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
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.
Heat maps are used by any business sector to identify foot traffic and vehicular mobility patterns in an area or point of interest, as their visualization presents multiple pieces of data in a way that makes immediate sense.
Heat maps can be used to identify foot traffic patterns from a country-level scale to a more detailed level such as the infrastructure of a store or building.
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.
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.
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 modeling.
Heat maps are used by retail giants to understand pedestrian and vehicular mobility patterns and thereby optimize their strategies for opening new points of sale.
At PREDIK Data-Drivenwe support global corporations in implementing a variety of expansion planning solutions.
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.
Pet shop owners 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 pet shops 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.
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.
The heat map is one of the most useful and powerful data analysis tools in business intelligence. It provides a visualization feature that presents multiple pieces of data in a way that makes immediate sense by assigning a different size and color.
New geographic information systems (GIS) allow us to see our complex world in a new way. With the right data sets, heat maps can be produced that show how different factors vary from one area to another. In recent years, several organizations have developed dynamic mapping systems that allow users to choose areas, data layers, filters, and presentation features. Read the complete article here