What is location analytics?

Location analytics is the practice of adding a layer of geographical data to a business’s data assets in order to extract more valuable insights. It is sometimes referred to as “geoanalytics.” Across industries, business data, including data on people, events, transactions, assets, and more, often includes a geographic component, which when added to an analysis of performance may unlock new related insights. This allows for greater context when asking questions about different business processes, offering a new understanding of trends and relationships in the data.

Location analytics provides everyone in an organization with spatial analytics and other analytics capabilities to understand the data through a location-specific perspective and make predictions and optimize business practices accordingly. Adding location to an organization’s analytics allows for greater context in decision making and drives greater insights that may not have been uncovered using traditional, flat business intelligence (BI) data.

Furthermore, maps are uniquely positioned to generate insights from location data—and are especially easy for non-experts to understand. This would not be possible with traditional analytics such as statistical plots, charts, or tables. From optimizing operations across different territories to matching assets in the field to appropriate resources to testing the profitability of potential new locations, location analytics can help businesses make better decisions based on geospatial information.

What are the benefits of location analytics?

In today’s global economy, understanding geospatial impacts on the business is critical for success. Companies that use location analytics to assess their business strategies can decrease costs, locate new sales opportunities, and implement changes for operational efficiency. Furthermore, location analytics is highly visual and therefore easier for non-experts to understand insights found in the data. This allows for those insights to be communicated across the organization and more readily acted upon by different departments and teams.

The following are three key benefits to be gained from implementing location analytics:

  • Hyper-local Intelligence: With location analytics, you can automatically turn data into location-based insights with beautiful, location-aware visualizations for Geographic Information System (GIS) and communicate with business users, analysts, scientists, and developers—everyone. This improved information management enhances collaboration across teams and helps everyone in the business make more informed decisions, often leading to reduced costs and increased revenue.
  • Real-World Context: Unlike many other information visualizations, a map connects data to the real world, clearly showing how location relates to other data features. This context enriches insights obtained as analytics teams and end users drill down into visualizations. It’s often the best way to add context and answer questions related to “where.” This greater context can help organizations find new opportunities and optimize operations.
  • Actionable Insights: Location analytics enables users to create insightful geographical analysis instantly without the learning curve of other GIS tools. But, don’t worry, it still provides the depth of analytical capabilities needed for predictive analysis and other optimizations through location. It’s the best of both worlds.

How do different businesses use location analytics?

Location analytics can be applied across a variety of industries for business improvements. In fact, location analytics can help to improve business processes from beginning to end, including manufacturing, assembly, logistics, and distribution processes. It can also help improve marketing strategies by using geographical data to better target the right people, to make relevant offers in real time, and to understand customers’ biggest needs. Furthermore, targeting customers with personalized content has become increasingly popular.

Location is often included in most business processes. From every financial transaction to stock transactions to location tracking and more, location is a crucial part of any business’s data. More and more organizations are looking for ways to harness their location data. For example, smart cities, connected vehicles, IoT, and smart factories are all recent technology developments that rely on location analytics.

Location analytics is also being used to identify places that businesses may want to target by filtering through demographic data, to optimize resource allocation by analyzing localized needs, and to make predictions concerning future business and market trends using historical and real-time data trends. As a result, businesses can monitor, analyze, and make decisions at the right time in the context of a geography.

Necessary capabilities for location analytics

Traditional charts, graphs, and tables are no longer sufficient when businesses are looking to uncover valuable information in location data. Oftentimes these tools miss important pieces of information. Instead, companies must insert spatial context to the data to generate deep insights. To do this, businesses require the following capabilities to be successful with location analytics:

Automatic, Interactive Maps: If your data includes geographical components such as country, state, city or GPS coordinates, you need a location analytics solution that can automatically recognize them as locations. It should also be able to offer an interactive map where users can quickly drill down into the corresponding data for analysis on the spot.

Multi-layered Maps: Location analytics needs to add more context to your maps. It should help you find location-based insights by bringing in other data from various sources and including multiple layers in the map. With your location analytics solution, you should see associations in your data with different zoom levels, including country, state, city, neighborhood, or address.

Non-geographical Maps: Unlike other types of visualizations, a map instantly connects data to a real-world context that others can easily interpret. That accessibility often leads to fast insight and understanding, helping users clearly identify how location (even non-geographic spatial data like store layout, seating chart, human anatomy, etc.) relates to other features of the data. With the ability to map non-geographic spaces, businesses can optimize routing and scheduling, calculate the economic potential of different route networks, reduce fuel emissions, optimize site selection, and more.

GIS Standards: Organizations require powerful multi-layered maps. They need to enable users to zoom into each layer, from the country down to the state, county, city, neighborhood, and house level. Then users should be able to choose to drill down into various associations—going beyond the base map (roads) to view data uniquely pertinent to each specific map level.

Third-party Base Maps: Batch processing is also important for location analytics solutions. It can be used to change addresses into latitude and longitude coordinates that can then be implemented in various location applications. It should enable worldwide address-level geocoding, route calculations, step-by-step directions, and additional geo-processing capabilities to analyze business data with best precision and accuracy.

Advanced Location Services: Location analytics solutions should also enable businesses to create more advanced spatial analyses of their own. This can allow experts to map your company’s data down to the address level. That way the business can calculate distances and optimize routes between multiple points of data, and also compute trade or catchment areas based on distance or travel time for a certain location.

Location analytics example

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