Retail trade area analysis using multiple variables modeling at residential zone

Abstract

Purpose – the purpose of the paper is to set up a method of retail trade area analysis using multiple variables modeling at a residential zone. Research methodology – system analysis; regression analysis; correlation analysis; simulating; urban characteristics analysis. Findings – retail trade area analysis using multiple variables modeling at residential zone based on the proposed method is performed by directly processing and analysing data in a separate zone. Research limitations – the obtained results can be used for data variation range of conducted experiment. Practical implications – the proposed method makes some adjustments in estimating the limits of the trade area, specifying it with the help of the non-linearity factor and area slope, thereby changing the shape of the circle into a complex figure, depending on the geographical landscape and the structure of the roads. Implementation is made for one of the consumer’s zone in Kharkiv, Ukraine. The results allowed to adjust the trade zone, which in fact reduced it twice. Originality/Value – the probabilities of visiting retailers have been calculated according to the developed model considering surrounding limitation. In such conditions, the analysis of the consumer market and identification of the trade zone of the retailers is one of the forms to improving the efficiency of the retailer functioning

Assessing the Utility of Retailer Based on Generalized Costs of End-Consumers

“…In order to find the initial values of the parameters of end-consumers and retailer, natural studies were conducted. The studies use a two-stage methodology for determining the relevant geographic retail market (Galkin, et al, 2019b): distance, the nonlinearity factor, slope factor, and so on ( Figs. 1 and 2).…”

“…Assessing the utility of retailer presented via the O-D matrix of generalized cost of end-consumers (17). The generalized costs are calculated on models (2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21) and the results of calculations are given in Table 5. Assessing the utility of retailer presented via the O-D matrix of generalized cost of end-consumers (1).…”

“…The rising interest in the high-quality products at low prices, saving time or minimizing the level of fatigue during the purchase elements, stimulates development of utility of endconsumers. Assessment of the costs and benefits of clients uncovers new opportunities for improving their service, overestimates efficiency of the logistics system, and provides a new concept for consumerproduction collaboration, which aims to increase joint efficient functioning (Galkin, et al, 2019b).…”