Optimizing Customer Experience by Exploiting Real-Time Data Generated by IoT and Leveraging Distributed Web Systems in CRM Systems

Abstract

Integrating smart devices from the Internet of Things (IoT) with Customer Relationship Management (CRM) systems presents significant opportunities for enhancing customer experience through real-time data utilization. This article explores the technological frameworks and practical solutions for achieving seamless integration of IoT data within CRM platforms. By leveraging distributed Web systems, this study demonstrates how companies can improve scalability, responsiveness, and personalization in managing customer relationships. This paper outlines key architectural designs for distributed Web systems that ensure efficient real-time data processing while addressing challenges such as security, system integration, and the demands of analytics. This research provides insights into overcoming these challenges with strategies like load balancing, edge processing, and advanced encryption protocols. Results from simulations and practical implementations underscore the effectiveness of these approaches in optimizing operational efficiency and delivering hyper-personalized customer experiences. This study aims to bridge the gap between theoretical possibilities and real-world applications, offering actionable guidelines for organizations to fully leverage IoT-driven CRM systems.

1. Introduction

In today’s digital age, a business’s success is increasingly based on its ability to understand and anticipate customer needs in real time [1]. The IoT (Internet of Things) has, once and for all, transformed the interaction between businesses and consumers, providing access to a huge volume of data generated by interconnected devices [2]. This abundance of information opens up new possibilities to improve the customer experience through personalization of services and faster and more relevant responses. But in order to meet these new challenges and effectively capitalize on these real-time data, companies must adopt technologies that allow them to manage and analyze data in a scalable and secure way [3].

This study aims to analyze the impact of integrating IoT data into CRM systems using distributed Web architectures, highlighting their advantages in terms of scalability, security, and user experience personalization. Specifically, the paper explores how distributing processing and storage tasks across multiple systems can improve response speed and data protection in IoT-based CRMs.

Existing studies mainly focus on the use of IoT in CRM systems without addressing the challenges related to real-time processing and data security in depth. Also, most research does not analyze the advantages of using distributed Web systems in detail to improve the performance of these platforms. This paper addresses this gap by proposing an optimized architecture that combines IoT and distributed Web systems to improve user experience and the operational efficiency of companies.

Customer Relationship Management (CRM) refers to the strategies, technologies, and systems that companies use to manage interactions with current and potential customers. Traditional CRM platforms focus on data centralization and automation of customer-related processes. However, the integration of IoT technologies into CRM represents a paradigm shift, enabling real-time data collection and analysis, as well as personalized customer experiences.

CRM systems play an important role in centralizing and utilizing these data [4]. Integrating IoT-generated data with CRM systems, along with the use of distributed Web systems, can greatly improve the efficiency and ability of these platforms to provide personalized services. By distributing processing and storage tasks across multiple systems, companies can scale their infrastructure without compromising speed or security [5].

Security and reliability are key factors when handling large volumes of real-time data. Distributed Web systems play a crucial role in this regard, providing an architectural framework that ensures optimized management of processing and storage tasks. These systems allow companies to balance the load between different servers, avoid overload, and ensure service continuity, even during peak traffic times [6]. By implementing distributed solutions, one can achieve higher data processing speed and lower latency, in addition to improving security by reducing vulnerabilities at central data management points [7].

The main contribution of this paper is to propose a distributed architecture for integrating IoT data into CRM systems that improves the scalability, security, and personalization of customer interactions. This architecture is tested in an experimental environment to evaluate its performance and advantages compared to traditional solutions.

High flexibility is another significant advantage of integrating IoT data into CRM systems via distributed Web systems. Companies are able to adapt their IT infrastructure to meet new customer demands or to rapidly increase the number of connected devices [8]. Providing real-time responses, proactive actions, and advanced personalization optimizes the user experience. Therefore, customers enjoy more relevant and effective interactions, and companies strengthen their relationships with them, which means more loyalty and greater financial growth of the business [9].

The structure of this article is outlined as follows: Section 2 provides a review of the relevant literature on IoT integration in CRM and the use of distributed Web systems. Section 3 describes the proposed methodology, including the system architecture and data processing strategies. Section 4 presents the experimental results along with their analysis, discussing the implications for optimizing the user experience in IoT-based CRM systems. Finally, Section 5 provides the main conclusions and future research directions.

While existing studies discuss the integration of IoT into CRM, most focus on data-driven marketing strategies or customer segmentation rather than on the architectural and security challenges of real-time data processing. This paper addresses this gap by proposing a distributed architecture that enhances scalability, security, and efficiency in IoT-driven CRM systems.

In recent years, the integration of IoT data into Customer Relationship Management (CRM) systems has become a focal point for improving customer experiences. While prior studies, such as those by Sanodia [10], have explored the technical integration of IoT with CRM, they often overlook critical operational challenges, including real-time data processing and security. Sanodia’s research primarily focuses on data collection mechanisms but lacks in-depth analysis of performance under varying workloads and the implications for security in distributed environments. Our study addresses these gaps by proposing an advanced architecture that uses distributed Web systems to enhance real-time data handling, scalability, and data security.

Compared to conventional CRM architectures, which centralize data and, thus, face bottlenecks and security vulnerabilities, our distributed approach disperses workloads across multiple nodes. This not only improves system resilience but also facilitates faster response times and more secure data handling. Through simulations in a distributed environment, we demonstrate superior performance in managing high-volume IoT data with reduced latency and improved personalized recommendation accuracy. By tackling these practical challenges, our study offers actionable guidelines for businesses seeking to adopt IoT-driven CRM systems with enhanced operational efficiency and data protection.

Currently, many CRM systems are not equipped to effectively process IoT data in real time. This results in a lack of rapid response to user behaviors and needs. By integrating edge and IoT technologies into CRM, organizations can gain valuable insights to personalize customer interactions and improve support processes. In this article, we explore the integration of real-time data generated by IoT devices into CRM (customer relationship management) systems using distributed Web systems. This approach aims to improve the user experience and optimize customer relationship management processes.

2. Literature Review

2.1. Actual Works in the Field of Study

Research carried out by Sanodia analyzes how technologies from the IoT spectrum can be integrated into CRM systems to improve the understanding of customer behavior and preferences. The study details the methods by which IoT data are collected and processed, such as through the use of sensors and connected devices, to provide contextual and real-time information about customers. Through these technologies, organizations can gain much more detailed insight into user behavior, allowing them to deliver personalized services and efficiently optimize customer interactions based on the obtained data. It also addresses major challenges such as data security and technological complexity while highlighting the potential of these technologies to revolutionize CRM by automating certain processes, providing proactive recommendations, and anticipating customer needs. The author presents a broad analysis of current and future trends in the integration of IoT into CRM, demonstrating how the two can contribute to deeper personalization of customer experiences and better use of data to support business decisions [10].

Sanodia’s work [10] provides a foundational perspective on integrating IoT into CRM platforms by detailing the mechanisms for data collection and the potential for improving customer insights. However, while the study emphasizes the technological feasibility of IoT integration, it does not sufficiently address critical practical concerns:

• Feasibility in Large-Scale Deployments: Sanodia’s model assumes controlled environments with limited IoT endpoints. In contrast, our architecture demonstrates scalability through a distributed Web system that can dynamically adjust to increasing data flows without performance degradation.
• Security Considerations: Although Sanodia highlights the importance of data privacy, the study does not detail specific implementation strategies for securing real-time IoT data. Our solution incorporates TLS/SSL encryption and role-based access control, ensuring data integrity and compliance with regulatory frameworks.
• Performance Limitations: Sanodia’s study lacks empirical validation under heavy traffic conditions. We address this by providing performance benchmarks on a four-node distributed Web system, illustrating consistent efficiency across varying workloads.

By bridging these gaps, our study presents a comprehensive framework that enhances both the scalability and security of IoT-driven CRM systems.

A study by Eslami et al. analyzes the segmentation and behavior of IoT customers to optimize CRM strategies. The research uses the Self-Organizing MAP (SOM) algorithm to identify three distinct clusters of IoT users based on their usage patterns of connected devices. The second stage of the study analyzes 17 key factors that influence the purchase decisions and satisfaction of IoT customers using the decision tree classification and regression technique (CART). These analyses provide essential information for companies, enabling them to develop personalized marketing strategies and improve customer loyalty in the context of the IoT ecosystem [11].

Research by Jabeen et al. analyzes how data obtained through the IoT can improve decision-making processes in CRM. The study explores how IoT devices can collect highly valuable data on customer behavior, which are then integrated into CRM systems to provide a more complete view of customer preferences and needs. Detailed information allows companies to anticipate customer actions and customize experiences to better meet market demands. The research also highlights the difficulties companies face in integrating IoT into CRM, including a lack of expertise in software development and the management of large volumes of data. By integrating these two technologies, companies can develop innovative business models focused on consumer needs and improving their loyalty [12].

2.2. Analysis of Authors and Research Groups

An analysis of the authors and research centers that have studied the integration of IoT into CRM provides us with a perspective on the current research directions and helps us identify the lack of a focus on the use of distributed Web systems the optimization of IoT data processing. This analysis supports the formulation of the research problem and justifies the need to propose an optimized architecture, as detailed in the following sections.

Numerous authors have conducted various works on the topic of CRM systems using a dynamic analytical approach. An empirical investigation was carried out using the “Dimensions.ai” online platform as a database. The research category includes distributed computing and software systems. The VOSviewer tool was used to interpret the data and facilitate the identification of each author who wrote a paper on this topic. This analysis resulted in a graph. There are 3166 total writers in the dataset. In addition to having at least three citations, an author must be listed as an author in three of five publications to be considered relevant. In total, 68 authors met these requirements (see Figure 1) [13].

A map of the countries where the research centers in which the writers conducted their investigations are located was made using the same dataset. For a country to be considered relevant, it must have at least three papers with three citations each. After applying the criteria described above, research centers in 84 countries were found to meet the requirement. A graph was created with the centers that collaborate for the creation of scientific writings, so 54 of these countries are presented in Figure 2 [14].

3. Methodology

This research adopts a Design Science Research (DSR) methodology, combined with experimental analysis, to develop and evaluate an optimized architecture for the integration of IoT data into CRM using distributed Web systems. DSR is used to design and justify the proposed solution, and experimental analysis validates its performance through simulations and measurements of response times, resource utilization, and accuracy of personalized recommendations.

The methodology includes the following steps:

• Problem Identification—Analysis of the limitations of traditional CRM systems in processing IoT data and justification of the need to use distributed Web architectures.
• Solution Design—Proposal of a distributed architecture that improves the scalability, security, and personalization of CRM systems.
• Experimental Implementation—Simulation of the architecture using a distributed environment, with tests on 4 server nodes.
• Evaluation—Performance analysis by measuring response times, CPU/RAM usage, and accuracy of IoT-based recommendations.
• Interpretation of Results—Comparison of the results with those of traditional CRM systems to demonstrate the advantages of the proposed architecture.

3.1. Distributed Web Systems

Distributed Web systems are an essential factor for modern architectures that demand scalability, performance, and high security. These systems allow processing and storage tasks to be distributed across multiple servers, ensuring an optimal balance of resources and reducing the risks associated with the overloading of a single workstation [15].

These systems are designed to handle large volumes of data and variable traffic requirements. In this study, we implemented a distributed architecture that allows for horizontal scaling, that is, the addition of new nodes to handle an increased number of users or connected devices. This approach improves the overall performance of the platform, ensuring fast response times and efficient real-time data processing [16].

One of the main advantages of distributed Web systems is the ability to balance loads between multiple servers. In this study, we used load-balancing mechanisms to distribute traffic evenly among the available nodes. This was achieved by using a load balancer that constantly monitors the resources of each server and directs requests to the least loaded one. This mechanism prevents certain servers from becoming overloaded and helps ensure service continuity, even during periods of high traffic [17].

Security is one of the main issues when handling sensitive data or a large volume of real-time transactions. Distributed Web systems offer an additional advantage in that data and processes are shared between multiple locations, reducing the risk of vulnerabilities concentrated at a single centralized point. In our analysis, we considered TLS/SSL encryption protocols to secure all communications between distributed nodes. Each node was also equipped with role-based access control mechanisms, limiting access to resources based on the permissions of each user [18].

The proposed architecture was derived from the analysis of the limitations of traditional CRM systems, which are not optimized for the processing of large volumes of data generated by IoT devices in real time. The design principles were influenced by the requirements of scalability, security, and efficiency in data distribution processing.

Figure 3 shows the main components of a distributed Web system:

• Distributed Web Servers: Several servers (Server 1, Server 2, Server N-1, and Server N) are connected via a LAN, and each has a database (Db 1) associated with it. This suggests load balancing or redundancy.
• DNS (Domain Name System): A laptop sends an HTTPS request to the Internet for a domain (www.example.com). A DNS server resolves the domain name to an IP address (93.184.216.34).
• DNS Authority: The authoritative DNS server handles domain queries and returns the corresponding IP address to the laptop.

This system ensures scalability and reliability, with multiple servers responding to requests and domain resolution through the DNS (see

Table 1. Advantages of Using distributed Web systems.

Advantage	Description
High Scalability	Allows for the inclusion of additional resources (nodes or servers) to handle traffic increases without affecting performance.
Fault Tolerance	In the event of a node or server failure, the system automatically redistributes tasks to other functional nodes, maintaining service continuity.
Improved Response Times	Requests are split among several servers, which lowers latency and gives users faster answers.
Balanced Load	Spreading traffic among all available resources maintains optimal performance and prevents certain servers from being overloaded.
Enhanced Security	Data and processes are shared across multiple nodes, reducing vulnerabilities and risks of a single point of failure.
Effective Management of Resources	Optimized use of available resources, ensuring that each node or server is used efficiently without wasting resources.
Flexibility in Adaptation	Systems can be quickly configured and adjusted to meet dynamic requirements, such as increasing numbers of users or IoT devices.

).

Edge processing is a crucial component in reducing latency and managing data closer to the source. While the current architecture incorporates edge-level preprocessing for basic data filtering, a deeper integration of edge intelligence could enhance decision making at the device level. This would alleviate the load on central nodes and improve real-time responsiveness. Future work will focus on integrating edge computing modules capable of executing advanced analytics and local decision making to further optimize performance.

By extending edge capabilities, the architecture can reduce the need to transmit large volumes of data across the network, minimizing latency and enhancing data privacy through localized processing. Implementing this approach would enable real-time IoT data analysis while maintaining the overall efficiency of the distributed CRM system.

Additionally, privacy concerns arise when integrating IoT data into CRM systems. To mitigate risks, we ensure that sensitive customer data are encrypted during transmission and stored securely using access control mechanisms. By leveraging edge computing, some data processing occurs closer to the source, reducing unnecessary exposure of personal information in centralized cloud storage.

3.2. IoT

The Internet of Things (IoT) refers to a system of interconnected physical devices, vehicles, household appliances, industrial machinery, and other objects equipped with sensors, software, and network capabilities, allowing them to gather, process, and share data over the Internet. This interconnected ecosystem allows these devices to communicate not only with each other but also with centralized or distributed Web systems, creating opportunities for enhanced automation, real-time monitoring, and data-driven decision making. The IoT has the potential to revolutionize numerous industries by optimizing resource allocation, reducing operational costs, and improving service quality [19].

For example, in customer relationship management (CRM), integrating IoT can help businesses gain in-depth insights into customer interactions and preferences by analyzing data from various touchpoints, like smart devices and IoT-enabled environments. This information can be leveraged to deliver highly personalized services, anticipate customer needs, and improve overall customer satisfaction (see

Table 2. Advantages of using IoT in customer relationship management.

Advantage	Description
Real-Time Data Collection	IoT devices can collect data on customer behavior in real time, providing valuable insights for service personalization.
Advanced Personalization	Analyzing the collected data enables companies to provide personalized recommendations and offers, enhancing customer satisfaction.
Process Automation	Integrating IoT allows for the automation of processes, reducing the need for human intervention and increasing operational efficiency.
Proactive Monitoring	By continuously monitoring customer interactions, companies can anticipate their needs and intervene before issues arise.
Service Improvement	IoT data can be used to identify areas needing improvement, allowing companies to optimize their services.
Increased Customer Loyalty	By providing personalized and relevant experiences, companies can build stronger relationships with customers, increasing their loyalty.

). In addition, IoT-driven CRM systems can automate repetitive tasks, improve response times, and offer predictive analytics, enabling businesses to stay competitive in an increasingly data-centric market [20].

3.3. CRM

CRM is a strategic and technological approach that businesses use to monitor interactions with customers throughout their entire life cycle [21].

In the digital age, CRM has evolved from simple contact management to a complex and integrated system essential for the success of modern businesses. By centralizing data and analyzing customer behavior in real time, an effective CRM system transforms customer interactions into personalized and memorable experiences. The integration of advanced technologies, such as the Internet of Things (IoT) and distributed Web systems solutions, allows companies to access up-to-date information and anticipate customer needs, facilitating strategic decision making. This not only increases operational efficiency but also contributes to the strengthening of customer loyalty, transforming every interaction into an opportunity for growth and innovation.

Figure 4 presents the architecture of a CRM system, with the “Customer Data Management” module at its center, playing a key role in collecting, processing, and distributing information across various departments. The sales, marketing, and support teams maintain bidirectional connections with this central module, enabling data exchange and personalized customer interactions.

Additionally, each team is linked to a specific functional module: “Sales Automation” for the tracking of deals and tasks, “Marketing” for the building of campaigns, and “Customer Support” for the management and resolution of issues.

Table 3. Features and benefits of CRM.

CRM Module	Function	Advantages
Data Management	Collecting and storing customer data, interaction history	Centralized data storage, better customer management
Sales and Marketing	Automating marketing campaigns, managing sales opportunities	Increased marketing efficiency, higher sales
Customer Service	Managing customer inquiries and issues, tracking satisfaction	Improved customer service, higher satisfaction and loyalty
Analytics and Reporting	Generating reports on sales, performance, and customer data	Better business understanding, informed decisions, and strategies
Process Automation	Automating routine tasks such as email sending, reminders, and alerts	Saving time and resources, better efficiency and accuracy
Interaction Management	Tracking contacts and communication with customers across channels	Enhanced communication, personalized approach to each customer

summarizes the key modules of CRM systems, their functions, and the benefits for businesses. Each module plays a specific role in improving customer data management, optimizing sales and marketing, and enhancing customer service. The data management and analytics modules provide centralized information and valuable insights that help to make informed decisions. Sales and marketing modules, along with process automation, increase the efficiency of marketing campaigns and reduce human involvement in routine tasks, leading to higher conversions and time savings.

Figure 5 demonstrates the stages of automation within a CRM system, including data collection, task automation, and sales forecasting, streamlining workflows for greater efficiency.

Combining tools, processes, and strategies, CRM enhances customer satisfaction, loyalty, and profitability by centralizing customer-related data and enabling personalized engagement. CRM systems typically include tools for managing customer data, automating sales and marketing activities, and analyzing interactions. These systems provide a unified view of the customer journey, from the first contact to post-sale support, empowering cross-departmental teams to align their efforts and deliver consistent, high-quality experiences. Automation features streamline workflows, such as follow-ups, while data analytics reveal trends, enabling predictive actions such as upselling or customer retention strategies [22].

The system’s process is based on data centralization and task automation, ensuring efficient synchronization between departments. Data from marketing campaigns are sent back to the sales team for lead tracking, while feedback from support is analyzed to improve services. In addition, automated processes such as sales forecasts, targeted marketing recommendations, and support requests facilitate fast, data-driven decision making. This architecture ensures a unified view of the customer, improves operational efficiency, and delivers a better, more personalized customer experience [22].

Advancements in artificial intelligence (AI) and machine learning have significantly reshaped CRM. Predictive algorithms now examine both historical and real-time data to pinpoint opportunities, while AI-powered assistants streamline customer support and recommend the best sales strategies. Cloud-based CRMs enhance scalability and accessibility, allowing businesses of all sizes to securely access and utilize data in real time.

A critical aspect of CRM is ensuring data security and compliance with regulations like GDPR and CCPA. Implementing encrypted communications, secure storage, and role-based access controls protects sensitive information, fostering customer trust. Using these features, organizations can optimize both operational efficiency and customer relationships, making CRM an indispensable tool in the modern business landscape [23].

CRM systems also optimize customer service by providing tools for quickly resolving issues and managing interactions across different channels. With integrated communication management, these systems ensure a personalized experience that increases customer loyalty and satisfaction. As a result, CRM systems not only improve business processes but also contribute to long-term growth and company competitiveness [24].

3.4. CRM Using Data from IoT

The integration of IoT data into CRM systems represents a revolutionary advancement, enabling organizations to leverage real-time, context-rich insights for better CRM. IoT devices continuously collect and transmit data, offering unprecedented opportunities to understand customer preferences, behaviors, and product usage. By incorporating these data, CRMs deliver hyperpersonalized experiences and proactive services [25].

IoT–CRM integration allows for real-time monitoring and automation. For example, smart home devices can report usage patterns, enabling energy companies to offer customized efficiency tips or maintenance alerts. Similarly, wearable fitness trackers can feed data into CRM systems to generate personalized recommendations, improving customer satisfaction and engagement [25].

Figure 6 shows a diagram of the integration of the IoT with CRM. The integration of IoT devices begins with the collection of data from various sensors and devices such as thermostats, smartwatches, and motion sensors. These data are sent to edge processing, where preliminary filtering and processing are performed to reduce latency and cloud load. The processed data are then sent to cloud storage, which allows for the storage of large volumes of data for further processing and analysis.

Subsequently, the data pass through the CRM system, which provides modules for data management, analytics, and automation. Based on these analyses, the CRM system can generate automatic actions, such as sending maintenance alerts or adjusting the settings of IoT devices. These actions are sent back to the IoT devices, enabling process optimization and improving customer interaction [25].

From a technical point of view, IoT-enhanced CRMs rely on distributed architectures and cloud computing to handle large-scale data processing. Advanced analytics and machine learning algorithms transform raw IoT data into actionable insights, while real-time frameworks ensure immediate responsiveness. These capabilities enable businesses to anticipate customer needs, resolve issues preemptively, and foster deeper relationships.

Security and privacy are paramount in IoT-CRM ecosystems, given the sensitive nature and volume of data involved. Robust encryption, secure APIs, and adherence to international data protection standards ensure that organizations can safeguard information while utilizing it to optimize customer experiences. Clear governance policies and compliance frameworks are essential for maintaining trust in this context.

By integrating IoT data into CRMs, businesses also achieve process automation and operational efficiency. For example, predictive maintenance alerts from IoT devices can automatically generate service requests in CRM systems, while usage insights can trigger targeted marketing campaigns. This level of automation reduces human intervention, improves precision, and saves resources [25].

The use of IoT-generated data in CRM introduces significant privacy challenges, particularly concerning customer consent and regulatory compliance. To address these challenges, organizations must implement strict data governance policies, ensuring transparency in data collection and processing. Role-based access control (RBAC) and encryption protocols are essential to protect sensitive information, while data minimization strategies help limit exposure to only necessary data points.

Data collected from IoT devices are transmitted through edge systems, which process them before entering them into the CRM. This data are then analyzed and used to automate marketing, technical support, and customer relationship management processes.

The diagram illustrates the integration of IoT data into CRM systems using edge computing and a distributed Web system. IoT devices, such as surveillance cameras, smart watches, and temperature sensors, collect and send data using protocols such as MQTT and CoAP. The surveillance cameras (TP-Link Tapo C200, TP-Link Corporation, Shenzhen, China), smart watches (Xiaomi Mi Smart Band 6, Xiaomi Inc., Beijing, China), and temperature sensors (DHT22, Aosong Electronics Co., Guangzhou, China) were used in this study. These data are pre-processed at the edge computing level, then stored and managed by a distributed Web system before being used in CRM software (HubSpot CRM, free plan, accessed in February 2025) to optimize the user experience (Figure 7).

Main integration steps are outlined as follows:

• Data Collection: IoT devices (e.g., sensors, beacons, and smart devices) record relevant parameters.
• Edge Computing: Latency is reduced by preprocessing data before sending them to the CRM system.
• Transmission to the Distributed Web system: Data are stored and managed in a distributed system, providing scalability and redundancy.
• Data Analysis and Use in CRM: CRM uses data to personalize the customer experience and make decisions in real time.

IoT integration into CRM can include a variety of devices, such as the following:

• Temperature and humidity sensors (used in retail and logistics to monitor products).
• Bluetooth beacons (for location tracking in stores and events).
• Camera and video analytics (for detecting customer behavior).
• Wearable devices (which provide data on the user’s health and activity).

In summary, the fusion of IoT and CRM systems allows organizations to remain agile, customer-focused, and innovative in a rapidly evolving digital landscape. This integration elevates CRM capabilities, driving personalization, improving operational efficiency, and building stronger and longer-lasting customer relationships.

3.5. Personalization Algorithm

The personalization algorithm is a critical component of our system, designed to dynamically adapt to real-time IoT data. The algorithm follows these steps:

• Data Ingestion: IoT devices continuously stream data to the distributed Web system.
• Data Preprocessing: Edge nodes filter and sanitize incoming data, reducing noise and ensuring quality.
• Feature Extraction: Relevant features (e.g., user behavior patterns) are extracted for analysis.
• Model Application: A collaborative filtering algorithm predicts user preferences, utilizing both historical and real-time data.
• Recommendation Generation: Personalized content is generated and refined based on continuous feedback loops.

Efficiency is measured through multiple performance metrics, including the following:

• Accuracy Rate: Evaluated against user choices (achieving up to 90% accuracy at peak loads).
• Latency: Average response times under different request loads.

This detailed process ensures a robust personalization mechanism that scales efficiently with growing data streams.

4. Results and Discussion

The use of distributed architectures within CRM systems has highlighted a significant improvement in the processing speed of data generated by IoT devices. By distributing processing tasks across multiple nodes and using load-balancing techniques, the platform can handle large volumes of data in real time without affecting performance. This process was achieved by implementing a horizontal processing system, which allows for the dynamic addition of new resources to support the ever-growing data requirements [26].

While the initial experiments used four server nodes to simulate the distributed environment, we acknowledge that this is a limited sample size. To improve the generalizability of our findings, future work will involve the following:

• Scaling of the Architecture: Additional tests will be conducted with increased numbers of nodes (e.g., 8–16 nodes) to evaluate the architecture’s performance under larger-scale deployments.
• Variability Testing: Simulations will be implemented under diverse network conditions and data volumes to ensure robust and universal applicability.

These enhancements aim to validate the system’s scalability and efficiency across various operational scenarios.

The incorporation of IoT data into CRM systems provides companies deep insights into customer behavior and preferences, allowing for advanced personalization of interactions with them. IoT devices, through their smart sensors, collect real-time data about product and service usage, as well as operating conditions. These data are analyzed using machine learning algorithms implemented in Web systems to identify patterns and anticipate customer needs [27].

Integrating IoT with CRM systems significantly improves operational efficiency by automating customer interactions and personalizing services. IoT devices continuously collect real-time data on customer behavior, which are then processed by CRM platforms to predict needs, recommend personalized offers, and trigger proactive actions. This reduces the need for manual intervention, allowing businesses to respond to customer demands faster and more effectively. For example, CRM can automatically initiate marketing campaigns based on real-time data from IoT devices, such as customer preferences or behavior patterns. In addition, IoT data are instrumental in areas such as inventory management, predictive maintenance, and supply chain optimization. By automatically forecasting stock levels and maintenance needs, companies can ensure smoother operations, lower operational costs, and greater resource efficiency, ultimately improving overall productivity [28].

Distributed Web systems within CRM platforms allow businesses to scale efficiently as their data and device networks grow. As the number of IoT devices increases, companies need to handle larger data volumes without compromising performance. Distributed Web systems offer horizontal scalability, meaning businesses can add new nodes or servers to their network to manage increased loads without interrupting services [29]. This capability allows CRM systems to seamlessly integrate additional IoT devices and customers, ensuring that the system can handle growing data volumes as the business expands. The flexibility to scale infrastructure without significant hardware upgrades or downtime allows businesses to remain agile and adapt to market demands. By maintaining high system performance, even during peak usage, companies can confidently grow their customer base and data collection capabilities [30].

Experimental tests targeting three main aspects were conducted to demonstrate the efficiency of integrating the IoT system with the distributed CRM architecture:

• System response time depending on the number of requests processed simultaneously.
• Utilization of hardware resources (CPU and RAM) to evaluate the scalability and efficiency of the task distribution.
• The efficiency of the personalization algorithm based on IoT data, measured by the accuracy of the generated recommendations.

The tests were conducted in a controlled environment using a distributed server with four nodes, each with the following specifications: an Intel Xeon 2.4 GHz CPU (8 cores) (Intel Corporation, Santa Clara, CA, USA), 32 GB RAM, and NVMe SSD storage. The requests were simulated using a traffic generator based on IoT data, and the results are presented below.

These nodes were connected through a local area network (LAN) with a consistent bandwidth of 1 Gbps, ensuring low-latency communication. Load balancing was implemented using a round-robin strategy to evenly distribute incoming requests across all nodes, while a failover mechanism was employed to ensure task continuity in the event of node failure.

Data distribution across the nodes was managed using a partitioned database approach, where each node handled a specific subset of the IoT-generated data. This design improved both query performance and system scalability. To capture the impact of network variability, we introduced latency simulations ranging from 5 ms to 100 ms to reflect real-world conditions in distributed environments. This allowed us to assess how network performance fluctuations impact system response time and resource utilization.

A key parameter in performance evaluation is system latency, defined as the response time to real-time requests. Tests were conducted for a variable number of simultaneous requests, from 10 to 10,000, to observe the system’s ability to handle high loads (see

Table 4. System response time based on the number of requests.

Number of Requests	Response Time (ms)
10	45 ± 5
50	52 ± 4
100	58 ± 6
500	85 ± 7
1000	140 ± 10
5000	310 ± 15
10,000	620 ± 20

and Figure 8).

After analyzing Table 4, a clear correlation between the number of requests and the system response time is highlighted, indicating that the distribution of tasks between nodes contributes significantly to maintaining a reasonable processing time, even at high loads. This linear behavior, with a moderate increase in the response time as the number of requests increases, reflects the efficiency of the implemented distributed architecture. Thus, the system demonstrates a remarkable capacity to scale, maintaining optimal performance by evenly distributing the tasks between the nodes.

In parallel, observations on the use of resources (CPU and RAM) highlight the fact that the load-balancing and data distribution mechanisms are well calibrated to avoid the overloading of any node. This approach allows for a dynamic allocation of resources, ensuring that, regardless of the volume of requests, the system remains robust and reactive. Experimental results indicate that although the increase in requests leads to a more intensive use of resources, this remains below critical thresholds, confirming the viability of the proposed solution for realistic operational environments.

To evaluate the efficiency of resource usage, CPU and RAM consumption were monitored at different request levels.

A progressive increase in resource consumption is observed but without reaching system saturation, which demonstrates the efficiency of the distributed architecture. At values above 5000 requests, CPU utilization approaches 80%, suggesting the need for a more efficient distribution of tasks (see

Table 5. CPU and RAM usage based on the number of requests.

Number of Requests	CPU Usage (%)	RAM Usage (GB)
10	4 ± 1	0.3 ± 0.05
50	9 ± 2	0.7 ± 0.1
100	14 ± 3	1.2 ± 0.2
500	37 ± 5	2.8 ± 0.3
1000	55 ± 6	4.5 ± 0.5
5000	78 ± 5	7.2 ± 0.6
10,000	92 ± 4	9.8 ± 0.8

and Figure 9).

Another crucial factor is the system’s ability to tailor the user experience through personalized recommendations based on IoT data. Accuracy was measured by comparing the system’s predictions to actual user choices in a test set.

The results show that as the system processes a larger volume of IoT data, the accuracy of recommendations increases, reaching 90% for 10,000 requests. This confirms the efficiency of the proposed model in interpreting user data (see

Table 6. Accuracy of personalized recommendations based on IoT data volume.

Number of Requests	Recommendation Accuracy (%)
10	72 ± 3
50	76 ± 3
100	79 ± 2
500	82 ± 3
1000	86 ± 2
5000	88 ± 2
10,000	90 ± 1.5

and Figure 10).

Table 5 and Table 6 present empirical measurements directly aligned with our research objectives. The system’s response time, CPU and RAM usage, and recommendation accuracy serve as key performance indicators (KPIs) for evaluating the efficiency of the distributed architecture in real-time CRM applications. These metrics validate the hypothesis that distributing IoT data across multiple nodes improves processing speed and enhances the personalization of customer experiences.

Network variability tests revealed that the system maintains stable performance up to a simulated latency of 50 ms. Beyond this point, response time increased by approximately 12%, while CPU usage rose by 8%, indicating a moderate impact on system efficiency. Despite these variations, recommendation accuracy remained above 85%, underscoring the system’s robustness under diverse operational conditions.

These results demonstrate that our distributed architecture provides faster, more accurate, and secure data processing, addressing industry gaps in real-time IoT–CRM integration (see

Table 7. Comparative performance of our system vs. industry standards.

Metric	Our System	Industry Standards
Response Time (1000 req)	140 ms	250 ms (estimated)
Recommendation Accuracy	90%	75–80%
Security Measures	TLS/SSL, RBAC, Edge Processing	Basic Data Encryption
Scalability	Dynamic Node Expansion	Fixed Node Environment

).

Privacy remains a critical aspect of IoT-driven CRM systems. Our implementation ensures that personal data are processed in compliance with GDPR and other regulatory standards by anonymizing non-essential information and restricting access through encrypted communication channels. The results indicate that integrating security measures such as edge processing and role-based access contributes to both improved privacy and system performance.

To highlight the effectiveness of our architecture, we compared its performance with that of existing industry standards.

5. Conclusions

In conclusion, optimizing the customer experience by analyzing real-time data generated by IoT devices and using distributed Web systems within CRM platforms bring about significant advantages in terms of both security and system performance. Security is essential because distributed Web systems reduce the risks associated with centralized data storage by dispersing storageacross multiple nodes. This architecture improves resilience against cyber threats, such as DDoS attacks, and guarantees the protection of sensitive customer information through encryption and role-based access control.

A major benefit of integrating distributed Web systems with edge processing is reduced latency. Faster response times, the facilitation of real-time interaction, and improved service delivery can be achieved by processing data closer to their source and distributing the workload across multiple servers. In situations such as alerts or immediate maintenance actions triggered by IoT devices, this enables faster decision making and more efficient customer service without waiting for data to reach a central server.

The experimental results confirm that the proposed distributed Web architecture significantly improves CRM system performance by reducing latency, enhancing scalability, and increasing the accuracy of IoT-driven recommendations. The addition of edge processing provides further opportunities to optimize system efficiency by offloading tasks to localized environments, reducing central node congestion, and enabling faster decision making.

Our proposed architecture is adaptable to businesses of varying scales:

• Small and Medium Enterprises (SMEs):

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  Minimal hardware requirements (four to eight nodes).

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  Cost-efficient implementation with cloud-based solutions.

• Large Enterprises:

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  Highly scalable, supporting 16+ nodes.

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  Enhanced security protocols to manage sensitive customer data.

A cost–benefit analysis suggests that the distributed approach reduces infrastructure bottlenecks and enhances customer retention through improved personalization.

Incorporating network variability into the analysis demonstrates the system’s resilience under dynamic conditions. Future research will explore the integration of advanced edge computing techniques and adaptive load-balancing algorithms to maintain high performance across diverse operational environments. This work lays a foundation for the building of scalable, secure, and responsive CRM systems capable of harnessing real-time IoT data for superior customer experiences.

While our study offers substantial improvements in IoT–CRM integration, several limitations must be acknowledged:

• Experimental Environment: Simulations were conducted in a controlled environment; real-world variability could affect performance outcomes.
• Node Limitations: The reported experiments involved only four nodes; future research will scale experiments to larger environments.
• Algorithm Scope: Our algorithm is optimized for accuracy but does not yet address resource efficiency under extreme loads.

These limitations present future research opportunities to refine and expand our framework.

These improvements not only increase operational efficiency but also contribute to increased customer satisfaction and strengthening of their trust in the provided online service. The experimental results demonstrate that the proposed distributed architecture significantly reduces system response times, improves scalability, and enhances the accuracy of personalized recommendations. By processing IoT-generated data in real time and leveraging distributed computing, businesses can offer hyper-personalized services while maintaining high system efficiency and security.