Method for analyzing the location, assortment and success of outlets based on transactional data
Pages 29 - 34
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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Published In
December 2020
266 pages
ISBN:9781450388559
DOI:10.1145/3446999
Copyright © 2020 ACM.
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Association for Computing Machinery
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Published: 09 April 2021
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- The Russian Science Foundation
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ICIT 2020
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