Top 10 Location Intelligence Use Cases

Introduction

Businesses compete for more than just price today. Instead, there is a strong emphasis on opening stores in the right areas and determining the resources needed in each geographic market. Location intelligence use cases have significantly impacted strategic decision-making by allowing companies to move beyond monitoring competitor prices to incorporate data from various sources into a comprehensive spatial analysis of how their operations are impacting customers in the marketplace.

Retailers use location intelligence applications for various tasks, such as deciding when to open or close stores and detecting fraud for their customers. By effectively managing Point of Interest (POI) data use cases and geospatial analytics, you can gain a competitive edge over your rivals. By doing this, you will cut down on operational costs, find more effective ways to target your customer base, and enter the marketplace much more quickly than you would without these location-based technology tools that are now part of your business processes.

This guide will walk you through some tried-and-true use cases of how location intelligence can drive revenue, eliminate risk, and increase efficiency across your business operations. By leveraging B2B data alongside geospatial technologies, companies can gain valuable insights and optimize strategies. You’ll be able to see examples of how using B2B location intelligence applications, urban design applications, or other use cases can give companies a competitive edge through geospatially based data sources.

What Is Location Intelligence?

Location intelligence combines business analytics with geographic data to help answer questions about your market, customers, and operations. This approach incorporates multiple types of data, such as point-of-interest (POI) data, geolocation and coordinate data, mobility patterns, and foot traffic, to develop actionable insights.

Location data analytics helps organizations understand where things happen. Unlike traditional business intelligence, which focuses mainly on data analysis, location analytics adds geographical information.

Retailers use it to find the best spots for their stores. Logistics companies use it to choose optimal warehouse locations and improve trucking routes. Financial services companies rely on it to detect fraud patterns based on geographic trends. Overall, location intelligence is a key part of decision-making across various business areas.

Organizations that implement location intelligence applications have validated their expansion plans, optimized their sales territories, and better understood the competitive landscape. As a result, data-driven decisions minimize risk and deliver greater returns on investment across the organization.

What Are the Most Common Location Intelligence Use Cases?

The most common Location Intelligence Use Cases are:

1. Site Selection and Market Expansion

Selecting a good site is vital to retail store success and has a significant impact on whether a store survives its first year in business. By utilizing location intelligence in selecting sites, such as defining the right area by analyzing demographic trends, competitor distance, traffic volume, and market saturation, one can ultimately find the best spot for a retail store.

To determine the existing competition nearby, retailers access Points of Interest (POI) data through tools like LocationsCloud. The retailers then analyze areas with high income and low service coverage. The retailer’s real estate team can identify foot traffic volume across an area and predict how many customers may visit their store, helping them avoid opening stores in oversaturated markets and saving the retailer substantial money.

Drive-through restaurants often excel at using this methodology. Examples of the type of data retrieved and analyzed include:

• Drive time from residences.
• Proximity of other complementary businesses.
• Visibility from major streets.
• Demographics of target customers matched with those of potential buyers.

LocationsCloud helps retailers leverage Point of Interest (POI) data use cases to plan store expansions without requiring confirmed POI information. This approach allows retailers to reduce risk and accelerate the recovery of their investment in new stores.

2. Competitive Intelligence and Market Mapping

Understanding your competition is key to developing insights about them. You will need to know when and where they’re entering and leaving markets to determine your own position against them. To help achieve this, geospatial analytics use cases enable you to collect competitive intelligence and spot market opportunities (gaps) relative to your competitors.

Many companies leverage location data analytics to track competitors’ store openings and closures to identify areas with high demand and low competition (aka “white space”). Telecommunications companies typically map their competitors and identify coverage gaps; banks also map their branch locations to identify underserved communities.

Geospatial competitive market maps can also show how clustered competitors typically are within affluent neighborhoods, and how many do not serve certain demographics. Companies can use this data to determine how best to differentiate their strategy and where to position themselves in the market. Businesses can use LocationsCloud Point of Interest data to compare their market presence with their competitors.

3. Sales Territory Design and Coverage Optimization

Improving sales territories with B2B location intelligence helps sales teams and customers work more efficiently, which increases revenue.

Poorly balanced sales territories can lead to frustration and inefficiency for a company, resulting in underutilized resources. Analyzing and designing territories based on historical data using an intelligent mapping tool enables sales leaders to maximize customer visits by showing how long it would take a sales representative to reach each customer’s location.

Location intelligence can be helpful to both field service and field sales organizations to improve efficiency in several ways:

• Create areas where technicians can service customers based on geographical proximity
• Provide fast response to service requests that have the highest priority
• Reduce fuel consumption by optimizing technician routes
• Effectively balance workload per technician in a service area

Using a mapping application with location intelligence can help businesses improve the customer experience and reduce operational costs. LocationCloud has the necessary location data to design equitable, fair, and effective B2B territories.

4. Retail Network Optimization

Not all retail locations yield a positive return on investment. Retail network optimization relies on location intelligence use cases, enabling retail networks to identify low-performing stores and determine whether consolidation is warranted.

For each retail location, a retailer will analyze the retail catchment area (the geographic area that the retail draws customers from), evaluate if nearby locations are taking sales away from each other (cannibalization), and utilize foot traffic volumes to determine which retail locations have not been visited by customers at the expected level.

Additionally, a retailer will analyze population demographics using historical trend data to assess whether the demographic composition of retail markets continues to align with the retailer’s marketing strategies.

The findings of this analytical investigation help retailers in making the following decisions regarding retail locations:

• Should the retailer close stores that are operating below acceptable performance?
• Can the retailer convert retail locations to other formats?
• If the retailer were to relocate to another site within its existing retail network, would performance improve?
• Are there retail markets that can justify additional retail locations?

Using comprehensive Point of Interest (POI) data use cases and validated business premises datasets stored in LocationsCloud will help retailers evaluate and model the above scenarios and provide insights for an actionable, data-driven strategy to maintain brand equity and optimize network profitability.

5. Foot Traffic Analysis and Demand Measurement

Foot traffic analytics is one of the best ways to assess demand for retail stores in a physical location. Location data analytics calibrate how visitors move through space and interact with retail spaces.

Retailers will compare foot traffic across locations to determine which stores are their best-performing. By assessing how many visitors are generated by marketing campaigns, retailers can examine store purchases to determine whether a campaign was effective.

Similarly, real estate developers and other investors evaluate the value of a particular property by understanding how many pedestrians are in the immediate vicinity.

Shopping malls will use traffic data to determine the best tenant mix for their malls.

Statistical uses of foot traffic include:

• Measuring seasonal variation in demand.
• Evaluating the effectiveness of a store’s layout.
• Compared to similar locations.
• Validating ROI on marketing based on the number of visitors.

The greatest value of foot traffic analytics comes from its integration with other geospatial analytics use cases. When used together, traffic data from LocationsCloud provides the contextualized point-of-interest data needed to turn raw foot traffic counts into actionable, strategic insights.

6. Supply Chain and Logistics Optimization

Strategic placement of facilities within your supply chain is essential for your business’s operational efficiency. Using location intelligence applications enables warehousing & distribution centers to be placed in the best possible locations, minimizing freight costs while maximizing delivery speed.

For example, e-commerce businesses analyze their customer base to determine where they should locate their warehouses. They seek to balance the need to be reasonably close to serve population clusters without incurring excessive real estate & transportation costs. Ultimately, they reduce the time for last-mile delivery & the overall logistics cost by applying best practices in this area.

Logistics optimization includes:

• Identifying geographic clusters with potentially high demand for the product.
• Measuring how accessible local transportation networks are to those clusters.
• Researching where competitors have placed their warehouses & distribution centers.
• Developing a model to demonstrate how much area can be serviced based on available transportation modes from their warehousing locations.

LocationsCloud provides you with all of the data you need to give your logistics company the confidence to make facility placement decisions. The cost savings from using B2B location intelligence provide your organization with additional funds to improve your company’s service.

7. Risk Assessment and Compliance (KYC/AML)

Regulatory pressure on financial institutions is increasing daily as they are required to verify customer identities and continuously monitor them to detect and prevent fraud. Location intelligence use cases for risk assessment leverage geographic data combined with transaction behavior to highlight outliers (anomalies), helping financial institutions comply with Know Your Customer (KYC) and Anti-Money Laundering (AML) regulatory requirements.

Commercial banks look at the geographic location of a customer’s business address to help confirm its legitimacy before onboarding the customer. If the customer’s transaction history contains multiple transactions from high-risk geographic areas, these transactions will typically be flagged.

Fintech companies leverage geospatial analytics to identify account takeover attempts by tracking where users try to access their accounts with their login credentials.

Primary Compliance Applications for geospatial analytics include:

• Confirming a business address against its registered location
• Identifying multiple account holders associated with suspicious addresses
• Identifying suspicious geographic patterns of transactions
• Identifying risk based on neighborhood characteristics.

Overall, implementing location data analytics to increase security and improve regulatory compliance processes is a tremendous benefit to the financial industry. LocationsCloud offers verified POI data for banks and other financial institutions to verify business addresses and authenticate location-based risk assessments.

8. Real Estate and Investment Intelligence

Investors in property and developers in the real estate market require in-depth knowledge of the overall market to assess potential investments and identify associated risks. The use of location intelligence applications enables spatial analysis of properties to create value estimates for commercial and residential development decisions and to manage a portfolio of investment properties.

Real estate professionals in a market use location intelligence applications to analyze neighborhood amenities, types of commercial activity, and demographic trends. Private equity firms use location intelligence applications to evaluate the highest-quality retail locations before making investment decisions. Venture capitalists evaluate potential investments in their portfolio companies by assessing the validity of their market claims through POI analysis.

An evaluation of investment opportunities typically includes an analysis of:

• Commercial density and retail mix.
• Transportation accessibility.
• Demographic and income levels of neighborhoods.
• Development activity and approved development application/permit status.

LocationsCloud provides comprehensive B2B location intelligence and business location data to help investors and developers make informed investment decisions. By analyzing and using this information, investors/developers can reduce their overall investment risk and identify high-potential investment opportunities.

9. Urban Planning and Smart Cities

Geospatial analysis helps city governments and urban planners improve infrastructure and public services. This work enhances residents’ quality of life. Cities can use life cycle analysis (LCA) to gain insight into a community’s mobility patterns and identify areas with service deficiencies and/or infrastructure needs.

To optimize transportation routes and schedules, urban planners analyze pedestrian traffic flows. To evaluate the need for a commercial density, urban planners will also determine how many residents are within walking distance of commercial amenities. In addition, infrastructure teams analyze location data analytics assets to identify where improving roads is a priority.

Smart city solutions include:

• Optimization of Public Transportation Routes and Schedules
• Identifying neighborhoods that historically have been underserved and where additional public infrastructure/amenities are needed
• Planning improvements to the Pedestrian Infrastructure
• Analyzing the development of New Commercial Establishments

In short, the Location Intelligence application supports evidence-based planning in urban areas. The LocationsCloud-generated point of Interest (POI) data provides the necessary information for a community to gain a better understanding of its commerce and, therefore, to better inform decision-making related to their planning.

10. Data Products, AI, and Analytics Platforms

Companies offering Software as a Service (SaaS) and analytics have added location capabilities to their offerings. This capability enables location intelligence use cases to drive dashboards, train machine learning models, and power location-based functionality for customers.

Analytics platforms enhance customer data using geographic context. Demand prediction models leverage Point of Interest (POI) data, while business intelligence tools develop graphical representations of market opportunities through geospatial analytics use cases.

Examples of applications of location intelligence include:

• Building dashboards and visualizations that are location-aware
• Training predictive models using spatial properties
• Enhancing customer relationship management (CRM) with geographic properties
• Developing SaaS-based products using location intelligence.

LocationsCloud provides the API access and bulk data feeds developers need to add location intelligence to their products. This B2B location intelligence allows SaaS companies to develop differentiated tiers of offering and provide additional value to their customers.

What Data Types Power Location Intelligence?

To achieve the most efficient location intelligence application possible, combining POI data, Geographic Location Data, Mobility Data, and Foot Traffic Data into an integrated solution will provide you with complete insights.

Each type of Location Intelligence will enable different analyses, including but not limited to Competitive Analysis, Market mapping, Tracking the customer journey, and Measuring Real-World Demand.

Most location intelligence use cases will require multiple data set types. Therefore, access to integrated, complete, and analytics-ready data sets is essential for your business.

How Do Businesses Access Location Intelligence Data?

Organizations acquire location information through various routes, including scraping websites, using point-of-interest databases, accessing application programming interfaces (APIs), obtaining bulk data feeds, or working with enrichment companies.

Each method provides unique benefits tailored to the organization’s technical needs—for example, API access enables near-instant integration, while bulk feeds work well for large-scale analytical reporting. Businesses will therefore have to select the delivery method that best suits their location intelligence use case and business operations.

Why Does POI and Location Data Quality Matter?

Data quality affects the accuracy of decision-making across all types, including geospatial analytics. In particular, using poor data when considering POIs (points of interest) will create blind spots and very little knowledge of competitors. Outdated locations will make site selection difficult, and inaccurate coordinates can create significant routing issues.

To generate accurate and consistent results, Enterprise Applications must use validated datasets with standardised address formats, categorised by address type, and verified against descriptions and/or attributes.

How Does LocationsCloud Enable Location Intelligence at Scale?

LocationsCloud provides businesses across all industries with verified points of interest (POI) data accessible worldwide, as well as detailed local-level information. This is delivered in analytics-ready format for use in B2B location intelligence. Through flexible access via APIs or bulk delivery.

LocationsCloud supports enterprises across retail, real estate, logistics, and financial services by providing highly accurate data to help each business make informed decisions about every aspect of their operations.

Location Intelligence Transforms Geography into Strategy

Organizations can use geospatial analytics, Location Intelligence use cases, and Location Data Analytics to provide companies with a competitive advantage by enabling more accurate decision-making and reducing the risk of those decisions. Therefore, organizational success depends on the quality of your data, as incomplete or out-of-date information can lead to erroneous conclusions.

LocationsCloud delivers B2B location intelligence via verifiably accurate, globally covered, and continuously updated datasets, thus turning geographic-based data into actionable business strategy.

FAQ

What are the most common location intelligence use cases?

The most common location intelligence use cases include site selection, competitive analysis, sales territory optimization, retail network planning, supply chain optimization, and risk assessment. Retailers use these applications for expansion planning. Logistics companies optimize facility locations. Financial institutions detect fraud patterns.

 Which industries benefit most from location intelligence?

Businesses with physical locations can greatly benefit from location intelligence applications. This includes sectors like retail, quick-service restaurants, real estate, logistics, banking, financial services, telecommunications, and analytics platforms. Any business that operates in geographic markets can use geospatial analytics effectively.

 What data is required for location intelligence?

Location intelligence use cases typically require POI data, geolocation coordinates, demographic information, foot traffic data, and mobility patterns. Most applications combine multiple data types to generate comprehensive insights. LocationsCloud provides integrated datasets that support diverse analytical needs.

 Is location intelligence only for large enterprises?

No. While large enterprises extensively use B2B location intelligence, small and medium businesses also benefit from location intelligence applications. Emerging brands use POI data use cases for expansion planning. Regional chains optimize existing networks. Startups validate market opportunities before investing in physical locations.

 Can location intelligence be used for AI and analytics?

Location data analytics are essential for machine learning, predictive analytics, and AI applications. Data scientists use points of interest (POI) data as features for their models. Analytics platforms include geospatial analytics in their dashboards. Software as a Service (SaaS) products use location features to stand out from their competition.

 Does LocationsCloud provide location data via API?

LocationsCloud offers two ways to access data: you can use an API for real-time integration or download bulk data for large-scale analysis. This flexibility helps businesses develop location-based applications that meet their specific needs and technical requirements.