Analytics based on Big Data allows mall operators to maximize revenues and visits by better selecting tenants, optimizing mall design, determining rents, establishing signage and advertising campaigns, etc.
New technological tools allow mall operators to measure the number of consumers spending in and out of stores, the time they spend in and out of stores, know their relative wealth index and understand visitor behavior patterns, helping to determine the best mix of stores, site infrastructure, rent price range and implement more efficient signage and advertising.
The POI characterization 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 POI 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.
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.
Mobility analytics and location intelligence allow you to select the ideal location for opening a restaurant or coffee shop.
Have you ever wondered what your favorite coffee shop does to offer you a unique experience?
With location intelligence solutions it's possible to determine one or more areas where you want to establish a new coffee shop, while mobility analytics identifies all the factors that can be used to maintain and increase its success.
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 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.
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.
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.
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.