Big Data technological tools and spatial data play a very important role in business by measuring footfall and helping to understand consumer behavior patterns in any given area of interest or point of sale.
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.
Understanding consumer mobility patterns is critical for all types of businesses. Big Data tools, spatial data, and anonymized and aggregated mobility data play a very important role in these analyses, as they enable the measurement of foot traffic and consumer behavior patterns in any given area or point of interest.
Foot traffic analytics, location intelligence, and point-of-sale categorization have revolutionized the way retailers implement expansion models, commercial and operational strategies in the supermarket franchise focused on more organically sourced products.
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 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.
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.
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.
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?
Customer analytics along with Big Data tools, predictive modeling and geospatial data are used to understand consumer needs, analyze price sensitivity, and the general behavioral patterns that customers follow when choosing products or services.
The analysis of customer information offers great advantages in strategy development, as customer interactions, consumer response and behavioral patterns, among others, can be monitored and predicted.
Retail companies are already implementing Big Data and geolocation analytics tools to understand consumer mobility patterns, measure foot traffic in each store, understand the performance at their points of sale and estimate competitors’ turnover.
Big Data techniques allow the recollection of large volumes of geospatial and anonymous data from various mobile devices such as cell phones, computers, tablets, etc., making possible to generate different detailed and general analysis that help to solve any kind of business problems in any specific sector.
Geolocation intelligence is accurate contextual information about the location and movement of people anywhere in the world.
The potential that this type of data has in the context of business decision making in the retail sector is invaluable. It includes demographic information about the retail outlets themselves, nearby businesses, competitor insights, customers, suppliers, among others.
More and more companies are turning to predictive analytics to optimize their processes, achieve better business results and increase their market share.
Organizations use internal predictive analytics to forecast trends, understand and predict customer behavior, improve performance and drive strategic decision making.
With the boom in data mining and the use of algorithms to make recommendations to customers, companies are beginning to face the decision of whether to provide "human" assistance or through bots.
At a global level, several companies have opted to develop their data analysis departments, with the aim of finding suitable information to develop models that automatically make recommendations to their customers.