How to Measure Pedestrian Flow with Mobility Data?

Through solutions based on advanced mobility data analytics and predictive models, it is possible to identify different types of patterns, needs, strategies or even future consumer trends.

There are hundreds of solutions and analyses that can be performed thanks to mobility data, such as forecasting models, tracking and predictive market models, business intelligence, real estate project evaluations, solutions based on geomarketing probabilistic models, among others.

Obtaining all kinds of data to channel and transform them into useful information to improve decision making has become vital for companies. What would be the use of knowing the flow of pedestrians or vehicles in streets, commercial or industrial areas, if predictive models and other analysis tools are not used to extract valuable insights for decision making?

You may be interested in: “Geospatial data to optimize supply chains.”

Solutions generated from mobility data

Analyzing final consumer behavior through their movement on the streets is just one of the many things that can be done with mobility data, in order to obtain different types of information about consumer behavior and come up with interesting findings. For example: Why do customers concentrate in certain stores?

Using mobility data, it is possible to perform origin-destination analysis, which allows inferring, among other things, consumer behavior patterns, such as from which areas of the city they come from, how often they come, and where they move to after spending some time in a certain place, such as a shopping mall.

Estimates can also be made of the number of people who visited a place, helping any business to make strategic decisions focused on its objectives by identifying behavioral patterns related to the influx of mobility in a specific area.

“An analysis carried out by PREDIK Data-Driven, on mobility patterns in a shopping mall in the city of Lima, Peru, is appreciated.”

Also read: “Mobility data and its value for the retail sector“.

In the logistics sector, for example, large commercial brands can record visits in different places throughout the day, to define relationships between different geographical points, and thus identify economic relationships between suppliers, competitors, and others. With mobility data it is possible to answer, among other things, which and how many of my competitors could be working at the same time with my client?

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