This tailor-made dataset uses foot traffic data combined with predictive models to analyze and predict the behavior of customers inside and outside points of interest, in order to identify the ideal location for the opening of future stores. This tailor-made dataset helps businesses to optimize their site selection strategies with methodologies 100% based on foot traffic analytics and predictive modeling. You will be able to able to determine the ideal areas for the opening of new stores, reducing the investment risks by estimating the expected annual sales in each of the locations derived from the results of the model. We gather all relevant information regarding the area or city of interest: With 100% data-driven techniques, we collect and analyze information such as: floating population and its sociodemographic characteristics, mobility trends and patterns (visitors trends by days, hours, at a street level), competitors´ presence, traffic mobility, consumers' expenditure in the product or service of interest, among others. Using machine learning techniques, we combine and analyze all the variables to calculate a suitability index that will rank the different areas within the city. The result is a ranking of the most suitable locations in the city or area of interest of the client. Use Cases -States, cities and countries heat maps, where you can identify the concentrations of pedestrian and vehicular traffic in any area of interest, and with our data layer of social network people behavior, you'll be able to categorize the transient population by age, gender and purchasing power. -Overview of the potential resident customers in the points of interest. -Estimate the average annual, monthly and per capita expenditures of the population related to any product or service. -Identify which types of products or service are most commonly acquired and from where they're acquired. -Through web swapping, obtain a segmentation of properties by construction area and average price per square meter. -Distinguish the price of the available properties according to their sale and rental price. -Identify the location of competitors and avoid brand cannibalization. -Learn more about your own and the competitors´ points of sale, and identify the mobility and consumption patterns of visitors. -Obtain the potential revenue of any point of sale.