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.
To ensure the success of branch expansion strategies for site selection plans, it’s necessary to compare the business model with the the potential market needs and build the corresponding strategy, location and foot traffic analytics are the best tool to do so.
Foot traffic data gives retailers a competitive advantage in site selection by helping to visualize how local market dynamics have changed over a long period of time, uncovering new opportunities and insights in real time that would not necessarily have been evident through more traditional or one-dimensional data sources.
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.
The secret to site selection in such a competitive market is based on the ability to analyze the right data and be able to understand and interpret the site selection strategies of nearby locations to stay ahead of their expansion plans and gain a competitive advantage.
Where? This is the fundamental question that guides any site selection decision. Tools such as location intelligence and footfall analytics enable the aggregation, analysis and visualization of spatial data and bring significant advantages to the site selection process.
In order to optimize advertising costs and maximize revenue, marketing leaders are using spatial data to create geofences in specific areas, allowing them to reach audiences that are more likely to become potential customers.
What is geofencing marketing?
Geofencing marketing is based on location intelligence, which allows enterprises to connect with smartphone users in a designated geographic area through mobile applications. This tool consists of establishing virtual boundaries around a point or area of interest, which generates a trigger every time someone with a mobile device intersects with them. When this happens, a notification announcing a store, brand, service, or product is sent to that person’s mobile device. Read the full article here
Location analytics is taking its place as a key tool for identifying what consumers want and need, regardless of their wealth or demographic status. COVID-19 has led to completely unexpected behavioral changes.
How can location intelligence help in the recovery of any type of business?
COVID-19 restrictions have caused many owners to halt expansion plans, limit their operational capacity or even close their doors for good. As the world begins the transition to a post-pandemic society, retailers face unprecedented levels of uncertainty.
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.
Location intelligence is a key tool that shopping center managers should use since, via people’s location and mobility data, they gain valuable insights such as how much time consumers spend in stores and how often they visit.
Shopping centers can use this technology to collect geospatial data sets, transaction history, and point-of-sale data, as well as other business processes for in-depth geographic analysis by providing demographic data on adjacent businesses, including competitors.
Matching demand and supply is the basis of the business model of any company whose operations depend on micro-mobility, since for every unit of demand that is not satisfied, an order is lost, leading to loss of profits and customer loyalty.
All companies that rely on micro-mobility can better manage their assets by improving their algorithms with location intelligence and foot traffic analytics, identifying demand peaks or drops beyond the average value in order to foresee or solve any kind of unexpected problem and generate solutions based on Big Data. Mobile tracking helps to know what is happening over any terrain and teaches how to be proactive about it.
Supermarkets can apply location intelligence techniques and footfall analytics to understand consumer mobility patterns, generate efficient site selection strategies, understand the performance of their stores, and estimate competitor turnover.
The correlation between foot traffic visitation, sales, and the success of retail supermarkets have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion strategies.
Footfall analytics and POI characterization through Big Data are being used by different business sectors in order to make smarter business decisions and thereby maximize their revenues and optimize their costs.
Case Study: POI analysis of N1 City in Cape Town, South Africa
Foot traffic analytics and point-of-interest analytics help large chains supplying contractor-grade building materials and home improvement products measure footfall and understand consumer behavior patterns in any given zone of interest or point of sale.
Case Study
Foot traffic analytics: Builders Warehouse City Cape Town, South Africa
Footfall analytics have revolutionized the way retailers implement site selection, commercial and operational strategies in the quick-service restaurant franchise market.
The correlation between location and footfall analytics, visits, sales, and the success of retail fast-food 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 strategies.
POI characterization through Big Data has become more frequent as it allows the implementation of strategies and site selection solutions within the various business sectors.
Leaders use these techniques to make more efficient and concise decisions that generate greater profitability by maximizing revenues and optimizing costs.
Case Study: Visitor profiling at Perisur, one of the most exclusive shopping malls in Mexico City.
Identifying mobility patterns and classifying consumers within a point of sale or areas of interest helps large retail fast fo measure foot traffic in and out of their stores while understanding the behavioral patterns of their consumers.
The correlation between location and footfall analytics, visits, sales, and the success of retail fast-food 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 strategies.