The characterization of zones through Big Data has become more frequent as it allows the implementation of strategies and solutions within the business sectors.
The variety of solutions based on location intelligence and zone characterization rely on efficient POI analysis, which benefits any type of business.
Location intelligence and zone characterization through Big Data are increasingly being used to make business decisions in the retail, real estate, logistics, and port sectors, among others.
Location and Footfall Analytics has changed the way retailers implement marketing and commercial strategies in the fast-food restaurant franchise business.
Understanding consumers’ mobility behavioral patterns are critical for all types of restaurants. Big Data tools and spatial data play a very important role in these analyses since they make it possible to measure the foot traffic and mobility patterns of any area or point of interest. By performing these studies, businesses understand and predict the performance of their stores, as well as estimate the competitors’ turnover or identify areas for expansion planning. (site selection)
Big Data together with mobility analytics and location intelligence techniques allow increasingly accurate estimates of the levels of visits received by points of sale, revealing geographic patterns of brand loyalty and market penetration.
Get to know the competition and how they behave in the market is now possible, thanks to technological tools that provide an overview of mobile device activity associated with brand locations, helping to visualize a detailed picture of consumer engagement, brand loyalty, and market share.
Consumers are shifting their spending from physical stores to e-commerce, physical stores will only survive in this new environment if they reinvent their business, taking advantage of new technologies and modern analytical capabilities.
Today, there is access to significant amounts of data on consumer behavior, information on the economy of different areas, competitors' sales, and market trends. However, only a handful of forward-thinking retailers are leading the way in advanced analytics as they use location intelligence, foot traffic analytics, and predictive modeling to make smarter business decisions.
Business intelligence and technology tools enable retail businesses to deeply analyze the behavior of their customers inside and outside their stores, in order to identify the ideal location for future expansion plans.
The data science models are the third phase of location and mobility intelligence analysis, helping retailers to assess the market potential of areas of interest for new store openings and identify optimal potential locations for expansion. These are divided into three steps:
Data science now generates strategic solutions that help retailers identify optimal potential locations for expansion by assessing the market potential of areas of interest for new store openings.
Applying geomarketing techniques and location intelligence based on spatial data, retailers can perform analyses that identify areas or points of interest by analyzing the concentration of people, identifying the most and least crowded places, the times and days of peak pedestrian and vehicular traffic, and defining the shopping profiles of consumers in areas of interest.
Location intelligence and mobility analytics allow companies to create Out-of-Home advertising strategies focused 100% on the tastes and preferences of the people who pass through each point.
In outdoor advertising (OOH), knowing in depth the consumers who pass through the point where the advertising will be exposed is crucial to maximizing as much as possible the return on investment.
Footfall analytics helps to make critical operational and strategic decisions for any type of business, improving conversion rates, maximizing sales, optimizing costs and increasing brand positioning.
Thanks to mobility data, retailers can get a deeper insight into their business by analyzing changes in sales volumes and the consequences of fluctuations in footfall levels inside and outside stores. At the same time, they can measure the effectiveness of marketing campaigns, providing a clearer picture of what really works for a target audience.
The COVID-19 pandemic has exponentially accelerated the growth of e-commerce, but physical stores are still far from disappearing.
Today, retail businesses have accelerated their digitalization strategies by evolving omnichannel processes, trying to maintain their efficiency and generate added value without affecting their operations, as consumers' needs to interact virtually with the different points of sale have increased.
In order to identify the optimal place to establish a new business, for example, an educational center, it's vital to resort to geolocation analytics and mobility analysis.
Through location intelligence solutions, it's possible to determine one or more areas where you want to establish an educational center, while mobility analysis and behavioral patterns can identify the factors that can maximize its success.
The clothing store market is one of the most popular in the USA, with Big data analytics and location intelligence techniques we made an brief exploratory analysis in Atlanta city.
Case Study: Analysis of Apparel Stores in Atlanta, USA
Using location intelligence techniques it was possible to identify all the stores distributed in the city, the detailed analysis allowed us to classify the venues by market share and identify in which areas of the city they are located. Our research showed that the women’s clothing category is the one with the largest number of stores in the center of Atlanta, while the other categories are distributed in the surrounding areas.
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.
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