Foot traffic data consists of spatial data (GIS), and is at the core of building intelligent strategies, transportation routes, processes and decision making in both public and private sectors.
What is it?
Foot traffic data associates people's movements with physical locations, and can be collected in different ways, such as WiFi signals, GPS from mobile devices and sensors, providing useful information for sectors like retail, real estate, agriculture, financial services, insurance, tourism, sports, entertainment, among others.
A competitive analysis begins by defining the target and its scope, exploiting different sets of structured and unstructured data available thanks to innovative tools that help to identify and measure competing brands and estimate their sales.
With Big Data tools that help analyze large volumes of information like predictive models,location intelligence and mobility data, it's possible to generate a competitive analysis, which consists of identifying the main competing companies and estimating their turnover, quantifying their potential sales, identifying gaps in the market, predicting the cost of developing new products, discovering trends, among others.
Mobility data analytics are transforming the way commercial strategies are defined in the retail business, and supermarket chains are no exception.
Understanding what consumers think, what they want and what they do is critical for companies in the retail sector. This is where Big Data tools play an important role, as it is possible to measure the affluence at a location and customer behavior, among other aspects. With this information, it is possible to model the performance of points of sale and estimate the turnover of competitors or potential locations.
The analysis of mobile and geospatial data focused on points of interest has become vital for the efficiency of any business, as it allows to understand some important aspects that retailers need to ensure the success of their establishments.
In the digital age, location intelligence and foot traffic analytics based on mobility data are changing the retail business, giving many retailers an edge over their competitors.
Location intelligence is defined as a methodology for understanding and visualizing mobility data to help solve a wide variety of retail problems.
Foot traffic analytics is derived from location intelligence, helping retailers to obtain a better understanding about how people move around physical locations and enabling new visibility into consumer behavior patterns by presenting a visualization of people's movement within a physical area, even segmented by the infinite type of consumers who pass near a store.
Knowing and understanding the geographic distribution of points of sale in the retail channel can provide retailers with new opportunities to improve the promotion and marketing of their products and better influence consumers when making purchasing decisions.
When we talk about point of sale (POS), we refer to the place where a customer transacts payment for goods or services.
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.
Through geospatial data analysis techniques, CentralAmericaData carried out an analysis of five Walmart distribution centers in Florida, United States, with the aim of identifying patterns in the supply chains of these five centers and their relationships with commercial establishments and other logistics complexes in the State.
Through this analysis, whose objective is to show how geospatial data science techniques can be applied to solve problems in the logistics sector, the existing relationships between Walmartdistribution centers and their supply sites were identified and characterized, so that different large commercial chains can evaluate and at the same time improve processes in their respective supply chains.
By analyzing the large volumes of anonymous data generated by mobile devices, it is possible to establish whether a distribution center has a commercial relationship with other logistics complexes, and even with establishments that serve the end consumer.
Using the most advanced Big Data tools, it is possible to understand the behavior of the supply chains of companies in the retail sector, since by monitoring delivery parts and counting mutual visits between suppliers and vendors, it is possible to identify and establish which are the most important relationships between distribution centers and points of sale to the end consumer, such as stores.
Thanks to advanced Big Data techniques that make it possible to collect and analyze large volumes of mobility data, it is possible to establish where consumers live and where they go before visiting a shopping mall or supermarket.
Today, business leaders have access to Business Intelligence solutions that are based on millions of anonymized data generated every second by cell phones, records that allow increasingly accurate estimates of the levels of affluence received by commercial establishments.
Analyzing the number of consumers who visit the establishments of any retail company, establishing the days and hours of greatest affluence and comparing it with competing sales points, is possible with Big Data techniques that allow the collection and analysis of large volumes of mobility data.
The millions of anonymized data generated every second by cell phones in all markets around the world make it possible to make increasingly accurate estimates of the levels of customer traffic received by commercial establishments.
Through the management of large volumes of data, it is possible to understand the pedestrian mobility of an area or city, and from this, predict the sales potential of a business according to its location and the characteristics of consumers who pass through the surrounding area.
Currently, companies in the retail and mass consumption sector that have opted to develop "data driven" work teams have found in Business Intelligence and Geomarketing solutions a new way to understand the dynamics of their business models.
In the last week of May 2021, El Salvador, Nicaragua, Honduras, Dominican Republic and Guatemala were the economies in which the number of people visiting establishments identified as supermarkets was considerably higher than the figures reported before the pandemic.
In the first five months of the year, and in the context of the reactivation of commercial activities, more Central American consumers have visited locations identified as supermarkets and pharmacies.
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.
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.
Whether it is a restaurant, a coffee shop, a hotel, a supermarket, or an auto parts store, location is, if not the most important, one of the most decisive factors in determining the success or failure of a business.
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