Mobility analytics and location intelligence play an important role in business by helping to understand the behavioral patterns of consumers in any given zone of interest or point of sale.
Pedestrian flow analysis, location intelligence and point-of-interest categorization have revolutionized the way retail and wholesale industries implement expansion, commercial and operational strategies in the pharmaceutical sector.
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.
Analytics based on Big Data allows mall operators to maximize revenues and visits by better selecting tenants, optimizing mall design, determining rents, establishing signage and advertising campaigns, etc.
New technological tools allow mall operators to measure the number of consumers spending in and out of stores, the time they spend in and out of stores, know their relative wealth index and understand visitor behavior patterns, helping to determine the best mix of stores, site infrastructure, rent price range and implement more efficient signage and advertising.
The POI characterization through Big Data has become more frequent as it allows the implementation of strategies and solutions within the business sectors.
The variety of solutions based on location intelligence and zone characterization rely on efficient POI analysis, which benefits any type of business.
Location intelligence and POI characterization through Big Data are increasingly being used to make business decisions in the retail, real estate, logistics, and port sectors, among others.
Location intelligence is revolutionizing the way companies establish, operate and expand their business.
From deciding where to locate a new store to analyzing foot traffic to gauge market competition, the use of location data is growing.
One of the key components of location intelligence is data related to points of interest (POIs). POIs indicate a specific location in an area of interest to businesses: it can be a store, a hospital, a university, or a corporate building, among others, depending on the information required for the particular target, helping companies make faster, more informed and cost-effective decisions.
Big Data together with mobility analytics and location intelligence techniques allow increasingly accurate estimates of the levels of visits received by points of sale, revealing geographic patterns of brand loyalty and market penetration.
Get to know the competition and how they behave in the market is now possible, thanks to technological tools that provide an overview of mobile device activity associated with brand locations, helping to visualize a detailed picture of consumer engagement, brand loyalty, and market share.
COVID-19 and climate change have directly impacted the supply chains of the sectors and industries that generate the most economic output.
Unfortunately, fiction has become reality, and a global pandemic coupled with sudden climate changes have increased these problems worldwide, also due to unforeseen events in logistics routes and the exponential increase in online shopping, forcing industries to increase the load of transportation, vehicles, staff and resources in general.
Location intelligence and mobility analytics allow companies to create Out-of-Home advertising strategies focused 100% on the tastes and preferences of the people who pass through each point.
In outdoor advertising (OOH), knowing in depth the consumers who pass through the point where the advertising will be exposed is crucial to maximizing as much as possible the return on investment.
Location Analytics has changed the way marketing and commercial strategies are defined in the fast-food restaurant franchise business.
Understanding consumers’ behavior patterns is critical for all types of restaurants. Big Data tools play a very important role in this analysis, since they make possible to measure the foot traffic and mobility patterns of a specific area or location, among other variables. With these analyses, businesses can understand and predict the performance of their stores, as well as estimate the competitors’ turnover or identify areas for the opening of new locations. (site selection).
Customer behavior analysis it's important because it helps businesses to understand how users interact with a brand, now it can be done in a more objective and real way using Big Data management techniques
Customer behavioral analytics is the process of collecting and analyzing data, using technology tools such as Big Data, machine learning and location intelligence, help gain long-term insights of the average purchase value, customer lifetime value and users interaction with a brand, enabling companies to incorporate data-driven business strategies that facilitate decision making and revenue maximization.
Through data analytics it's possible to improve vehicle performance, reduce costs, improve processes, establish strategies, optimize routes and times, and foresee and identify problems, among others.
Transportation analytics takes a variety of data ecosystems, helping industry leaders to use advanced analytical techniques such as machine learning, Big Data and geospatial data to optimize business strategies in the sector.
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.
Unlike historical analytics, predictive supply chain analytics allows you to anticipate and prepare for the future, taking out the conjectures planning processes and improving decision making.
Predictive supply chain analytics use advanced technological tools such as machine learning, geomarketing, data mining that enables organizations to identify hidden patterns, understand market trends, identify demand, establish pricing strategies, achieve a high return on investment, optimize and reduce inventory costs.
O4Bi is a system that allows to control and manage what a company needs: the complete process of development of works, accounts receivable, treasury, banks, sales and accounting.
O4Bi is a very robust system that allows to control and...
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