Unified Customer Identity and Profile Architecture for Customer Enterprise Orchestration
DOI:
https://doi.org/10.15662/IJEETR.2024.0606027Keywords:
customer identity resolution, unified customer profile, enterprise data orchestration, real-time event streaming, Apache Kafka, identity graph management, customer data platform, GDPR compliance, microservices architecture, AI-assisted decisioning, omnichannel personalisation, data meshAbstract
Digital interaction ecosystems have created many architectural challenges for enterprises in terms of managing, synchronising and activating customer intelligence across multiple distributed systems and interaction channels. Traditional infrastructures for customer engagement tend to suffer from disjointed identity stores, disjointed behavioural data, and batch processing models that hinder scalability, slow down personalization responsiveness, and prevent coordinated delivery of the customer experience. In the age of real-time engagement strategies, there's a growing demand for scalable architectural patterns that allow enterprises to integrate identity resolution, profile computation, and orchestration across channels in a continuously adaptive digital ecosystem
This paper proposes a common customer identity and customer profile architecture to meet the requirements of real-time enterprise orchestration by distributed identity resolution, streaming event processing, scalable customer profile computation and low latency activation frameworks. The proposed architecture is capable of a 94% identity match accuracy, achieving profile freshness latency of less than 2 minutes for batch window of 480 minutes as compared to the conventional batch-oriented models, and cross-channel consistency score – 54% to 91%. Implementation results, across telecommunications, retail, financial services and media, showed reductions in activation latency of between 60% and 74% and improvements in personalisation accuracy of between 76% and 92%. The framework delivers an extensible and privacy-enabled approach for enterprise-wide customer intelligence orchestration which can be used for support AI-assisted decisioning, predictive audience modelling and adaptive journey orchestration
References
1. Al-Nasser, A., & Koutb, M. (2023). At the confluence of artificial intelligence and edge computing in IoT-based applications: A review and new perspectives. Sensors, 23(3), 1639. https://doi.org/10.3390/s23031639
2. Araújo Machado, I., Costa, C., & Santos, M. Y. (2022). Advancing data architectures with data mesh implementations. In J. De Weerdt & A. Polyvyanyy (Eds.), Intelligent information systems: CAiSE Forum 2022 (Lecture Notes in Business Information Processing, Vol. 452, pp. 10–18). Springer. https://doi.org/10.1007/978-3-031-07481-3_2
3. Cambronero, M. E., Martínez, M. A., Llana, L., Rodríguez, R. J., & Russo, A. (2024). Towards a GDPR-compliant cloud architecture with data privacy controlled through sticky policies. PeerJ Computer Science, 10, e1898. https://doi.org/10.7717/peerj-cs.1898
4. Cao, L., & Zhu, C. (2022). Personalized next-best action recommendation with multi-party interaction learning for automated decision-making. PLOS ONE, 17(1), e0263010. https://doi.org/10.1371/journal.pone.0263010
5. Chouaten, K., Rodriguez Rivero, C., Nack, F., & Reckers, M. (2024). Unlocking high-value football fans: Unsupervised machine learning for customer segmentation and lifetime value. Frontiers in Sports and Active Living, 6, 1362489. https://doi.org/10.3389/fspor.2024.1362489
6. Glöckler, J., Sedlmeir, J., Frank, M., & Fridgen, G. (2024). A systematic review of identity and access management requirements in enterprises and potential contributions of self-sovereign identity. Business & Information Systems Engineering, 66(4), 421–440. https://doi.org/10.1007/s12599-023-00830-x
7. Gujjala, P. K. R. (2023). The future of cloud-native lakehouses: Leveraging serverless and multi-cloud strategies for data flexibility. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 9(9), 868–882. https://doi.org/10.32628/CSEIT239093
8. Hu, L., Liu, Z., Zhao, Z., Hou, L., Nie, L., & Li, J. (2023). A survey of knowledge enhanced pre-trained language models. IEEE Transactions on Knowledge and Data Engineering, 35(12), 12412–12430. https://doi.org/10.1109/TKDE.2023.3310001
9. Ireddy, R. K. (2024). Deep learning architecture for banking risk management: Cloud and AI-driven predictive analytics solution. International Journal of Scientific Research in Computer Science, Engineering and Information Technology, 10(5), 1194–1206. https://doi.org/10.32628/CSEIT24113395
10. Merlec, M. M., Lee, Y. K., Hong, S.-P., & In, H. P. (2021). A smart contract-based dynamic consent management system for personal data usage under GDPR. Sensors, 21(23), 7994. https://doi.org/10.3390/s21237994
11. Pons, G., Bilalli, B., & Queralt, A. (2024). Knowledge graphs for enhancing large language models in entity disambiguation. In The Semantic Web – ISWC 2024 (Lecture Notes in Computer Science, pp. 162–179). Springer. https://doi.org/10.1007/978-3-031-77844-5_9
12. Raptis, T. P., & Passarella, A. (2023). A survey on networked data streaming with Apache Kafka. IEEE Access, 11, 85333–85350. https://doi.org/10.1109/ACCESS.2023.3303810
13. Raptis, T. P., Cicconetti, C., & Passarella, A. (2024). Efficient topic partitioning of Apache Kafka for high-reliability real-time data streaming applications. Future Generation Computer Systems, 154, 173–188. https://doi.org/10.1016/j.future.2023.12.028
14. Santos, S., & Gonçalves, H. M. (2022). Consumer decision journey: Mapping with real-time longitudinal online and offline touchpoint data. European Management Journal, 40(5), 683–693. https://doi.org/10.1016/j.emj.2021.09.002
15. Thaichon, P., Quach, S., Barari, M., & Nguyen, M. (2023). Exploring the role of omnichannel retailing technologies: Future research directions. Australasian Marketing Journal, 31(2), 162–177. https://doi.org/10.1177/14413582231167664
16. Theodorakopoulos, L., & Theodoropoulou, A. (2024). Leveraging big data analytics for understanding consumer behavior in digital marketing: A systematic review. Human Behavior and Emerging Technologies, 2024, Article 3641502. https://doi.org/10.1155/2024/3641502
17. Velepucha, V., & Flores, P. (2023). A survey on microservices architecture: Principles, patterns and migration challenges. IEEE Access, 11, 88339–88358. https://doi.org/10.1109/ACCESS.2023.3305687
18. Vyas, S., Bhargava, M., & Bhargava, D. (2022). Performance evaluation of Apache Kafka – A modern platform for real-time data streaming. In 2022 2nd International Conference on Innovative Practices in Technology and Management (ICIPTM) (pp. 1–6). IEEE. https://doi.org/10.1109/ICIPTM54933.2022.9754154
19. Zhou, P., Ying, K., Wang, Z., Guo, D., & Bai, C. (2024). Self-supervised enhancement for named entity disambiguation via multimodal graph convolution. IEEE Transactions on Neural Networks and Learning Systems, 35(1), 231–245. https://doi.org/10.1109/TNNLS.2022.3173179
