Open AccessEvaluation of Customer Orientation of Russian Companies Using Machine Learning Methods
Author(s) -
I. Y. Melnikova,
A. E. Snezhkin,
O. V. Mikhaylova
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/753/7/072026
Subject(s) - computer science , customer orientation , quality (philosophy) , automation , objectivity (philosophy) , orientation (vector space) , sample (material) , process (computing) , metric (unit) , customer intelligence , artificial intelligence , data mining , knowledge management , data science , marketing , customer retention , business , service quality , engineering , mathematics , mechanical engineering , philosophy , chemistry , geometry , epistemology , chromatography , operating system , service (business)
In this paper, the researchers’ attention was focused on how the strategy of customer orientation is reflected in the internal documents of companies and materials posted on the websites. The authors used a formalized method of studying text information-content analysis. The results of the pilot study indicate that for a number of reasons, the client-oriented approach is not a business ideology for many large companies; most of the sample studied is characterized by a formally stated interest in the needs of customers. In order to ensure the possibility of carrying out large - scale regular research, the concept of building a software package for the analysis of customer orientation companies using machine learning methods – SP ACOC has been developed. To implement this concept, the quality metric of classification algorithms was determined; several algorithms were trained; a program was developed and tested, which determines the degree of customer orientation of the company according to its documents according to the chosen algorithm Automation of the process of evaluation of customer orientation companies will reduce its complexity, expand the range of objects studied, improve the accuracy and objectivity of the results.