МОДЕЛИРОВАНИЕ ПРОЦЕССОВ, ФОРМИРУЮЩИХСЯ РОЗНИЧНЫМИ ТОРГОВЫМИ СЕТЯМИ

Abstract

The main purpose of this study is to consider the influence of internal and external factors on the development of services of retail trade networks. Studying the internal factors of development of services, it should be noted that for enterprises of network formats, which include retail trade networks, it is very important to have their own service standards, personalized approaches of interaction and contact with consumers to ensure the quality of the provision of trade services. Attentive attitude towards customers is ensured, among other things, by reducing the tension and intensity of staff work. The article presents a hypothesis about the identified internal and external factors affecting the development of retail trade networks, showing their influence or connection. Customer focus is seen as one of the priority factors and is divided into two categories in relation to customers and employees of the retail chain. The quality of trade services largely depends on the customer focus of consumers. Using a paired regression model, we analyzed the impact on the share of attracted customers in relation to factors such as customer satisfaction with the service in order to understand how satisfied consumers are with the interaction with the enterprise; average frequency of visits to the retail network (customer traffic) per week per 1 m2 of retail space; net support index; an indicator of dissatisfaction with the service. The dependence and influence of these factors on customer satisfaction have been determined. The analysis of environmental factors was carried out using the method of principal components, three groups of factors and their impact were identified. The resulting indicators of the «dynamics of retail trade turnover per capita» are the average per capita cash income of the region's population, the availability of retail space, the development of transport infrastructure, the proximity of administrative centers, accounting for which made it possible to forecast the dynamics of total regional income.