Management of retail network development using geoanalytical and artificial intelligence technologies

The article investigates the theoretical and applied foundations of managing the development of retail networks based on geo-analytical and intelligent technologies. It is substantiated that under conditions of intensifying competition, dynamic changes in consumer behavior and the deepening digitalization of the economy, traditional approaches to selecting locations for retail outlets, which are primarily based on expert judgments and a limited set of analytical tools, no longer ensure the required level of accuracy and justification of managerial decisions. In this context, the need to transition to modern analytical approaches that integrate spatial data, advanced analytics and intelligent technologies becomes particularly relevant. It is determined that one of the key directions for improving the management of retail network development is the use of geo-analytics, which allows for a comprehensive assessment of territories by taking into account a wide range of spatial, socio-economic, infrastructural and behavioral factors. Unlike traditional methods based on simplified service radius models, modern geo-analytical tools enable the use of isochrones that reflect real accessibility considering transport infrastructure and mobility patterns. This approach ensures a more accurate definition of service areas and prevents the overestimation of location potential. Particular attention is paid to the analysis of the competitive environment, where the application of the Huff model allows for estimating the probability of consumer choice depending on the attractiveness of retail outlets and distance. The integration of this model with geo-analytical tools makes it possible to move from simplified assessments to probabilistic modeling of consumer flows, ensuring a more precise evaluation of potential traffic, sales volumes and the impact of internal competition within the network. The study also emphasizes the growing role of machine learning technologies, which enable the processing of large volumes of heterogeneous data and the identification of complex nonlinear relationships between multiple factors. The use of such models allows for forecasting key performance indicators of retail outlets, including the number of transactions, revenue levels and payback periods, thereby significantly increasing the reliability of planning decisions. It is proven that the integration of geographic information systems, spatial analysis, big data and machine learning forms the basis for a data-driven approach to retail network management. This approach ensures higher forecasting accuracy, reduces the time required for analytical procedures, minimizes investment risks and supports the optimization of the territorial structure of retail networks. At the same time, it is established that the effectiveness of implementing geo-analytical and intelligent technologies depends on the quality and completeness of data, the level of digital maturity of enterprises and the availability of relevant analytical competencies among personnel. The results of the study confirm that geo-analytical and intelligent technologies create a fundamentally new paradigm for managing retail network development and serve as an important factor in strengthening the competitiveness and adaptability of enterprises in modern economic conditions.