Mapping regional business opportunities using geomarketing and machine learning

Abstract

Abstract: The objective of this study is to develop a quantitative tool, based on Machine Learning and Geomarketing to identify business opportunities and contribute to the strategic process of local choice of franchises’ network selecting regions that have a high demand forecast and a lack of product supply. In addition, we conducted a qualitative analysis of the selected business places based on defined criteria. This prediction is given by constructing a consumption pattern, defined by a classifier, based on the characteristics of the reserved rights. Initially, for a better understanding on this subject, a theoretical background was made covering the main concepts about Geomarketing and Machine Learning and its applications. After that for a demonstration of the results, we opted for the application of the method for the market of fine chocolates (Cacau-Show) in the Distrito Federal. The main databases used in this paper were Pesquisa de Orçamentos Familiares and from Instituto Brasileiro de Estatística e Geografia (IBGE). As a result, the Standardized Spend was obtained, which indicates the requirement for each Censitar Sector, as georeferenced information of the competition, containing 44 stores that have as their main product of fine chocolate, and as digital meshes of the Federal District. The crossing is available for the elaboration of a map that facilitates the identification of the business opportunities for the market of fine chocolates in the Distrito Federal, Brazil.

Keywords

• Geomarketing
• Support vector machine
• Regional economics
• Estrategic franchises
• Supervised learning