Predictive location, and foot traffic analytics reveal valuable information that helps retailers to choose the right location when setting up geographic expansion strategies.
How are predictive analytics models used to determine the optimal location for a new facility?
Predictive analytics is a type of statistical analysis that uses data mining, statistical modeling and machine learning to extrapolate trends from historical facts and current events and is often used for risk assessment and decision making.
Analytics through big data management techniques allows governments to understand the needs of their citizens, combat fraud, minimize system errors and improve operations, reducing costs and improving the services of any government entity.
Foot traffic analytics through geospatial data and Big Data enables governments and public sector organizations to deliver more efficient and secure services, as well as respond more quickly and accurately to the needs of customers and citizens.
In order to bring our operations, processes and services under one single brand name, we have decided to transform CentralAmericaData and definitively integrate it into our global brand, PREDIK Data-Driven.
We will continue to provide the same services to our clients in Central America and Mexico, but under the new brand name.
Location intelligence and foot traffic analytics are transforming the way retail strategies are defined, and department stores are no exception.
The correlation between foot traffic, sales, and the success of department stores 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.
With Big Data management techniques, companies can optimize their strategic business planning, by taking advantage of market and companies' data.
Big data has emerged as a powerful tool that organizations can use to leverage data-driven decision making for better strategic planning, determine which market niches of their products, are growing or shrinking, obtain traffic data of their stores or website, determining where they come from, what kind of devices they use, dwell time, and foot traffic patterns to help analyze which promotions and efforts are successfully driving their business.
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 order for retailers to stay ahead of digital competition, they must overcome cost and flexibility disadvantages; it's necessary to have real-time insight into what is happening inside and outside the point of sale.
Retailers must begin to take immediate action on unforeseen events at their physical points of sale, such as lack of inventory, shelf problems, environmental impacts, local events, loss of merchandise and customers, among many others, as they generate a significant loss for their business.
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.
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.
Predictive analytics has transformed the real estate industry due to its powerful ability to deliver fast and accurate actionable insights. This has largely come about through the advent of Big Data and Geographic Information Systems (GIS) that harness the intrinsic power of real estate data.
Predictive analytics tools take this analysis to the next level to predict future outcomes based on how past and present events occurred. Consumer demographics, housing trends and property price history are some of the areas where predictive analytics represent a huge opportunity for the industry.
Through information solutions based on the use of satellite photos, the application of classification models and the implementation of machine learning algorithms, it is possible to optimize the management of large plantations and minimize the risks faced by crops that affect profitability per hectare planted.
The growing availability of data that exists today is leading companies to seek new ways and tools to take advantage of this huge wave of information that is being generated in different business sectors.
With the technologies available it is possible to use satellite photos to detect types of surfaces and roofs, objects, land use and variance in farmland, and then analyze the results and transform them into useful data for business decision making.
In the past, it was possible to establish whether an area was industrial, commercial, residential or agricultural by analyzing aerial images, but today, with the use of high-resolution satellite images, more information can be obtained.
For companies, it is increasingly essential to support their strategies with artificial intelligence (AI) and "machine learning" tools, since these systems have the ability to suggest the best combinations of offers to ensure sales success.
For sales software used by companies, artificial intelligence (AI) and machine learning have become essential, because only in this way is it possible to successfully analyze the large volumes of information generated from information systems that record customer data.