Foot Traffic analytics can identify the characteristics of transit stops and routes, helping to determine and improve the overall coverage of any transit system.
When it comes to public transport, foot traffic analytics can be of great help to authorities, who are faced with various challenges such as road congestion and the diversity of modes of transport, among others. This poses difficulties for public transport managers and operators when it comes to planning.
By incorporating location intelligence into urban planning, it becomes possible to develop infrastructure adapted to the needs of citizens, enhancing living conditions in any given city. In addition, spatial data helps to optimize costs and prioritize government administration projects.
What does location intelligence provide to urban planning?
Micro-mobility analytics improve retailers’ expansion strategies by accurately identifying consumer demographics, understanding customer behavior, and understanding how their competitors are performing.
Micro-mobility is a methodology that combines geospatial data and foot-traffic analytics to solve several problems and improve site selection strategies by helping to understand how people move around specific brick n’ mortar locations, allowing companies to analyze movement patterns around specific locations, such as retail stores, to extract meaningful information.
Heat maps are used by any business sector to identify foot traffic and vehicular mobility patterns in an area or point of interest, as their visualization presents multiple pieces of data in a way that makes immediate sense.
Heat maps can be used to identify foot traffic patterns from a country-level scale to a more detailed level such as the infrastructure of a store or building.
The heat map is one of the most useful and powerful data analysis tools in business intelligence. It provides a visualization feature that presents multiple pieces of data in a way that makes immediate sense by assigning a different size and color.
New geographic information systems (GIS) allow us to see our complex world in a new way. With the right data sets, heat maps can be produced that show how different factors vary from one area to another. In recent years, several organizations have developed dynamic mapping systems that allow users to choose areas, data layers, filters, and presentation features. Read the complete article here
Several companies, especially in the retail sector, have realized that they need to challenge traditional last-mile delivery solutions in light of recent advances in technology tools.
With the help of location intelligence that gives access to aerial, satellite imagery and high-definition maps, last-mile delivery processes can evolve to the degree that drivers can avoid traffic and expedite deliveries through predictive alerts, fleet managers can focus on planning delivery routes more efficiently based on fuel costs, travel time, road tolls, etc.
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.
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.
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.
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