Mobility solutions based on Big Data techniques have revolutionized the way luxury shopping malls understand the behavioral patterns of their visitors, profile their consumers, and make business decisions.
Leaders use these techniques to make more efficient and concise decisions that generate greater profitability by maximizing revenue and optimizing costs.
Case Study: POi Categorization of UB City Mall in Bangalore, India
At PREDIK Data-Driven we conducted a detailed study of UB City Mall the first luxury shopping mall in India, where we found brands such as Burberry, Canali, Coach, Diesel, Emporio Armani, among many others. 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 customers of the establishment as well as their relative wealth index characteristics.
By generating a heat map covering a one-kilometer radius around UB City Mall, the most prominent foot traffic areas where we find denser mobility concentrations are in a small part of Cubbonpet, Ganigarpert, Spangirama, Macivor Town, FM Cariappa Road, Ashok Nagar, and Mahatma Gandhi Road station, during the month of July 2021
These analyses help to generate more detailed information focused on consumers and markets in the area.
This type of analysis also allows to identify mobility concentrations in streets and avenues, this helps to identify bottlenecks in urban mobility or even to determine where to place the exit or entrance of a shopping mall.
How are visits distributed in N1 City?
The distribution by the hour, day, month, and year is an important segmentation when characterizing and analyzing a POI, since it allows: estimating the times and schedules of certain premises or locations and knowing when they are more and less crowded, estimating the average time that certain records are spent in or near specific areas, finding the times of day that are more and less favorable for implementing a product or service.
Which is the profile of the people around UB City Mall?
With a Global Relative Wealth Index data layer, collected by Facebook, the socioeconomic levels of the inhabitants and their status were estimated. In parallel, applying spatial data analysis techniques, corporate buildings, residential areas, schools, etc. are located; the combination of these two analyses allows classifying visitors as students, workers, residents, or “floating or instant population”, i.e. people who only transit through that area to reach another location.
What other locations are preferred by Starbucks visitors?
These analyses go into such detail that they allow us to identify and understand visitor behavior patterns and thus observe which are the potential markets, the places that generate the most interest, and the habitant’s preferences.