Кластерний аналіз клієнтської бази даних підприємств сфери послуг
Cluster Analysis of Customer Database of the Service Enterprises

Summary/Abstract: The aim of the article is to use cluster analysis for customer segmentation and the development of an automated system for processing customer orders, aimed at increasing the efficiency of the service enterprise.The development of the service enterprises, which is of particular importance under economic restructuring, has been studied. The service sector directly affects the quality of life of the society, economic growth and the position of Ukraine in the world market of services. It is noted that the development of service enterprises is possible only in the context of the introduction of innovative development models, involves the search for the most effective management methods and technologies that can accelerate the adaptation of these enterprises to the latest global trends.In this article, the term “customer base” refers to data about customers, their characteristics, consumer behavior and past purchases, which are collected and organized in such a way that they can be quickly retrieved to obtain the necessary information. In addition to data on existing customers, the customer database contains information about potential customers.The concept of customer base segmentation as the separation of clients into homogeneous groups according to certain criteria and the clustering of the client base as the distribution of clients into groups that correspond to stable segmentation features are considered.For client clustering, the k-means method was used. The cluster analysis algorithm is presented in the form of a flowchart and a use-case diagram.The result of the research conducted by the authors was the software development of an automated system for processing client orders under variable demand using clustering methods and client database segmentation. The use of economic and mathematical methods of cluster analysis and customer base segmentation makes it possible, unlike the use of an intuitive empirical method of separating customers into groups, to identify segments according to specified criteria depending on the task for the decision maker and is much more appropriate for effective management services.

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