Geomarketing: Real Success Stories

Making decisions based on models that collect sociodemographic information of the area, characterizing the commercial environment of the possible locations of new stores and including data on the tastes and preferences of potential customers, reduces the risks of investments in the opening of new points of service.

Thursday, February 18, 2021

The work teams and "data driven" companies, organizations that are based on the efficient use of data as the axis of all their processes, have found in the Commercial Intelligence and Geomarketing solutions, a possibility to reduce the risks in new investments associated with expansion plans and opening of new stores.

In Central America there are corporate groups that already benefit from this type of solutions, as is the case of a regional chain of stores dedicated to the sale of sporting goods.

Problem to be Solved

A corporate group involved in the commercialization of sporting goods needed, as part of its expansion plan, to identify the most suitable areas for the opening and implementation of new points of sale in Costa Rica.

Our Business Intelligence Solution

In order to solve this problem, CentralAmericaData developed a probability model that collects geographic, socio-demographic and subjective information on preferences and interests of potential customers of the chain's points of sale.

Among the variables contemplated by the model were: vehicle flow index in the areas under analysis, heat maps of the floating and resident population, concentration of sports stores in the area, interest in the "sports" category evidenced in the digital environment by consumers who live or travel in the areas under analysis, and a characterization of demand, with an estimate of potential spending in stores similar to the ones our client intended to open.

Invoicing of Existing Stores and Potential for New Outlets 

In order to enrich the zone evaluation model, we incorporated billing information from the company's existing stores, since by comparing areas with similar commercial characteristics to the new locations, it is possible to estimate the sales that could be achieved.

This information also allows decision-makers to verify whether an existing point of sale is achieving the expected sales levels or whether they are below acceptable levels. The data collected are of relevance when establishing how profitable a store is in terms of the commercial environment in which it is located.

Methodology and Data Sources Used 

The methodology used for the development of this solution consisted of the collection, cleansing, homologation, validation and analysis of all information from secondary and tertiary sources, including, but not limited to: records in social networks, online behavior, business directories, vehicle traffic, consumer surveys and import records.

Through the use of Big Data and machine learning techniques, a model was generated to identify the most suitable areas for the opening of new establishments.

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