Peter Evgenievich Gladilin
ABSTRACT
Accounting for consumer demand in different categories in various districts of the city is one of the most important stages in planning the development of a business related to the sale of goods and services. The exponential growth in the amount of data available for analysis in recent years opens up new opportunities for marketers to form optimal strategies for the development of a chain of retail outlets based on real data on customer purchases, which can be developed using data analysis and machine learning methods. The paper proposes a predictive analytics method based on transactional data on purchases of private clients of banks in an urban network of retail outlets. Trends in purchasing activity in various categories of goods and services, combined with the identified geolocation, demand and competitive characteristics of outlets, can be used to predict consumer demand in various city districts and make recommendations on changing the assortment of outlets to reduce costs and increase profits.
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