 11, pp. 56-65, 2016. Available: https://dl.acm.org/doi/10.1145/2934664
Jeffrey Dean, et al., "MapReduce: Simplified Data Processing on Large Clusters," OSDI'04: Sixth Symposium on Operating System Design and Implementation, San Francisco, CA, pp. 137-150, 2004. Available: https://static.googleusercontent.com/media/research.google.com/en//archive/mapreduce-osdi04.pdf
Surajit Chaudhuri, et al., "An overview of business intelligence technology," Communications of the ACM, Volume 54, Issue 8. Available: https://dl.acm.org/doi/10.1145/1978542.1978562
Yihao Li , et al., "A review of deep learning-based information fusion techniques for multimodal medical image classification," Computers in Biology and Medicine, Volume 177, July 2024, 108635. Available: https://www.sciencedirect.com/science/article/pii/S0010482524007200
Wilhelmina Addy, et al., "AI in credit scoring: A comprehensive review of models and predictive analytics," Global Journal of Engineering and Technology Advances, 2024. Available: https://www.researchgate.net/publication/378311289_AI_in_credit_scoring_A_comprehensive_review_of_models_and_predictive_analytics
Claudiu-Catalin Munteanu, et al., "A Methodological Approach for the Journey through Real-Time Marketing: From Customer Journey Analytics to Personalization Engines," “Ovidius” University Annals, Economic Sciences Series, Volume XXI, Issue 2 /2021. Available: https://ibn.idsi.md/sites/default/files/j_nr_file/Full-Vol.-XXI-Issue-2.pdf#page=864
Alejandro Baldominos, "A scalable machine learning online service for big data real-time analysis," IEEE Symposium on Computational Intelligence in Big Data (CIBD), 2014. Available: https://ieeexplore.ieee.org/abstract/document/7011537
Liu Yongjin, et al., "Architecture Design and Optimization of Large-scale Data Processing Systems in Cloud Computing Environments," Advances in Computer, Signals and Systems (2024), Clausius Scientific Press, Canada. Available: https://www.clausiuspress.com/assets/default/article/2024/06/02/article_1717377539.pdf
Published
Issue
Section
License
Copyright (c) 2025 Rajkumar Sukumar (Author)

This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.