Consumer Mobility as Data

Using today's technology, it is possible to know and accurately monitor consumer mobility, identify the places they visit, how often they do so, at what times and on what days, and transform this mobility and pedestrian flow data into solutions for optimizing commercial and marketing strategies.

Friday, June 4, 2021

People mobility is a concept that covers much more than just movement. It involves understanding where they travel, from where they come from and to where they go, what places they visit, at what times of the day and on what days of the week. There are now multiple ways to calculate it, from records generated on Internet sites to information provided by cell phones. The latter has proven to be the best option, as most people now have a mobile device.

Although the movement of consumers is a topic that has concerned companies in several sectors for a long time, nowadays and with the support of Big Data tools together with the application of innovative methodologies, more accurate estimates can be made.

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Rodrigo Morales, Data Analyst at CentralAmericaData explains that one of the uses of this data is to build "... more accurate estimates on the amount of expected sales in a company that depends on the number of people around. It can also be used to find an optimal location for a business or to understand the patterns of consumers of a product."

For Morales "... the great added value of this type of information is that it tends to faithfully reflect the reality of how people move, which is very useful when making decisions or estimates, since the results tend to be more consistent and reliable. It is important to remember that this data by itself is not very useful, the results tend to be more consistent and reliable".

Visualization showing the location of consumers and the distribution of pedestrian flow at the point according to days of the week, hours of the day and socioeconomic level. 

Understanding the levels of concentration of people in certain locations and comparing them with others, interesting inferences can be made, such as estimating the sales of competing establishments.

The data analyst clarifies that "... this type of data does not affect anyone's privacy, as it is impossible to identify a specific person from the information used. All data is anonymous."

 Information from mobile devices showing concentration levels, based on consumer location.
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