Thanks to predictive modeling and artificial intelligence (AI), marketers can now leverage these tools to segment audiences beyond traditional parameters and build a more accurate profile of consumers.
By using AI, it is possible to segment audiences at more granular levels and identify which ones are most valuable to marketing objectives.
Retail home improvement franchises need to apply location intelligence techniques and foot traffic analytics to identify consumer mobility patterns, in order to maximize sales and generate more efficient expansion models.
The correlation between foot traffic, visits, sales, and the success of hardware home improvement franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Retailers can apply location intelligence techniques and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
The correlation between foot traffic visitation, sales, and the success of retail apparel franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.
Retailers are already implementing Big Data tools such as location intelligence and foot traffic analytics to understand consumer mobility patterns, measure foot traffic at each store, understand the performance of their outlets, and estimate competitor turnover.
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.
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.
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.
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:
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.
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.
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.
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.
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. This information is useful for increasing customer acquisition, profitability and long-term customer loyalty.
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.
Consumers today have access to information anytime, anywhere, including what, where and when to buy, how much to pay, among other things. This makes it increasingly important to use consumer-focused data analytics to predict how they will behave when interacting with brands.
The goal of consumer analytics is to create a single, accurate customer view to make strategic decisions about how to acquire, identify and retain them. The better the understanding of consumer buying patterns, the more accurate the prediction of consumer behavior and journey when purchasing a product or service.
Recognized Brazilian company of backhoe loaders, telescopic, articulated and other types of cranes looking for companies interested in representing the brand and distributing their machinery in Central America and Mexico. The company manufactures and sells telescopic,...
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