Location Intelligence Data: Examples and Use Cases for 2025
At Echo Analytics, we work with data buyers across industries, including retailers, real estate firms, advertisers, urban planners, and many more, who all have one thing in common: they want to understand the real world better and move faster because of it.
We’ve seen firsthand what happens when businesses rely on generic, outdated, or incomplete location data: missed opportunities, wasted budgets, and poor experiences. That’s why this guide, made in partnership with Datarade, is focused on how to use location intelligence and avoid the mistakes we see far too often.
If you’re working with or planning to buy location intelligence data, this is the guide for you.
At its core, location intelligence combines points of interest (POIs), like stores, hotels, charging stations, or parks, with contextual layers such as foot traffic, mobility patterns, demographics, and dwell time. The result is a dynamic picture of how people interact with the physical world.
More than ever, companies are making real-world decisions based on digital data. But digital-only data can miss the full picture. That’s where location data steps in, giving you behavioral insight that bridges the gap between online profiles and offline actions.
Most businesses already have access to some form of location data, but it’s often incomplete, scraped from the web, or outdated. That leads to missed insights and flawed models.
📌 Expert Tip: What separates good from great location data is quality and processing. Look for datasets that are:
Buying location intelligence data isn’t like downloading a static map file or grabbing a free dataset online. It’s an investment, and like any investment, success depends on asking the right questions up front. Here’s what you need to know before choosing a provider:
Let’s start with what can go wrong. These are the most common issues we’ve seen when buyers don’t vet their location data providers thoroughly:
Incomplete or Inaccurate POIs: The POI might exist on the map, but is it open? Is it active? What type of venue is it exactly? If this context is missing, the POI is just noise in your model.
Inconsistent Data Quality Across Regions: Some providers have great coverage in one country and patchy coverage everywhere else. That’s a problem if your business operates internationally or plans to expand.
Lack of Confidence Scores or Metadata: Without indicators of data reliability (e.g., how recently it was updated, how confident the classification is), you can’t fully trust what you’re buying.
Missing Behavioral or Contextual Layers: Location alone isn’t insight. Without footfall, dwell time, or mobility overlays, you’re stuck with static data that doesn’t reflect how people actually interact with the space.
No Deduplication or Cleaning Process: One store may appear three times in the same dataset under different spellings. That’s a sign of a provider that isn’t doing the heavy lifting for you.
Unclear Licensing, Pricing, and Delivery Formats: You should always know how often your data is updated, how it will be delivered (API? S3? Email?), and what you’re allowed to do with it (can you use it across teams? for resale?).
Privacy and Compliance: Especially in Europe, compliance with GDPR is critical. If a provider can’t clearly explain how their data is collected and anonymized, walk away.
📌 Expert Tip: Ask your provider about their taxonomy, update frequency, and QA processes. If they can’t give you a straight answer, that’s a red flag.
Here’s what to look for:
Once you’ve decided to use location intelligence data to drive your strategy, the next big decision is choosing the right provider. To avoid wasting time and budget, keep a few key questions in mind when speaking to location data providers. Think of them as your buying blueprint: if a provider can’t answer clearly or confidently, it’s a red flag.
This is your first filter. High-quality data starts with high-quality sourcing.
📌 Pro Tip: You want providers who are transparent and can explain the lineage of their data. Echo, for instance, only delivers processed, verified data, not scraped datasets patched together.
A restaurant that closed two years ago is still a POI on many platforms. That’s a problem.
Ask:
📌 Pro Tip: Look for providers that use automation and human review to maintain data accuracy, especially across different countries and sectors.
A good dataset will not just tell you “there’s a business at this location”, it will tell you what kind of business it is, how it compares to similar venues, and what kind of traffic it attracts.
Ask:
📌 Pro Tip: Ask for a sample showing category granularity. That way, you know if you can use it for segmentation or targeting.
Location is just the start. True intelligence comes from layers like mobility, behavior, and demographics.
Ask:
📌 Pro Tip: Providers like Echo combine POI and foot traffic in one dataset, saving you the trouble of stitching insights together later.
You’ll want to integrate this data into your systems or tools quickly.
Ask:
📌 Pro Tip: A provider should meet you where you are, not force you to rebuild your pipeline just to use their data.
This is often overlooked, but it matters, especially if your team is cross-functional or if you work with clients.
Ask:
📌 Pro Tip: Ask for licensing clarity in writing. Good providers will be upfront. Great providers will offer flexible plans.
Access to data is one thing. Understanding how to use it effectively is another.
Ask:
📌 Pro Tip: Echo’s onboarding includes a milestone-based process, from first data delivery to 30-day check-ins and quarterly reviews, designed to ensure long-term success.
Here's a simple checklist you can take into your next conversation:
Once you’ve decided to use location intelligence data, the next step is putting it into action. Whether you’re building analytics dashboards, running marketing campaigns, planning retail locations, or enriching your internal BI tools, getting started doesn't have to be complicated, as long as you choose the right approach and partner.
This section will walk you through how to go from zero to value quickly, avoid common pitfalls, and ensure a smooth onboarding process for your team.
Before anything else, define your goals. What are you trying to solve or improve with location data? Examples of strong use cases:
📌 Expert Tip: A clearly defined use case helps your provider recommend the right datasets, formats, and enrichment layers from the beginning.
Instead of jumping into a full deployment, most companies start small. Echo recommends testing a sample dataset to validate coverage, structure, and compatibility with your tools. Here’s what to look for in a sample:
📌 Expert Tip: Use the sample to test how easily you can join the data with your existing platforms, such as CRM, GIS tools, dashboards, or campaign planning software.
Depending on your internal systems and team size, different delivery formats may work better:
Echo supports all of the above and tailors delivery to your tech stack.
📌 Expert Tip: Ask if the provider can support recurring delivery (e.g., monthly footfall updates) and historical datasets for trend analysis.
Most buyers begin with POI data, a structured list of locations. But true location intelligence comes when you layer in additional signals:
These layers add the why and how to the where, turning static points into strategic insight.
📌 Expert Tip: If you work in a sector like retail, automotive, or tourism, combining POI with mobility data unlocks deeper customer understanding and smarter decision-making.
Once your proof of concept delivers value, think long-term:
Echo’s onboarding includes milestone-based checkpoints, so clients can start small and scale as new needs emerge. Their white-glove approach ensures no surprises down the line.
📌 Expert Tip: Build internal buy-in by showing early results, for example, a test campaign that performs better with POI targeting, or a new store analysis with smarter catchment logic.
Location data is no longer a "nice to have"; it's a core driver of smarter business decisions across almost every industry. From retail to automotive, from adtech to public planning, location intelligence delivers a measurable ROI when implemented with precision and quality.
In this section, we break down the top real-world use cases across seven key industries, based on insights from Echo Analytics' work with 200+ companies and their eBook: "Driving Business Outcomes with Location Intelligence".
Location intelligence is not one-size-fits-all. It’s a high-impact enabler of smarter business decisions across every vertical. Whether you’re running a media campaign or managing a global POS network, Echo’s insight-ready data ensures you get ROI from day one.
At Echo, we’ve built our entire buisness around solving these exact pain points. Our POI data is cleaned, enriched, and classified using industry-grade taxonomies. We combine it with real mobility patterns, so you know not just where people are, but what they’re doing and why it matters.
We also tailor delivery to your needs: whether you're an adtech platform needing hyperlocal POI feeds, a retail chain optimizing store networks, or a consultant building proposals with footfall data.
At Echo Analytics, we work with data buyers across industries, including retailers, real estate firms, advertisers, urban planners, and many more, who all have one thing in common: they want to understand the real world better and move faster because of it.
We’ve seen firsthand what happens when businesses rely on generic, outdated, or incomplete location data: missed opportunities, wasted budgets, and poor experiences. That’s why this guide, made in partnership with Datarade, is focused on how to use location intelligence and avoid the mistakes we see far too often.
If you’re working with or planning to buy location intelligence data, this is the guide for you.
At its core, location intelligence combines points of interest (POIs), like stores, hotels, charging stations, or parks, with contextual layers such as foot traffic, mobility patterns, demographics, and dwell time. The result is a dynamic picture of how people interact with the physical world.
More than ever, companies are making real-world decisions based on digital data. But digital-only data can miss the full picture. That’s where location data steps in, giving you behavioral insight that bridges the gap between online profiles and offline actions.
Most businesses already have access to some form of location data, but it’s often incomplete, scraped from the web, or outdated. That leads to missed insights and flawed models.
📌 Expert Tip: What separates good from great location data is quality and processing. Look for datasets that are:
Buying location intelligence data isn’t like downloading a static map file or grabbing a free dataset online. It’s an investment, and like any investment, success depends on asking the right questions up front. Here’s what you need to know before choosing a provider:
Let’s start with what can go wrong. These are the most common issues we’ve seen when buyers don’t vet their location data providers thoroughly:
Incomplete or Inaccurate POIs: The POI might exist on the map, but is it open? Is it active? What type of venue is it exactly? If this context is missing, the POI is just noise in your model.
Inconsistent Data Quality Across Regions: Some providers have great coverage in one country and patchy coverage everywhere else. That’s a problem if your business operates internationally or plans to expand.
Lack of Confidence Scores or Metadata: Without indicators of data reliability (e.g., how recently it was updated, how confident the classification is), you can’t fully trust what you’re buying.
Missing Behavioral or Contextual Layers: Location alone isn’t insight. Without footfall, dwell time, or mobility overlays, you’re stuck with static data that doesn’t reflect how people actually interact with the space.
No Deduplication or Cleaning Process: One store may appear three times in the same dataset under different spellings. That’s a sign of a provider that isn’t doing the heavy lifting for you.
Unclear Licensing, Pricing, and Delivery Formats: You should always know how often your data is updated, how it will be delivered (API? S3? Email?), and what you’re allowed to do with it (can you use it across teams? for resale?).
Privacy and Compliance: Especially in Europe, compliance with GDPR is critical. If a provider can’t clearly explain how their data is collected and anonymized, walk away.
📌 Expert Tip: Ask your provider about their taxonomy, update frequency, and QA processes. If they can’t give you a straight answer, that’s a red flag.
Here’s what to look for:
Once you’ve decided to use location intelligence data to drive your strategy, the next big decision is choosing the right provider. To avoid wasting time and budget, keep a few key questions in mind when speaking to location data providers. Think of them as your buying blueprint: if a provider can’t answer clearly or confidently, it’s a red flag.
This is your first filter. High-quality data starts with high-quality sourcing.
📌 Pro Tip: You want providers who are transparent and can explain the lineage of their data. Echo, for instance, only delivers processed, verified data, not scraped datasets patched together.
A restaurant that closed two years ago is still a POI on many platforms. That’s a problem.
Ask:
📌 Pro Tip: Look for providers that use automation and human review to maintain data accuracy, especially across different countries and sectors.
A good dataset will not just tell you “there’s a business at this location”, it will tell you what kind of business it is, how it compares to similar venues, and what kind of traffic it attracts.
Ask:
📌 Pro Tip: Ask for a sample showing category granularity. That way, you know if you can use it for segmentation or targeting.
Location is just the start. True intelligence comes from layers like mobility, behavior, and demographics.
Ask:
📌 Pro Tip: Providers like Echo combine POI and foot traffic in one dataset, saving you the trouble of stitching insights together later.
You’ll want to integrate this data into your systems or tools quickly.
Ask:
📌 Pro Tip: A provider should meet you where you are, not force you to rebuild your pipeline just to use their data.
This is often overlooked, but it matters, especially if your team is cross-functional or if you work with clients.
Ask:
📌 Pro Tip: Ask for licensing clarity in writing. Good providers will be upfront. Great providers will offer flexible plans.
Access to data is one thing. Understanding how to use it effectively is another.
Ask:
📌 Pro Tip: Echo’s onboarding includes a milestone-based process, from first data delivery to 30-day check-ins and quarterly reviews, designed to ensure long-term success.
Here's a simple checklist you can take into your next conversation:
Once you’ve decided to use location intelligence data, the next step is putting it into action. Whether you’re building analytics dashboards, running marketing campaigns, planning retail locations, or enriching your internal BI tools, getting started doesn't have to be complicated, as long as you choose the right approach and partner.
This section will walk you through how to go from zero to value quickly, avoid common pitfalls, and ensure a smooth onboarding process for your team.
Before anything else, define your goals. What are you trying to solve or improve with location data? Examples of strong use cases:
📌 Expert Tip: A clearly defined use case helps your provider recommend the right datasets, formats, and enrichment layers from the beginning.
Instead of jumping into a full deployment, most companies start small. Echo recommends testing a sample dataset to validate coverage, structure, and compatibility with your tools. Here’s what to look for in a sample:
📌 Expert Tip: Use the sample to test how easily you can join the data with your existing platforms, such as CRM, GIS tools, dashboards, or campaign planning software.
Depending on your internal systems and team size, different delivery formats may work better:
Echo supports all of the above and tailors delivery to your tech stack.
📌 Expert Tip: Ask if the provider can support recurring delivery (e.g., monthly footfall updates) and historical datasets for trend analysis.
Most buyers begin with POI data, a structured list of locations. But true location intelligence comes when you layer in additional signals:
These layers add the why and how to the where, turning static points into strategic insight.
📌 Expert Tip: If you work in a sector like retail, automotive, or tourism, combining POI with mobility data unlocks deeper customer understanding and smarter decision-making.
Once your proof of concept delivers value, think long-term:
Echo’s onboarding includes milestone-based checkpoints, so clients can start small and scale as new needs emerge. Their white-glove approach ensures no surprises down the line.
📌 Expert Tip: Build internal buy-in by showing early results, for example, a test campaign that performs better with POI targeting, or a new store analysis with smarter catchment logic.
Location data is no longer a "nice to have"; it's a core driver of smarter business decisions across almost every industry. From retail to automotive, from adtech to public planning, location intelligence delivers a measurable ROI when implemented with precision and quality.
In this section, we break down the top real-world use cases across seven key industries, based on insights from Echo Analytics' work with 200+ companies and their eBook: "Driving Business Outcomes with Location Intelligence".
Location intelligence is not one-size-fits-all. It’s a high-impact enabler of smarter business decisions across every vertical. Whether you’re running a media campaign or managing a global POS network, Echo’s insight-ready data ensures you get ROI from day one.
At Echo, we’ve built our entire buisness around solving these exact pain points. Our POI data is cleaned, enriched, and classified using industry-grade taxonomies. We combine it with real mobility patterns, so you know not just where people are, but what they’re doing and why it matters.
We also tailor delivery to your needs: whether you're an adtech platform needing hyperlocal POI feeds, a retail chain optimizing store networks, or a consultant building proposals with footfall data.
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