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.
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.
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.
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:
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.
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.
Thanks to advanced Big Data techniques that make it possible to collect and analyze large volumes of mobility data, it is possible to establish where consumers live and where they go before visiting a shopping mall or supermarket.
Today, business leaders have access to Business Intelligence solutions that are based on millions of anonymized data generated every second by cell phones, records that allow increasingly accurate estimates of the levels of affluence received by commercial establishments.
Analyzing the number of consumers who visit the establishments of any retail company, establishing the days and hours of greatest affluence and comparing it with competing sales points, is possible with Big Data techniques that allow the collection and analysis of large volumes of mobility data.
The millions of anonymized data generated every second by cell phones in all markets around the world make it possible to make increasingly accurate estimates of the levels of customer traffic received by commercial establishments.
During the first half of 2021, consumers in Central American countries increased their interest in comics, martial arts, motorcycles, vegetarian food, spa services, air travel, weight loss products and sporting goods.
Through a system that monitors in real time changes in consumer interests and preferences in Central American countries, developed by CentralAmericaData, it is possible to project short- and long-term demand trends for the different products, sectors and markets operating in the region.
During the first quarter of 2021, consumption of household cleaning products increased in five of the six Central American markets, with Honduras and Panama reporting the highest year-on-year variation rates.
Data revealed by Kantar Worldpanel Division highlights that between January and March 2020 and the same period of 2021, consumption of indulgence and cleaning products increased 28% in Honduras, 17% in Panama, 13% in Nicaragua, 6% in Guatemala and 3% in El Salvador.
In the last week of May 2021, El Salvador, Nicaragua, Honduras, Dominican Republic and Guatemala were the economies in which the number of people visiting establishments identified as supermarkets was considerably higher than the figures reported before the pandemic.
In the first five months of the year, and in the context of the reactivation of commercial activities, more Central American consumers have visited locations identified as supermarkets and pharmacies.
Using today's technology, it is possible to know and accurately monitor consumer mobility, identify the places they visit, how often they do so, at what times and on what days, and transform this mobility and pedestrian flow data into solutions for optimizing commercial and marketing strategies.
People mobility is a concept that covers much more than just movement.
Using big data management techniques, it is possible to know, with greater precision than with traditional methods, the socio-demographic characteristics, tastes, preferences and interests of consumers living in a specific area of a city or of groups of people who visit particular stores.
Nowadays, with the large volumes of data that exist, it is possible to examine absolute and relative numbers of potential customers of a shopping center or business that are in any other location.
It is estimated that in Central America close to one million people express interest in soups in the digital environment, being Ramen, Curry and Maggi, some of the terms most associated by consumers with high purchasing power with the subject.
An analysis of the interests and preferences of consumers in Central America, prepared by the Business Intelligence Area of CentralAmericaData, yields interesting results on the preferences and tastes of people in various foods, products, services and activities.