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.
Location Analytics has changed the way marketing and commercial strategies are defined in the fast-food restaurant franchise business.
Understanding consumers’ behavior patterns is critical for all types of restaurants. Big Data tools play a very important role in this analysis, since they make possible to measure the foot traffic and mobility patterns of a specific area or location, among other variables.
Customer behavior analysis it's important because it helps businesses to understand how users interact with a brand, now it can be done in a more objective and real way using Big Data management techniques
Customer behavioral analytics is the process of collecting and analyzing data, using technology tools such as Big Data, machine learning and location intelligence, help gain long-term insights of the average purchase value, customer lifetime value and users interaction with a brand, enabling companies to incorporate data-driven business strategies that facilitate decision making and revenue maximization.
Through data analytics it's possible to improve vehicle performance, reduce costs, improve processes, establish strategies, optimize routes and times, and foresee and identify problems, among others.
Transportation analytics takes a variety of data ecosystems, helping industry leaders to use advanced analytical techniques such as machine learning, Big Data and geospatial data to optimize business strategies in the sector.
Foot traffic data consists of spatial data (GIS), and is at the core of building intelligent strategies, transportation routes, processes and decision making in both public and private sectors.
What is it?
Foot traffic data associates people's movements with physical locations, and can be collected in different ways, such as WiFi signals, GPS from mobile devices and sensors, providing useful information for sectors like retail, real estate, agriculture, financial services, insurance, tourism, sports, entertainment, among others.
Unlike historical analytics, predictive supply chain analytics allows you to anticipate and prepare for the future, taking out the conjectures planning processes and improving decision making.
Predictive supply chain analytics use advanced technological tools such as machine learning, geomarketing, data mining that enables organizations to identify hidden patterns, understand market trends, identify demand, establish pricing strategies, achieve a high return on investment, optimize and reduce inventory costs.
More and more retail organizations are monetizing their data to increase revenue, boost productivity and optimize costs, enabling effective leveraging of assets, technology tools and external information to generate better results.
In the retail sector, data monetization is about making better informed decisions, increasing revenue and reducing costs from access to different types of stored, categorized and accessible data.
Through location analytics, it's possible to identify a place of interest and establish its exact location, helping companies to understand what's happening around a specific place to make better strategic decisions.
Any business sector can leverage location analytics based on points of interest (POI) in a convenient way to characterize and analyze points of sale(POS), bringing special value to the decision making and strategy implementation of any market.
A competitive analysis begins by defining the target and its scope, exploiting different sets of structured and unstructured data available thanks to innovative tools that help to identify and measure competing brands and estimate their sales.
With Big Data tools that help analyze large volumes of information like predictive models,location intelligence and mobility data, it's possible to generate a competitive analysis, which consists of identifying the main competing companies and estimating their turnover, quantifying their potential sales, identifying gaps in the market, predicting the cost of developing new products, discovering trends, among others.
Customer dwell time is an analysis that should be closely monitored to capitalize on the full potential of each point of sale, indicating greater customer satisfaction.
Estimating and improving the average customer dwell time inside physical stores is possible thanks to technological tools such as Big Data, location intelligence analysis and mobility data, which improve strategic decision making by helping to increase the time consumers spend inside stores, and increasing the sales conversion rate as well.
Mobility data analytics are transforming the way commercial strategies are defined in the retail business, and supermarket chains are no exception.
Understanding what consumers think, what they want and what they do is critical for companies in the retail sector. This is where Big Data tools play an important role, as it is possible to measure the affluence at a location and customer behavior, among other aspects.
New data management methodologies now allow retailers to take advantage of even the smallest piece of information to generate valuable insights that help optimize their marketing and customer loyalty strategies.
What promotions do we do to get more customers to the point of sale?
How do we make them stay longer in the store?
How do we improve the customer experience so that they buy more at each visit?