Optimizing Enterprise CRM Systems for High Volume Customer Data Using Distributed Computing and Cloud Native Technologies
Keywords:
Customer Relationship Management, Distributed Computing, Cloud Native Computing, Big Data, Microservices, Customer Analytics, Distributed Database, Cloud CRM, Data Processing, Enterprise SystemAbstract
Enterprise Customer Relationship Management systems have evolved from centralized customer databases into complex digital platforms that continuously collect and process large volumes of transactional, behavioral, social, service, and interaction data. Traditional monolithic CRM architectures often experience scalability limitations, increasing response times, storage bottlenecks, and difficulties in supporting real-time analytics when customer data volumes increase substantially. This paper examines an architectural approach for optimizing enterprise CRM systems through distributed computing and cloud-native technologies. The proposed approach combines distributed data processing, microservices, containerized deployment, scalable storage, event-driven integration, distributed caching, and cloud-based resource management to improve CRM scalability and operational efficiency. Previous research demonstrates that integrating big-data capabilities with CRM can strengthen personalization, customer intelligence, service customization, and data-driven decision-making. Cloud-native architectures further enable organizations to separate CRM functions into independently scalable services while distributed computing frameworks provide parallel processing capabilities for large customer datasets. The paper presents a conceptual architecture for high-volume CRM processing and discusses data ingestion, distributed storage, microservices-based processing, real-time customer analytics, scalability, security, and reliability. The analysis indicates that combining distributed computing with cloud-native CRM architecture can improve system responsiveness, elasticity, availability, data processing capacity, and organizational ability to generate timely customer insights.
References
Anshari, M., Almunawar, M. N., Lim, S. A., & Al-Mudimigh, A. (2019). Customer relationship management and big data enabled personalization and customization of services. Applied Computing and Informatics, 15(2), 94–101.
Ardito, L., Cerchione, R., Del Vecchio, P., & Raguseo, E. (2022). Big data in customer relationship management strategies A structured literature review and future research agenda. International Marketing Review, 39(5), 1069–1092.
Böhm, M., et al. (2021). Building and operating a large-scale enterprise data analytics platform. Big Data Research, 23, 100181.
Sai Krishna Puli. (2021). Integrating Microsoft Dynamics 365 CRM with Apache Kafka: A Scalable Event-Driven Architecture and Data Synchronization Mechanism. International Journal of Computer Engineering and Technology (IJCET), 12(3), 139-158 doi: https://doi.org/10.34218/IJCET_12_03_015
Capuano, N., Greco, L., Ritrovato, P., & Vento, M. (2021). Sentiment analysis for customer relationship management An incremental learning approach. Applied Intelligence, 51, 3339–3352.
Fernández-Cejas, M., Pérez-González, C. J., Roda-García, J. L., & Colebrook, M. (2022). CURIE Towards an ontology and enterprise architecture of a CRM conceptual model. Business & Information Systems Engineering, 64, 615–643.
Guerola-Navarro, V., Gil-Gomez, H., Oltra-Badenes, R., & Sendra-García, J. (2021). Customer relationship management and its impact on innovation A literature review. Journal of Business Research, 129, 83–87.
Payne, A., & Frow, P. (2005). A strategic framework for customer relationship management. Journal of Marketing, 69(4), 167–176.
Sai Krishna Puli. (2026). Modernizing CRM Data at Scale. International Journal of Customer Relationship Management (IJCRM), 5(2), 1-15 doi: https://doi.org/10.34218/IJCRM_05_02_001
Suoniemi, S., Terho, H., Zablah, A., Olkkonen, R., & Straub, D. W. (2021). The impact of firm-level and project-level IT capabilities on CRM system quality and organizational productivity. Journal of Business Research, 127, 108–122.
Zhang, H., Liang, X., & Wang, S. (2020). Linking big data analytical intelligence to customer relationship management performance. Industrial Marketing Management, 91, 483–494.
Chen, I. J., & Popovich, K. (2003). Understanding customer relationship management People process and technology. Business Process Management Journal, 9(5), 672–688.
Ngai, E. W. T., Xiu, L., & Chau, D. C. K. (2009). Application of data mining techniques in customer relationship management A literature review and classification. Expert Systems with Applications, 36(2), 2592–2602.
Winer, R. S. (2001). A framework for customer relationship management. California Management Review, 43(4), 89–105.
Buttle, F., & Maklan, S. (2019). Customer Relationship Management Concepts and Technologies. Routledge.
Marston, S., Li, Z., Bandyopadhyay, S., Zhang, J., & Ghalsasi, A. (2011). Cloud computing The business perspective. Decision Support Systems, 51(1), 176–189.
Hashem, I. A. T., Yaqoob, I., Anuar, N. B., Mokhtar, S., Gani, A., & Khan, S. U. (2015). The rise of big data on cloud computing Review and open research issues. Information Systems, 47, 98–115.
