Pharmaceutical companies need to apply location intelligence techniques and foot traffic analytics to identify consumer mobility patterns, in order to maximize sales and generate more efficient expansion models.
The correlation between foot traffic, sales, and the success of pharmacy franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Retailers can apply location intelligence techniques and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
The correlation between foot traffic visitation, sales, and the success of retail apparel franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
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.
In the digital era, data is being created at a speed never seen before, and its proper application in business intelligence is already generating incalculable value for businesses
It seems irrational to suggest that a concept like data could be more valuable than an established and indispensable product like oil, but as the years have gone by, this already seems like a reality.
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.
Data has proven to be a competitive differentiator in different business sectors. The performance of any organization is highly correlated to the maturity of its data, so it's important to know in which level you are in this process.
What is Data Maturity?
Data maturity is a measure used to determine where a company's data quality, structure, distribution, security, and analytics are in their progress.
More and more retail organizations are monetizing their data to increase revenue, boost productivity and optimize costs, enabling effective leveraging of assets, technology tools and external information to generate better results.
In the retail sector, data monetization is about making better informed decisions, increasing revenue and reducing costs from access to different types of stored, categorized and accessible data.
Through location analytics, it's possible to identify a place of interest and establish its exact location, helping companies to understand what's happening around a specific place to make better strategic decisions.
Any business sector can leverage location analytics based on points of interest (POI) in a convenient way to characterize and analyze points of sale(POS), bringing special value to the decision making and strategy implementation of any market.
Many decision makers in the construction industry don't know what to do with the vast amount of data they have available, as they don't have the right tools to analyze it in a way that will have a meaningful impact on their projects.
Predictive modeling, Big Data analytics, machine learning and artificial intelligence unlock the ability to leverage the data coming from projects to organize and interpret it and thereby discover patterns more quickly. These tools reduce problems, lower costs and mitigate the risk of different processes in construction projects by making the work more predictable and efficient.
Geolocation systems and Big Data turn data into information that farmers and land managers can use to make more informed and timely decisions to increase productivity.
Sensors located in fields, tractors and on satellites high above farms are constantly collecting data.
Geolocation systems and Big Data are able to convert this data into information that agricultural companies can use to make more informed and timely decisions, increasing productivity, allowing to collect information on soil and plant needs to apply treatments that increase agricultural production, managing costly resources such as fertilizers, pesticides and herbicides.
Geospatial construction, or "geo-construction", uses data from a wide range of focal points, such as location, population and environment, to influence the design and construction of any infrastructure in order to save, time, money and reduce waste.
GIS (Geographic Information Systems) improve the effectiveness of construction planning and design by integrating location information into a single environment.
Location choice has become a critical point in the success or failure of any industry, as it has a great impact on the company's overall risk, as well as on transportation, logistics, salaries, rents and raw materials costs, among others.
Where to locate industrial facilities is one of the most important strategic decisions that companies must make. Identifying the optimal location is a spatial problem that requires the comparison of attributes of different locations that have the best combination of the desired variables and qualities.
Big data has currently become one of the newest allies to infer relationships between companies, competitors and suppliers, since the analysis of large volumes of data can reveal missing or relational aspects between different sectors within organizations, where they can complement each other.
The set of networks in which companies participate are considered as a resource for them.
What makes Big Data so useful for many business sectors is the fact that it provides answers to many questions unknown even to the stakeholders themselves. In other words, it provides a more accurate point of reference.
Big data is a term that refers to such large, fast and complex amounts of information that it is difficult or nearly impossible to process them with traditional methods.
MIDA and ETESA signed an agreement for the creation of a National Agroclimatic Data and Statistics System, which will be fed with data from agro-meteorological, meteorological and hydrological stations.
This agreement, which was signed by the rector of the agricultural sector, Augusto Valderrama, and the general manager of Empresa de Transmision Electrica, S.A.