Financial Data
Financial data is key in understanding consumer behaviour. By including consumer spending, purchasing power and wealth indicators in your analysis, you can rapidly identify trends that enable decision-makers in sectors such as Retail, Real Estate & local government to optimize their operations, reduce costs and increase revenues.
How to use this data in CARTO?
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Discover datasets to enrich your analysis, by category, country or provider.
Bring new datasets into your analysis using our Data Observatory, augmenting the data you're already using in CARTO.
Example of Financial Data Analysis
Retail Big Box Stores
Retail Big Box Stores in the US use CARTO’s platform to bring together their internal data on sales and performance with external consumer spending insights. This allows them to identify potential targets for expansion or consolidation, seeing how economic trends will affect their brick and mortar revenues.
Example of Financial Data Analysis
Commercial Real Estate Investment Funds
Commercial Real Estate Investment Funds use CARTO’s platform to bring together data on opportunity zones, available properties and open data (such as crime statistics) to identify potential locations for new co-working spaces and retail units. Consumer spending insights allows them to understand consumer journeys and catchment areas.
Example of Financial Data Analysis
Electric Vehicle Charging Point Planning
Electric vehicle charging point planning teams use UK financial data in CARTO alongside their internal data to understand potential locations where demand for electric vehicle charging points is likely to grow, using demographic insights and automotive spend data.
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DISCOVER OTHER SPATIAL DATA CATEGORIES
Property statistics, prices, and history to drive decisions and investment.
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The most recent census data including: age, income, household types, and more.
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Consumer Spending, Purchasing Power and Wealth indicators.
Data from routing apps and GPS to analyze traffic patterns and commuters.
Mobile device and GPS data provide insight into human movement patterns.
Location data for business establishments, restaurants, schools, attractions, …
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Climate and weather data, including exposure to weather-based hazards.
Browsing habits, app usage, feelings and experiences shared over social media platforms, ...
Digital boundaries for data aggregation and display on a map.
