Selecting Location of Retail Stores Using Artificial Neural Networks and Google Places API

Mehmet Hakan Satman, Mustafa Altunbey


Selection of locations of retail stores (similarly, facilities) is a kind of decision problem in which the researcher targets maximum profit or minimum cost by using statistical or judgmental forecasts. In many cases, statistical forecasts are based on survey data or data collected for an other store which are not reliable as the survey may not measure the potential demand or the other store that is used for comparison may not be similar enough. Addition to this, estimating the potential demand for a non-existing store can be expensive. Google Places API is a software library which provides a nearby search utility that is able to report several kinds of institutions, establishments and other objects placed in a circumferential area for a given radius. In this paper, we suggest a method which is based on estimating the relation between environmental properties of existing retail stores and their financial rankings. We remark that feed forward neural networks are capable to reveal such a relation as they permit using both integer, real and binary variables in the model and non-linearity. We apply our method on the location selection problem of a retail stores chain in Turkey (Note 1) which has $144$ clients in its organization. Expert opinions in the organization show us applicability of this method is high in similar situations.

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International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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