Modern Methods of Development of Algorithms for Identifying and Defining the Flow of Applications in the CRM System

• Kamil Fikratovich Kerimov

  DSc, Tashkent University of Information Technologies

• Buriev Absamatovich Yusuf

  Head Teacher, Tashkent University of Information Technologies

• Mirzaev Bokhodirjon

  Master of Tashkent University of Information Technologies

The rapid evolution of customer relationship management (CRM) systems has transformed the way businesses interact with clients, streamline operations, and manage sales pipelines. As these systems continue to expand in functionality and complexity, the ability to track, identify, and analyze the flow of applications within the CRM has become a crucial component of optimizing customer engagement and internal workflows. This article presents a comprehensive study on the development of intelligent algorithms aimed at identifying, defining, and managing the flow of customer applications across various modules of a CRM system. The research focuses on designing algorithmic solutions that can automatically classify and route applications based on predefined criteria such as customer profile, application content, urgency, and department responsibilities. The proposed algorithms incorporate techniques from data mining, natural language processing, and decision tree modeling to ensure that applications are processed efficiently and directed to the appropriate personnel in real time. Furthermore, the study explores the integration of these algorithms with CRM dashboards and notification systems to enhance transparency and traceability of application statuses throughout their lifecycle. Emphasis is placed on improving CRM system performance through intelligent automation, reducing human error, increasing response time, and elevating customer satisfaction. The article also evaluates the current challenges faced by enterprises in managing application flows manually, including loss of data, delays in response, and customer dissatisfaction. By developing dynamic algorithmic structures capable of learning and adapting to changing patterns in user behavior and application trends, the system becomes progressively smarter and more responsive. Experimental results from real-case scenarios in corporate CRM environments are discussed, demonstrating the effectiveness of the algorithms in categorizing applications, reducing backlog, and improving decision-making efficiency. The study concludes by highlighting the future potential of incorporating artificial intelligence and machine learning techniques to further advance application flow automation in CRM systems. This work is relevant to software developers, data analysts, CRM administrators, and business strategists who seek to enhance customer service operations through technical innovation. It also serves as a foundational study for further academic research in the fields of intelligent systems, process automation, and customer relationship technologies.

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