Retail store expansion strategies are one of the most fundamental issues for growing retailers. Opening a new store can be a game changer if you get the location right, or your new store could be doomed to failure if the location doesn’t attract enough customers.
In addition to geographical factors, such as transportation accessibility and real estate prices, demographic factors and mobility patterns in the areas of interest play a key role in decision making. These data on population, purchasing power and consumption habits are what generate an optimal expansion strategy.
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.
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.
Retailers need to use predictive modeling and combine it with business, customer, and market data in order to forecast sales or new businesses profitability.
Predictive modelingis a highly effective way to identify a suitable area for expansion strategies, as it has become a vital tool for narrowing down hundreds of potential sites or even a single site. When a potential site is identified, it’s combined with additional layers of data to determine if it meets the model’s requirements, sales performance targets, and other critical factors for any business requirements. 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.
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
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.
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.
Footfall analytics and POI characterization through Big Data are being used by retail industry leaders to make smarter business decisions, by maximizing revenue and optimizing costs.
Case Study: Footfall analysis of the largest Starbucks Coffe in Bangalore India
At PREDIK Data-Driven we conducted a detailed study of Starbucks Coffe, the largest physical store in Bangalore, India. Through a footfall analysis and POI characterization of the area and people who visited that establishment, we estimated the foot traffic, identified the most and least crowded places, classified the users who visit, live or work in the area, the observations per day, the most and least visited places by the establishment’s customers as well as their relative wealth index characteristics.
Organizations rely on the effective use of big data and geomarketing techniques as the backbone of all their processes and decision making, reducing the risks of new investments associated with expansion plans and new store openings.
At PREDIK Data-Driven we support global corporations to optimize their expansion strategies with 100% data-driven methodologies.
Identifying mobility patterns and classifying consumers within a point of sale or areas of interest helps large retail supermarkets measure foot traffic in and out of their stores while understanding the behavioral patterns of their consumers.
The correlation between foot traffic, visits, sales, and the success of retail supermarkets has been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Location intelligence and footfall analytics can be valuable tools for wholesale distributors to maximize their revenue, optimize their processes and choose the best distribution routes for their pickup and last-mile delivery processes.
The correlation between foot traffic patterns, visitation, sales, and the success of wholesale food distribution companies has been studied and proven, so the development of this type of analysis has become a priority in the process of site selection, supply chain process optimization, and expansion modeling.
Retail home improvement franchises 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, visits, sales, and the success of hardware home improvement 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.