Big data for retail space optimization

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SAN JOSE, Costa Rica, (CentralAmericaData) – Defining the design of a shopping center, determining the mix of store types and the optimal size of stores to maximize the benefits of commercial areas, are objectives that can be achieved through the proper analysis of large volumes of data.

The volumes of data being generated in the digital environment every second enables business leaders to make well-informed decisions that are based on the analysis of empirical evidence.

  • How many stores should be built in a new shopping center or shopping plaza?
  • What types of retail should be contemplated in the design of the property?
  • How many consumers might visit the site?
  • In terms of the mobility of people within the shopping center: How much rent should be charged to tenants?

These are some of the questions that a commercial space optimization model can answer to decision makers, when the project is in its early stages or at the moment of readjusting environments.

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For Erick Macias, senior data analyst at CentralAmericaData, from the use of different data sources, their processing and analysis, it is possible to determine the combination of types of stores and the optimal size of the same, which allow to obtain the best use of the commercial space and that the operator of the same receives the greatest benefit for the management and administration of them.

According to Macias, it is also “… possible to determine the natural flow of people within a shopping center or plaza, which is relevant information to determine those spaces with higher flow per person and therefore tend to bill a little more than the average, which also leads to establish a price structure for leasing space, specific for each of these.

See “Business Intelligence: Data as Maps

When asked about the variables that influence the models used to make the decision on how to distribute commercial spaces, the specialist explained that “… square meters of different commercial developments and their type (clothing store, jewelry store, retail, etc.), both successful and unsuccessful, are usually taken from automated digital data collection mechanisms. This allows us to have a lot of information and evidence of what works best in the design of commercial spaces.

Finally, it is also possible to incorporate financial information and sociodemographic characteristics of the population that usually visits shopping malls, so that based on this information we can estimate turnover levels by type of business, concludes the senior data analyst.

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