2022· American International Journal of Computer Science and Technology· 0 citations
TL;DR
A modular, cloud-native CRM architecture that is based on microservices, event-driven design, and distributed data management so that the system can be elastic, resilient, and interoperable without any glitches is proposed.
Abstract
As global enterprises find themselves in more and more complex and data-driven business environments, designing scalable Customer Relationship Management (CRM) architectural models has become a top priority. CRM systems have come a long way, changing from pretty basic contact management tools to smart, integrated platforms enabling companies to handle customers' interactions not only through multiple channels but also across the world and different touchpoints too. Such change has been largely driven by cloud computing, AI, and big data, which have greatly increased the contribution of CRM in customer engagement, efficiency of operations, and helping with decision-making at a strategic level. In these digital times, the ability to scale is more than a technical issue; it is a business one as well, and one of the reasons for that is that companies are forced to deal with rapidly increasing amounts of customer data, varying workloads, and the need for real-time personalization, all the while keeping performance and reliability at a high level. On the other hand, the struggle to design such architecture that is scalable to a large extent arises from the fact that data is often fragmented across different systems, there are a lot of challenges with integration, latency is a concern, security and compliance are on the agenda, and there is also a need to find the right balance between flexibility on the one hand and standardization on the other. The present paper tackles these issues and proposes a modular, cloud-native CRM architecture that is based on microservices, event-driven design, and distributed data management so that the system can be elastic, resilient, and interoperable without any glitches. The approach concentrates on detaching one system component from another, using APIs as the main means of integration, and intelligent automation can be considered as a tool for dynamic scaling and continuous innovation.
Initially, Customer Relationship Management (CRM) systems were mainly focused on managing contacts and closely related information. Nowadays, these systems are not only the main tools in the sales and service areas of enterprises but also drive operational efficiency. CRM systems have become powerful platforms supporting the entire operation of a business. As businesses become larger and more diverse, the standard CRM packages, which are ready to use, often cannot effectively deal with unique processes, specific requirements of an industry, and customers who keep changing their needs. This situation is the reason why companies nowadays put more focus on deep CRM customization as a factor that can change the game while at the beginning it was only considered as a technical adjustment. The main point of this article is that enterprises have the possibility to turn their operations if they make their CRM systems more in line with their ways of working, the organization of their data and their methods of communicating with customers. It points out the main problems that, as a rule, organizations experience such as the integration of the CRM system with other old systems preserving data integrity, getting user adoption, and performing a balance between customization and system scalability and maintainability. In order to overcome these problems, the article advocates a well-laid-out plan which is a combination of user-centric design, modular customization practices and continuous feedback loops. At the same time, strong governance and change management strategies are the pillars of this. In terms of results, organizations have become more efficient in their operations. They provide a better customer experience. Decision-making based on data has improved; in addition, CRM investment returns have increased.
Satyendra Kumar Vanapalli· International Journal of Com...· 0 citations
Recently, digital change has been one of the top concerns of most enterprises as they struggle with rapidly changing markets and customer requirements going up further, as well as technology getting better and better. The major part of this change is the Customer Relationship Management (CRM), which, nowadays, is regarded as the real source of generating the business value rather than just serving as a secondary source of business data. This article discusses the ways in which CRM can be effectively used as a support tool of business strategy, a link between the business goals and the capabilities of the technology, which could allow digital transformation to finally happen. Besides, it points out the fact that it is necessary to integrate different platforms, the relevance of using real-time as well as past data for well-informed decision-making, and the coming of a personalized and seamless customer experience. The research comprises examining the CRM systems through their theoretical and practical aspects. For this, the study has combined the literature review, the conceptual analysis, and the case study as methods. The outcome of this work points to a trend where the organizations that are implementing the state-of-the-art and open-ended CRM platform architectures that mainly incorporate the concepts of cloud computing, business analytics, and artificial intelligence can better adapt to the changes of the market, gain a higher level of efficiency in operations, and develop strong and lasting relationships with their clients. On top of that, the article presents some of the most recognizable challenges, which include data silos, integration complexity, and resistance of the organization, and at the same time, it suggests the way to solve these issues as well.
Satyendra Kumar Vanapalli· International Journal of Art...· 0 citations
Results indicate that organizations adopting microservices design on Kubernetes can achieve higher deployment frequency, better resiliency, improved resource utilization and greater agility to respond to evolving market needs.
Srichandra Boosa· American International Journ...· 0 citations
Centralized data operations are often using in Small and medium-sized enterprises (SMEs) for easy data management, but there suffering from few limitations like cloud platform integration with bigdata yet many still face difficulties integrating cloud platforms with big-data capabilities in a scalable and governed manner. To address the problems, this communication presents an Adaptive Cloud -Big-Data Enablement Framework (ACBDEF), which is a realistic mechanism of SME digital data transformation. This framework consists of five main steps, which are technological infrastructure, data governance and compliance, organizational capability development, environmental alignment, and intelligence/value extraction into a common architecture. One of the key elements in this work is the Adaptive Migration Engine (AME) which is used to assess dynamically workloads, the parameters such as data characteristics, regulatory constraints, the cost-performance metrics are used to decide on the optimal deployment in cloud premises, or in hybrid environments. The adaptive decision process is beneficial in assisting SMEs in mitigating technical and organizational issues in enhancing the efficiency, security, and analytical responsiveness. The proposed mechanism brings into line theoretical adoption factors with actionable implementation which provides a structured model for supporting SMEs to achieve sustainable and data-driven cloud transformation.
B. Madhu Uthej, Dudde Lohith, Atluru Sai Charan Reddy et al.· 2026 7th International Confe...· 0 citations
It has been found that CRM in combination with BI could lead to deeper customer understanding, forecasting capabilities, and making marketing strategies customer-specific and has practical value for enterprises in that they should consider not only sophisticated CRM equipment but also data management and human resources to be able to fully benefit from BI interfacing.
Satyendra Kumar Vanapalli· International Journal of App...· 0 citations
Enterprise systems still struggle to move data reliably across cloud and legacy platforms while keeping costs, latency, and risk in check. The author presents an artificial intelligence-enhanced middleware pattern that augments existing integration stacks with telemetry, stream processing, and a lightweight learning loop to predict failures, automatically tune policies, and direct traffic in real time. The architecture couples an integration core comprising application programming interfaces (APIs), messaging, and event flows with a model-driven policy layer and feedback control. The approach is validated through implementations involving retail order orchestration, logistics tracking, and financial services, demonstrating reductions in mean time to resolution of 35% to 55%, message loss of 0.01%, and cloud egress costs of 8% to 12% under production-like loads. The author outlines governance and observability practices that make the pattern portable across TIBCO Software Inc. integration platforms, Apache Kafka, MuleSoft, and cloud-native services without vendor lock-in. The result provides a pragmatic route to resilient, compliant, and scalable integration that organizations can adopt incrementally at enterprise scale without rewriting critical systems.
Tejas Gajjar· International Journal of Inf...· 0 citations
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