What the Retail Industry Is Really Talking About: 4 Takeaways from Shoptalk Spring
Christine Detris
• Apr 10, 2026 • 5 min read
Every year, Shoptalk brings together thousands of retail leaders, emerging brands, and technology innovators to talk about where the industry is heading. This year’s conference was squarely focused on AI; how it can expand what their teams are capable of, how it can solve long-standing challenges in merchandising, demand planning, and customer engagement, and how to move past the hype and into real impact.
Our team made the trip to Las Vegas to meet with players across the retail landscape firsthand, and while AI was impossible to ignore, some of our most energizing conversations kept coming back to the same question: what’s actually happening in the real world?
Here’s what stood out.
1. CPG Distribution and Demand Visibility Is the Conversation
If there was one theme that ran through nearly every meaningful conversation we had, it was that brands are dealing with shifting demand across channels and formats, and most don’t have a clear view of how that plays out in physical retail. In fact, many conversationskept coming back to a surprisingly common concern: brands not knowing whether they’re in the right places, reaching the right customers, or growing in the right direction.These are exactly the kinds of long-standing retail challenges that Dataplor is solving with better data and smarter tools.
2. Brands Know Where They Sell, But Not If Those Are the Right Stores
This was the most common “aha moment” we saw on the floor. Brand after brand had solid sales data, but when it came to whether they were optimally distributed or how to make a compelling case to a retailer for expansion, the answer was essentially a gut feeling. One version of this came up repeatedly: “We know where we sell, but we don’t know if those are the right stores, or how to convince retailers to put our product in more locations.” That’s exactly the gap location intelligence fills. Instead of relying on static store lists or retailer conversations alone, teams can look at real-world foot traffic patterns and retail density to prioritize where to go next, and make a data-backed case when they get there.
3. Brick and Mortar Is Having a Quiet Resurgence
For a conference where AI was the headline act, physical retail held its own in a surprising way. In our one-on-ones, a renewed focus on brick and mortar repeatedly surfaced. Many of the brand-side buyers we spoke with were almost relieved to shift the conversation to physical retail strategy. It’s not that AI and digital don’t matter, but the physical world hasn’t gone anywhere, and teams are starting to feel the gap between their digital sophistication and their visibility into what’s actually happening in stores. That energy was hard to miss. Which brings us to our final point…
4. The Physical World Is the Missing Layer in the AI Conversation
A significant portion of the attendees we met represented the digital and e-commerce sides of their businesses. This points to something important: the brands that are winning aren’t thinking about online and offline as separate problems. AI is reshaping how retailers think about decisions, and those decisions require robust, quality, and comprehensive data. The brands we spoke with are increasingly looking for ways to ground their strategies in real-world behavior, not just digital signals. Understanding the physical landscape—where demand is moving, where products should live, where to expand next—is a critical input to any well-rounded retail strategy. Talk to us to learn more.
An Intro to Movement Data: What it is and Why it Matters
Christine Detris
• Apr 08, 2026 • 5 min read
Movement data is data about how people move through the physical world. It’s derived from anonymized location signals off mobile devices, and it’s used across retail, commercial real estate, finance, and a growing list of other industries.
If you’ve ever seen a report that says a store received 15,000 visits last month, or that a shopping center’s traffic is up 8% year over year, that came from movement data.
Foot Traffic, Trade Area, and Other Common Terms
A lot of terminology in this space gets used interchangeably. Here are the ones worth knowing.
Foot Traffic: This is the most common term. It refers to how many people visit a physical location such as a store, restaurant, mall, or city block. Retailers have used this phrase for decades. In Europe you’ll often hear footfall instead.
Mobility Data: A broader, more encompassing term. Foot traffic tells us how many people showed up at a specific place, while mobility data also covers where they came from, what else they visited, and how travel patterns shift over time. Think of foot traffic as one slice of the bigger mobility data picture.
Trade Area: This is the geographic region a location draws its visitors from. The old way was to draw a radius on a map. Now, movement data lets you build trade areas based on where visitors actually live and work, which often looks very different.
Dwell Time: How long someone stays during a single visit. Dwell time is useful for understanding the nature of visits and benchmarking across competitors.
Visits vs. Visitors: People tend to mix these terms up. Visits = total trips, including repeats. Visitors = unique people. Someone who goes to the same coffee shop every weekday generates five visits but counts as one visitor. Most datasets report visits.
How Does Movement Data Actually Work?
The short version:
Anonymized location signals are collected from mobile devices at scale
Signals are cleaned, normalized, and matched against databases of real-world places (points of interest, or POIs)
When a device’s signal falls within a known location’s footprint, that’s recorded as a visit
Since no provider can see every device, statistical models scale the sample up to represent the broader population. While most models use a simple ratio based on the number of devices seen in the area, Dataplor uses additional factors like online popularity and modeling off the POI brand and category to arrive at a more accurate estimate of foot traffic.
What Can Brands Do With This Information?
Site Selection: Evaluate potential locations based on traffic and visitor profiles
Competitive Benchmarking: Compare your traffic against competitors
Portfolio Monitoring: Track trends across your own locations
Investment Research: Use foot traffic as a leading indicator of company performance
Urban Planning: Understand commuting patterns and pedestrian flows
Product Distribution: CPG companies can discover which stores to prioritize
Common QuestionsAbout Movement Data
Is this data tracking individuals?
No. The location signals come from devices where users have opted into location sharing. The data is anonymized and aggregated, so no names and no personal identifiers ever enter the Dataplor ecosystem. The goal is to understand patterns at a location level, not to track specific people.
Does this work outside the US?
It depends on the data provider. Many started offering US-only data and have limited international coverage. Device panels and POI quality vary significantly by region. If you operate globally, don’t just ask whether your provider has data in a country, ask how deep it is and how accurate the foundational POI data is.
Who uses this?
Retail and restaurant brands, commercial real estate firms, financial services (from hedge funds to insurance carriers, and everything in between), marketing agencies, CPG companies, tourism and economic development organizations, and increasingly tech companies that use POI and traffic data as a foundational layer in their own products.
What should I look for in a provider?
Geographic Coverage: Does it cover the regions and categories you care about, and how deep does it go?
POI Quality: Visit data is only as good as the map of places it’s matched against.
Methodology: How is the modeling done, and how reflective of the real world is it?
Update Cadence: How often is the data refreshed, and does that match your workflow?
Getting Started
Regardless of what you call it, the goal remains the same: turn human movement into action for your business to both accelerate growth and mitigate downside risk. Talk to us to learn more.
The Localization Challenge: Why Global Brands Need Market-by-Market Data to Compete
Kayla Kauffman
• Mar 19, 2026 • 4 min read
Competing globally has never been simple, but the complexity brands are facing today is fundamentally different than it was even a few years ago. Consumers are more informed, more selective, and more vocal about where and how they spend their money. Brand loyalty can shift quickly as local preferences, economic conditions, and cultural norms evolve.
At the same time, organizations have more data available than ever before. The challenge is no longer access, but confidence. With a growing number of data providers in the market, decision-makers are often left navigating inconsistent coverage, varying quality, and limited local context. Without trustworthy, market-specific intelligence, even well-funded global strategies can miss the mark.
Why High-Level Data Is No Longer Enough
Strategic decisions are only as strong as the data behind them. Country-level or high-level datasets may offer a broad snapshot, but they often mask critical differences that exist within markets. High-growth neighborhoods and areas of attrition can emerge quickly, driven by shifting demographics, changing consumer behavior, or new competitive dynamics.
Hyperlocal preferences vary dramatically from one city block to the next. Without timely, granular data, brands risk acting on outdated assumptions or incomplete pictures of market demand. To compete effectively, organizations need a continuously updated view of local insights that reflect real-world change as it happens.
The Global vs. Local Disconnect
Many global strategies fail when they are not adapted to local realities. QSR brands may expand too aggressively before demand is proven, or fail to localize menus to reflect cultural tastes. Retailers can underestimate whether a specific neighborhood will embrace their brand, impacting inventory health. CPG companies may misplace premium or value-oriented products, leading to cannibalization or lost shelf space.
Similar challenges extend to commercial real estate, logistics, and technology platforms. Reusing a successful site selection model from one market in another often overlooks differences in co-tenancy, foot traffic behavior, or surrounding amenities. Inconsistent or incomplete POI data compounds these risks, creating blind spots that slow response to emerging trends or hide warning signs of market decline.
Why Market-Level & Micro-Market Data Matter
Granular, location-level data enables decisions that simply aren’t possible with aggregated views alone. Product placement, merchandising strategies, and consumer alignment depend on understanding who is shopping, where they are moving, and what they prefer locally.
Local context also reshapes how performance benchmarks and competitor analysis should be interpreted. A competitor’s portfolio may look strong at a national level while masking underperforming locations or oversaturated trade areas. Market-specific intelligence allows teams to assess cannibalization risk, identify consolidation opportunities, and allocate resources where they will drive the greatest return.
Across industries, the pain points differ, but the root issue is the same. Without high-integrity local data, CPG brands waste distribution spend, retailers and QSRs struggle with underperforming locations, technology platforms build models on incomplete inputs, and CRE teams take on unnecessary risk in multi-tenant decisions. But having the right data is only part of the equation. Knowing when to act on it is what separates leaders from the competition.
Why Now? Global Scale & Local Depth Create Competitive Advantage
The pace of market change has accelerated. Consumer preferences shift faster, new competitors emerge with less warning, and economic conditions vary more sharply across regions than they did even a few years ago. Brands that rely on annual data refreshes or static market studies are already behind.
The organizations gaining ground today are those investing in real-time, hyperlocal intelligence before their competitors do. In emerging markets and neighborhoods, timing is everything. The brands that act on early signals capture the opportunity; those that wait inherit the competition.
But speed alone is not enough. The strongest insights emerge when global coverage is paired with hyperlocal precision. Large datasets can obscure important outliers, while purely local views can exaggerate short-term noise. The ability to shift seamlessly between macro and micro perspectives enables teams to separate meaningful trends from anomalies. Consistent, comparable international datasets unlock advanced use cases such as predictive modeling, trend forecasting, and risk-adjusted investment analysis. They enable global teams to operate from a shared foundation of truth, ensuring strategies scale without losing relevance at the local level.
When organizations rely on validated, market-specific data, business outcomes improve across the board. Expansion becomes more targeted, marketing more efficient, and competitive positioning more defensible.
Real-World Scenarios: Where Localization Changes the Outcome
Consider a QSR brand planning to expand into Saudi Arabia. On the surface, demand appeared strong based on external signals. Localized category analysis, however, revealed stagnation in the burger segment and rising demand for healthier fast-casual options. With that insight, the brand shifted its entry strategy, reducing risk and improving its odds of success.
In another case, a spirits brand evaluating expansion into South Africa saw promising population growth at a national level. Hyperlocal intelligence told a different story. Growth in wine bars, breweries, and alternative alcohol categories signaled a shift in consumer preference. Without that localized view, the brand would have entered the market misaligned with demand.
Localized intelligence also reveals competitive saturation and whitespace. It can highlight underperforming competitor locations that should be avoided, or emerging neighborhoods where demand is growing and first-mover advantage still exists.
Closing the Localization Gap
To future-proof global strategies, decision-makers must move beyond high-level assumptions and invest in hyperlocal, multi-source intelligence that reflects how markets actually behave. Dataplor is built for exactly this challenge. Our data is validated through AI and human review, updated weekly, and standardized across markets so global teams can compare regions without reconciling inconsistent sources. We cover merchants and POIs in markets that traditional providers leave blank, and our privacy-safe infrastructure integrates cleanly across CRM, analytics, and planning systems.
The result is local intelligence that scales and gives teams the confidence to expand, compete, and allocate resources wherever opportunity exists. Talk to us today.
The Global Location Intelligence Platform Behind Your Next Promotion
Christine Detris
• Mar 03, 2026 • 5 min read
Inside growing organizations, big questions surface constantly. Where should we expand next? Which markets are saturated? How strong is competitor presence outside North America? What does commercial activity actually look like on the ground?
The answers to those questions influence capital allocation, hiring plans, and long-term strategy. And they often fall on teams that are expected to deliver clarity quickly.
Professionals who consistently deliver confident, defensible answers are those who get noticed. Visibility builds trust with executive teams, and trust leads to larger initiatives, broader ownership, and career acceleration.
The challenge is not identifying the questions, but accessing the right intelligence to answer them with clarity and conviction.
Global Expansion Decisions with Incomplete Visibility
Yet the data supporting these decisions is frequently fragmented.
Many organizations remain heavily concentrated in domestic markets, as a result of global data being inconsistent or difficult to validate. Some datasets are incomplete or outdated; others lack transparency in how the data is sourced and refreshed. Filtering and segmentation capabilities can also be limited, restricting how deeply teams analyze markets. And even when the data is available, integrating it into existing workflows often requires manual effort.
In the end, you’re still responsible for strategic answers, but without the infrastructure to confidently stand behind them. That uncertainty shows. Recommendations become cautious. And cautious recommendations rarely earn visibility with leadership or open doors to bigger opportunities.
Stitching data together manually may fill gaps temporarily, but it doesn’t create a scalable foundation. A centralized platform does.
Most professionals responsible for expansion and strategy are not engineers. They’re real estate leaders, investment analysts, and go-to-market teams who want the ability to explore markets without submitting technical requests or waiting on manual exports.
A global location intelligence platform removes that dependency. It provides a structured, standardized environment where users can analyze and visualize global POI data directly. Instead of relaying information from multiple sources, you can generate insights yourself, apply filters, compare regions, and validate assumptions in real time.
That independence changes how you show up in the room. When you can walk into leadership meetings with clear validation and well-supported analysis, you move from reacting to requests to influencing direction. And in growing organizations, the people who shape direction are the ones promoted to lead it.
From Insight to Influence
Dataplor’s Global Platform delivers comprehensive, standardized places (i.e. point-of-interest) coverage across hard-to-source markets well beyond North America. Designed for both technical and non-technical users, the platform scales with international growth and reduces the friction that often slows strategic momentum.
Specifically, the platform empowers you to:
Validate market opportunities with speed: Assess commercial density, brand presence, and category distribution across countries within a single environment.
Map competitive landscapes dynamically: Compare competitor footprints across regions using standardized global taxonomies.
Optimize go-to-market strategy in real time: Align expansion, territory planning, and partnership efforts with up-to-date, structured intelligence.
Enable cross-functional visibility: Share consistent data across investment, strategy, operations, and executive teams.
Executives respond to clarity, speed, and confidence. When you consistently bring those qualities into strategic conversations, you stop being viewed as a contributor and start being recognized as a strategic leader.
The right platform does more than power your team’s growth. It accelerates your own.
Seeing the Real World Clearly: How Financial Services Are Using Location Data to Find Alpha
Kayla Kauffman
• Feb 12, 2026 • 5 min read
Financial services firms, particularly hedge funds, asset managers, and diversified investment platforms, are operating in an environment defined by speed, complexity, and shrinking informational edges. Traditional financial disclosures are delayed. Survey data is noisy. In many geographies, reliable, high-frequency indicators of real economic activity are hard to come by.
Across the industry, one theme has become increasingly clear: firms are looking for better visibility into what’s happening in the real world—earlier and with greater confidence—to make smarter, lower‑risk decisions.
Recent conversations with financial services teams at BattleFin Discovery Day reinforced how central this challenge has become and why alternative, location‑based data is playing a growing role in sourcing alpha, monitoring risks, and validating strategies.
While strategies vary across equity, credit, macro, and multi‑asset teams, many of the underlying goals and constraints are shared.
1. Finding Alpha Before It’s Obvious
Markets move quickly, and widely available data tends to be priced in just as fast. Firms are under pressure to identify signals that capture real‑world momentum, such as changes in consumer demand, business expansion or contraction, and geographic shifts, before they show up in earnings calls or macro reports.
2. Understanding Consumer Behavior at Scale
Consumer-facing sectors remain critical across equities and credit, yet understanding how people actually behave across regions, brands, and markets is difficult. Aggregated national statistics often miss local dynamics, while company‑reported metrics lack granularity and timeliness.
3. Monitoring Geographic and International Exposure
As portfolios globalize, blind spots grow. Many teams expressed difficulty monitoring private companies, international brands, or under‑covered markets where traditional datasets are thin or inconsistent. This makes it harder to assess true exposure, especially during periods of macro or policy-driven disruption.
4. Validating Ideas Without Reinventing the Wheel
Teams want to move quickly without building bespoke models or stitching together fragile datasets from scratch. Clean, research‑ready data increasingly matters as much as novelty.
5. Managing Risk With Real‑World Signals
Beyond alpha generation, financial firms focus on downside protection: identifying early warning signs of stress, monitoring footprint changes, and understanding how shifts in physical presence or demand can signal deterioration or recovery before financial stress becomes visible.
Why Physical‑World & Foot‑Traffic Data Matters
Location intelligence and foot‑traffic insights have moved from “nice to have” to necessary for decision-making.
Visitation trends reveal demand momentum at brands, venues, and sectors
Mobility changes capture expansion, contraction, and strategic shifts
Geographic patterns highlight regional exposure and local risk
Competitive dynamics emerge through peer‑level and share‑of‑visits analysis
Crucially, these signals operate outside the accounting cycle. They reflect what consumers and businesses are doing, not what they later report.
However, the value of this data depends heavily on quality. Accuracy, historical depth, and consistent mapping—from individual points of interest to brands and entities—are recurring points of discussion. Without these foundations, even the most promising alternative dataset can introduce more noise than signal.
From Raw Signals to Usable Intelligence
Another recurring theme is usability. Financial services teams are increasingly sophisticated data consumers, but they don’t all use it the same way.
Some teams evaluate alternative data as features for systematic models. Others rely on it for bespoke research, thesis validation, or event‑driven diligence. Many organizations now support both, often across multiple desks, strategies, and geographies.
This creates demand for data that is:
Standardized, so multiple teams can use it consistently
Flexible, supporting raw events, aggregated indices, or both
Governance‑friendly, with clear documentation, versioning, and delivery reliability
Global, enabling cross‑country comparisons rather than isolated regional views
In practice, this means moving beyond isolated POI datasets toward structured brand‑ and entity‑level intelligence that can plug directly into research pipelines, backtests, and monitoring frameworks.
The Importance of Global Breadth
One of the most consistent gaps identified was international coverage. While U.S. consumer data is relatively saturated, many firms are increasingly focused on international opportunities, including global equities, multinational issuer surveillance, and macro nowcasting.
Monitoring demand trends across countries and metros
Comparing activity momentum across markets
Evaluating multinational exposure with geographic precision
Identifying growth or stress in under‑covered regions
Without consistent global coverage and clean entity mapping, it becomes difficult to generalize signals, test robustness, or confidently deploy strategies across regions.
Turning Real‑World Data Into Durable Advantage
The firms making the most progress with alternative data aren’t just chasing novelty. They are building durable infrastructure: datasets that multiple teams can trust, reuse, and extend over time.
When physical‑world data is accurate, well‑structured, and globally consistent, it supports a wide range of use cases:
Early detection of demand inflections and competitive shifts
Risk and diligence workflows for equity and credit
Macro and sector‑level activity monitoring
More confident thesis validation through real-world signals on foot traffic trends
In a market where informational edges decay quickly, the ability to see real economic activity clearly and act on it confidently has become a defining advantage.
Thoughtful application of location intelligence doesn’t replace traditional analysis. It strengthens it, grounding financial decisions in what’s actually happening on the ground.
And increasingly, that real‑world clarity is what separates reactive strategies from resilient ones. Connect with the Dataplor team to see how research-ready location intelligence can help your team make more informed, lower-risk decisions.
Turn Global Location Data Into Confident Decisions
Kayla Kauffman
• Feb 05, 2026 • 4 min read
Location intelligence has become foundational to how organizations evaluate markets, allocate capital, and plan for growth. From expansion planning to risk assessment, understanding where activity happens in the real world increasingly shapes strategic decisions. Yet despite the volume of data available today, many teams still struggle to translate location data into clear, actionable insight.
The challenge is not access. It is usability, consistency, and global reliability.
Dataplor’s Global Platform was built to solve that problem. It gives teams a clearer, faster way to see where places are, and how people move through the physical world to understand market operations at global scale.
Why Location Intelligence Needs to Evolve
As companies expand into new regions and rely more heavily on location-driven data, existing tools often fall short.
Global coverage breaks down outside major markets.
Data quality varies by region and category.
Analysis requires stitching together disconnected datasets.
Insights take too long to synthesize to inform real-time decisions.
These gaps create uncertainty, and in competitive markets, uncertainty translates directly into missed opportunity.
Our platform brings accurate global places data and foot traffic insights into a single, intuitive experience designed for how teams actually work today. It replaces fragmented workflows with a unified view of markets that supports faster and more confident decision-making.
Built for Scale & Accessibility
Dataplor’s Global Platform is designed to be powerful without complexity.
Business, strategy, and analytics teams can explore data directly without writing queries or managing multiple tools. And importantly, underneath the hood our platform is backed by Dataplor’s continuously refreshed global dataset, consistent schema, and privacy-first approach to foot traffic data.
This combination ensures teams can trust the insights they’re drawing from the data, regardless of region and across diverse use cases.
A Visual Approach to Market Intelligence
The platform centers on an interactive global map that allows users to explore markets visually, rather than through static tables or rigid dashboards.
Teams can move fluidly from a global view to regional comparisons and down to individual locations. They can analyze brand presence, category density, visitation trends, and market patterns without needing technical expertise or specialized tooling.
This visual approach matters because most location questions are exploratory by nature. The platform is designed to support investigation, comparison, and iteration, making it easier to uncover patterns, validate assumptions, and refine strategy.
How Leading Teams Use the Platform
The Platform is designed to support a wide range of industries and decision types. In practice, teams use it to answer questions like these:
Consumer Packaged Goods (CPG)
Where does our retail coverage actually exist, and where are we missing opportunities?
For CPG teams, distribution strategy depends on understanding where independent and regional retailers truly operate, especially outside large national chains.
The platform helps teams map retail presence across markets, identify retail whitespace based on store category coverage, and compare store category density across regions and countries. This visibility allows teams to prioritize expansion based on real-world activity rather than incomplete or outdated lists.
Commercial Real Estate (CRE)
How do locations perform, and what signals indicate future growth?
Commercial real estate teams need to understand both where properties are located and how those locations function within their surrounding environment.
Using the platform, teams analyze tenant mix, foot traffic patterns around assets and corridors, and market dynamics across regions. This makes it easier to compare opportunities consistently and spot early signals of neighborhood change.
Financial Services
What is happening on the ground before it impacts operations and shows up in financial reporting?
The platform allows teams to track physical expansion or contraction of brands, validate investment theses using inputs like visitation trends, and monitor activity in emerging or underreported markets. These insights help ground financial analysis in observable market behavior.
A Clearer View of the World
Markets are constantly changing. Stores open and close. Consumer behavior shifts. And new opportunities emerge faster than traditional data sources can capture.
Our platform helps teams see those changes sooner, understand them more clearly, and act with confidence.
By turning global complexity into an intuitive visual experience, the platform enables better questions, faster insight, and stronger decisions.
Geospatial Data: What It Is and Why Your Business Needs It
Geoff Michener
• Jan 30, 2026 • 7 min read
In today’s data-driven world, location data has become a crucial asset for staying competitive. From optimizing marketing campaigns to improving site selection, the ability to understand and utilize geospatial data can transform the way your business operates. Businesses across numerous industries use location information to enhance decision-making, predict customer behavior, and streamline operations.
In simple terms, geospatial data refers to information linked to a specific place on the surface of the earth. This data can consist of points of interest (POI), which provide geographic coordinates such as latitude and longitude, along with additional attributes and details on a given place. If you want to access valuable insights like POI data, providers like dataplor offer comprehensive location data to help you gain a competitive edge.
A study revealed that the geospatial data industry is growing rapidly, with the global GIS market projected to reach $14.5 billion by 2025.
In this article, we’ll review what geospatial data is, how spatial analysis works, and why this data is critical for business. Whether you’re looking to optimize site selection, improve real-time marketing strategies, or enhance customer engagement, geospatial insights are the key to success.
What is Geospatial Data?
Geospatial data refers to information tied to a specific location on the earth’s surface. This data comes in various formats and is crucial for gaining insights about locations. Understanding the spatial relationships between different places allows you to make more informed decisions.
By linking geographic locations to real-world data points, geospatial data helps you visualize trends effectively with the help of data visualization tools.
Key Components:
1) Spatial Data:
This component represents the physical location of a place, defined by precise geographic coordinates such as latitude and longitude. These coordinates enable the accurate pinpointing of locations on Earth, making spatial data essential for location analysis.
For example, point clouds, polygons, and vector data are common spatial elements used to define the boundaries of a geographic area, whether it’s a business, landmark, or other point of interest. Additionally, raster data represents information in a grid format with pixels, often used in satellite imagery for analysis.
2) Attribute Data:
While spatial data answers the “where?” question, attribute data provides additional details by addressing the “what?” of a location. This includes important information such as the type of business, address, and operating hours.
For businesses utilizing Point-of-Interest (POI) data from providers like dataplor, attribute data offers essential context for making informed decisions about site selection and location optimization. Here are some examples of how geospatial data is used across various industries.
Examples of Geospatial Data:
Geospatial data is widely used across industries to support operations and strategy. Here are a few examples:
Point-of-Interest (POI) data: This type of geospatial data is essential if you’re aiming to gather insights about locations such as stores, restaurants, offices, and more. For instance, dataplor’s comprehensive POI data allows you to analyze the demographics and real-time updates of business locations, helping you optimize your operations.
Satellite Imagery: Used for a range of applications, from urban planning to environmental monitoring, satellite imagery gives you a bird’s eye view of large geographic areas. It is often processed using GIS data and tools like ArcGIS for detailed analysis.
What is Spatial Analysis?
Spatial analysis is the process of examining, interpreting, and extracting actionable insights from geospatial data. By leveraging location data and advanced algorithms, you can uncover hidden patterns, trends, and relationships that may not be apparent in traditional datasets.
This approach allows you to make more informed decisions based on spatial relationships, enhancing everything from market analysis to customer targeting.
How does it work?
Spatial analysis leverages geospatial data to assess the relationships between various geographic points. By utilizing Geographic Information Systems (GIS) software such as ArcGIS and other analytical tools, you can perform detailed examinations of locations, analyze spatial relationships, and extract valuable insights to support decision-making.
Whether you’re examining land usage patterns or assessing the proximity of competitors, spatial analysis serves as a powerful tool for optimizing your business operations. Data collected through APIs and data sources like ESRI can support these analyses, providing the insights needed to support your strategies.
Here are some examples of spatial analysis techniques:
1. Hot Spot Analysis:
This technique allows you to identify areas with a high concentration of specific types of geospatial data. For example, if you’re looking to find optimal locations for new stores or distribution centers, you can use hot spot analysis to determine which geographic location offers the most promising opportunities.
Many businesses, like Walmart, use big data combined with geospatial information to identify these patterns.
2. Proximity Analysis:
This method evaluates the distance between different POIs, helping you understand your location in relation to your competitors. You can, for example, use proximity analysis to assess how close your store is to your suppliers or potential partners, ensuring that its location is strategically advantageous.
Why is Geospatial Data Important for Businesses?
As businesses increasingly rely on data-driven strategies, geospatial data has become a vital resource for gaining a competitive edge. Recognizing how physical locations and their surrounding environments impact your business operations can result in more informed decision-making and better resource allocation.
Whether you’re refining your real estate strategy or optimizing supply chain logistics, geospatial data plays an essential role in today’s data management and decision-making landscape.
Make Data-Driven Decisions
In today’s competitive business landscape, making decisions based on reliable, actionable data is essential for success. Geospatial data empowers businesses to make data-driven decisions by providing valuable location insights that enhance strategic planning, resource allocation, and operational efficiency.
In industries like commercial real estate, integrating geospatial technology enables professionals to make informed decisions in investment research, risk assessment, and market and competitor analysis. These decisions are supported by precise geographic data, ensuring a comprehensive understanding of the operational landscape.
With tools like GIS software, you can visualize geographic data, making it easier to interpret complex data models and make informed decisions in real-time. Research shows that geospatial technology is widely used in urban planning and resource management to support smarter decision-making.
Improved Market Analysis and Site Selection
By utilizing POI data, Starbucks has refined its store placement strategy by targeting urban and suburban areas with high customer potential. Through geospatial data analysis, they were able to pinpoint areas with the greatest opportunity and gain a deeper understanding of their customers. Enriching their data ultimately helped them expand to over 60 countries with approximately 19,767 company-operated and licensed locations.
By targeting high-traffic and high-visibility areas, Starbucks has optimized its global reach and solidified its dominance in the coffeehouse industry. It currently holds a 36.7% market share in the U.S. coffeehouse market, depicting the effectiveness of its geospatial data-driven site selection strategy.
Enhancing Property Insurance With Geospatial Data
Tensorflight, a leader in property analytics for the insurance sector, partnered with dataplor to improve the accuracy of data used in property insurance assessments. By leveraging our POI data, they were able to improve geocoding accuracy, which allowed them to generate more accurate building replacement cost estimates and better classify building occupancy types.
These advancements have empowered Tensorflight to provide more reliable and actionable data to their insurance clients, transforming how real estate and property insurance decisions are made.
Unlock the Power of Location Data with dataplor
In today’s competitive landscape, leveraging geospatial data and spatial analysis empowers you to make informed decisions, improve operational efficiency, and enhance customer engagement. Whether optimizing market strategies, refining site selection, or tailoring your marketing campaigns, geospatial data helps you understand the key geographic elements that drive success.
With dataplor’s Point-of-Interest (POI) data, you can gain the insights needed to make smarter location-based decisions. Learn how dataplor’s comprehensive data helped companies like Wolt and FLO® to transform their business. Request a sample today and experience the powerful benefits firsthand.
How Location Intelligence Transforms Business Strategy
Geoff Michener
• Jan 30, 2026 • 3 min read
Technology is transforming the way companies are growing, especially through the use of location intelligence. At Dataplor, we empower many of the world’s largest companies by delivering real-time, accurate location data. This valuable geographic information can be used to fuel advanced technologies and data systems across a number of industries.
By integrating diverse data sources into their business intelligence systems, companies can elevate their location analytics and data visualization capabilities. This enriched business data enables more effective marketing campaigns and resource optimization, ultimately leading to better decisions across the board.
What is Location Intelligence?
Location intelligence is gained by analyzing geospatial data collected from various sources like GIS, satellite imaging, drones, census reports, and more. While this process can be time-consuming and complex, Dataplor simplifies it by streamlining the data collection and delivering relevant point-of-interest data directly to your business. With this information in hand, your data analysis team can easily identify marketing and retail opportunities as well as new possibilities across various industries.
How Location Intelligence Works
Location intelligence is often mistaken for simple GPS tracking, but true location intelligence is far more powerful. Rather than relying on a single data source, it uses a layer-by-layer approach to reveal how people interact with the physical world.
At its foundation is raw geospatial data, which provides precise information about where places and movement exist in space. This data is then enriched with additional layers, such as demographic insights, point of interest (POI) data, and foot traffic patterns, to add critical context. When these layers are combined, businesses can move beyond static maps and begin to understand real-world behavior.
By analyzing these interconnected datasets together, location intelligence transforms location data into actionable insights that support smarter planning, forecasting, and decision-making.
The Benefits of Location Intelligence for Businesses
Location intelligence helps businesses reduce risk, uncover growth opportunities, and make data-driven decisions with confidence. Its strategic value spans site planning, marketing, operations, and competitive analysis.
Precision Site Selection
Reduce the risk of brick-and-mortar investments by identifying locations with strong foot traffic and the right target demographic. Location intelligence replaces guesswork with real-world behavioral data to support smarter expansion decisions.
Enhanced Customer Understanding
Go beyond basic demographics to understand how customers move through and interact with physical spaces. These insights reveal behavioral patterns, preferences, and opportunities that traditional data sources often miss.
Operational Efficiency & Logistics
Improve supply chain planning and delivery efficiency by analyzing real-world movement patterns, traffic conditions, and environmental factors. Location intelligence helps streamline operations while reducing costs and delays.
Hyper-Local Marketing
Increase marketing effectiveness by tailoring campaigns to the unique behaviors and needs of specific neighborhoods. Hyper-local insights enable more relevant messaging, higher engagement, and improved conversion rates.
Competitive Benchmarking
Monitor competitor foot traffic and market share trends to understand performance in real time. These insights help businesses identify threats, measure impact, and capture new opportunities faster.
Common Use Cases of Location Intelligence Solutions
Location data holds the key to unlocking smarter business strategies across various sectors, including retail, healthcare, real estate, insurance, and more. This data-driven approach facilitates informed decision-making and maximizes revenue through several use cases:
Mapping: Location intelligence enables GIS and other spatial data technologies to create intuitive dashboards and visualizations, helping to identify patterns and potential relationships within and between locations.
Site Selection: Insights from geographic data allow organizations to assess the pros and cons of potential real estate locations, optimizing their site selection for competitive advantage.
Competitor Investigation: Gain a deep understanding of the competition to identify market opportunities and areas to target.
Supply Chain Optimization: Use location data to optimize logistics, distribution, and inventory management.
Risk Assessment: Identify potential risks by considering factors like ROI forecasting and fraud prevention.
Customer Segmentation: Customize product promotions and offerings tailored to consumer demand based on location data.
Real Estate Valuation: Determine property values based on location-specific factors like proximity to amenities, schools, and transportation.
Urban Planning: Forecast which areas are most likely to see substantial growth, and predict the best areas for development.
Comprehensive Location Insights From Dataplor
As a leading provider of global location intelligence, Dataplor utilizes advanced machine learning, image recognition, and artificial intelligence technologies to examine vast arrays of data across more than 250 million locations in over 200 countries and territories. This commitment to quality ensures that you can make well-informed, data-driven business decisions. Our international team of analysts and human validators leverage their local experience and language fluency to maintain high data accuracy, reduce errors, and identify duplicates in real-time.
Reach Out to Our Experts for More Information
Partner with a provider committed to giving you a competitive edge. Dataplor is your global partner in location intelligence, helping your business optimize customer experiences, improve supply chain functionality, and enhance financial services through our accurate Point-of-Interest (POI) data. Our industry-leading quality control procedures ensure you receive highly accurate and relevant location data that you can trust. Contact our experts to learn how our data can strengthen your business strategy and support data science initiatives, urban planning, CRM, IoT applications, pricing strategies, and more.
Building Smarter, Safer Cities: Leveraging Location Intelligence Data
Geoff Michener
• Jan 30, 2026 • 6 min read
Key Takeaways
Location intelligence helps cities understand real-time movement, optimize services, and make data-driven decisions.
Urban planners use geospatial insights to reduce congestion, improve emergency response, and allocate resources effectively.
Combined with other datasets (like census or weather data), location intelligence offers a full picture of urban dynamics.
Real-world examples include smarter transit in London, infrastructure planning in NYC, and population forecasting in Dubai.
Choosing the right data provider, like dataplor ensures accuracy, privacy compliance, and actionable insights.
Imagine a city that understands, adapts, and responds to its inhabitants’ needs with precision.
No longer a futuristic concept, location intelligence has become the heart of modern urban development, offering unprecedented insights into the complex dynamics of city life.
Cities are more than just collections of buildings and roads. They’re living ecosystems of human movement, infrastructure, and interconnected systems. According to the United Nations, 68% of the world’s population is projected to live in urban areas by 2050, making the need for intelligent, data-driven urban management more critical than ever.
In this comprehensive guide, we’ll explore how location intelligence data is revolutionizing urban planning, public safety, and city management, providing a roadmap for creating smarter, more responsive urban environments.
What is Location Intelligence Data for Urban Environments?
Urban areas are constantly evolving landscapes shaped by human movement, infrastructure, and environmental factors. Location intelligence data provides a structured way to analyze these dynamics, offering city planners and policymakers a comprehensive view of how people interact with urban spaces.
By integrating geospatial data, real-time location analytics, and location-based insights, cities can make data-driven decisions that enhance efficiency and safety.
Location Intelligence Use Cases for Urban Planning
At its core, location intelligence leverages a mix of advanced analytics, artificial intelligence, advanced data science, and geospatial datasets. City officials can use location intelligence to:
Identify high-traffic areas and congestion points: Foot traffic data patterns reveal bottlenecks in transportation networks, aiding in decongestion strategies.
Understand population movement during emergencies: Spatial data combined with real-time analytics helps optimize evacuation plans and emergency response times by analyzing visitation patterns in highly congested areas.
Optimize resource allocation for public services: By integrating location data with business intelligence, cities can deploy resources like police, medical assistance, and transit where they are needed most.
Identify areas with higher risk of incidents: Geospatial datasets combined with crime statistics allow law enforcement to focus efforts on high-risk zones.
While location intelligence provides key insights, its value increases when combined with other business data, such as census bureau statistics, economic indicators, and even weather data for disaster preparedness.
By integrating these diverse data sources, cities can dive deeper into urban challenges and proactively develop smarter, safer environments.
How Location Intelligence Data Enhances City Planning
Urban infrastructure development is no longer a game of educated guesses but a precise science driven by data-driven decision-making.
With location intelligence, urban planners can move beyond broad estimations and base decisions on real-time insights about how people interact with their environment.
Whether it’s optimizing public transit, planning new urban infrastructure, or guiding economic development, location intelligence ensures that urban growth is strategic, efficient, and sustainable.
Smart Resource Allocation for Public Services
Beyond infrastructure, public services such as schools, hospitals, and parks must be strategically placed for maximum accessibility. Location intelligence, when combined with additional city planning data, enables:
School and healthcare facility planning: Cities can predict future demand for schools and hospitals based on existing infrastructure and frequently visited areas..
Emergency services optimization: Analyzing point of interest and mobility data ensures emergency facilities like fire stations and ambulances are placed in high-need areas.
Green space development: Location intelligence helps planners identify areas lacking parks and recreational spaces, promoting balanced urban development.
Zoning and Land Use Optimization
Land use decisions impact everything from residential density to commercial zoning. Geospatial insights enable planners to:
Determine optimal zoning regulations: Balancing residential, commercial, and industrial spaces to promote efficient urban growth.
Prevent overdevelopment and urban sprawl: Identifying areas that require preservation or controlled expansion.
Assess environmental impact: By overlaying air quality measures with location and mobility data, policymakers can prioritize interventions where vulnerable populations are most affected (like schools or hospitals).
Economic Development Strategies
A city’s economic success depends on understanding market trends and consumer behavior. Location intelligence supports:
Site selection for businesses: Analyzing customer movement patterns to identify prime retail locations and boost customer relationship management.
Competitive market analysis: Mapping existing businesses to determine gaps in service and potential investment areas.
Tourism and event planning: Understanding visitor trends to strategically place attractions and facilities.
Other Real-World Location Intelligence Use Cases
Public transportation optimization: Cities like Singapore and London use real-time location data to adjust bus and metro frequencies, ensuring efficient commuter flow.
Infrastructure investment: Road maintenance and expansion projects in New York City are prioritized based on traffic congestion data and wear analysis.
Population growth planning: Fast-growing cities like Dubai and Austin use geospatial analytics to plan new residential zones, schools, and hospitals.
Impact assessment of urban development projects: Location intelligence tools help evaluate the effects of large-scale developments like stadiums, shopping centers, or new highways, ensuring they benefit the community.
Choosing a Location Intelligence Data Provider
With public safety relying on accurate and timely location intelligence, selecting the right data provider is crucial.
Here are the evaluation criteria to consider:
Data accuracy and reliability: Consistently updated and verified datasets to ensure and provide insights
Global coverage and geographic reach: Supporting cross-jurisdictional safety planning for large metropolitan areas
Data privacy and compliance: Ensuring compliance with GDPR, CCPA, and other privacy regulations by using aggregated, anonymized data
Integration with other data sources: Compatibility with existing GIS platforms, emergency dispatch systems, and law enforcement databases.
Customer support and expertise: Ongoing guidance from GIS specialists
Aggregated, anonymized data for privacy protection
Consistent updates to the data directory to reflect real-time global insights
Expertise in analyzing spatial data
The Future of Smart Cities is Location Intelligence
As urban populations continue to grow, the role of location intelligence in city planning and public safety becomes indispensable.
Cities that embrace advanced analytics, artificial intelligence, and real-time geospatial insights will be better equipped to handle emergencies, prevent crime, and optimize resource allocation. In addition, they’ll benefit from:
Improved infrastructure efficiency, such as optimizing road networks, transit systems, and public safety facilities.
Optimized resource allocation in areas such as law enforcement, medical, and emergency resources is used where needed most.
More responsive urban environments by leveraging real-time data to adapt to changing urban dynamics.
Are you ready to transform your city’s potential with location intelligence?
From Signal to Strategy: How Location Intelligence Is Evolving for Financial Markets
Kayla Kauffman
• Dec 18, 2025 • 4 min read
Alternative data has entered a new phase. At the Neudata NY Winter Data Summit, discussions with financial services and investment professionals pointed to a clear shift: location intelligence is no longer evaluated as an experimental dataset, but as a core input into how firms understand markets, companies, and consumer behavior in near real time.
What stood out most wasn’t a single new use case or dataset, but a broader shift in how investment professionals are thinking about scale, specificity, and signal quality when it comes to location-based data.
The Rise of Investment-Grade Location Data
Across hedge funds, asset managers, and private equity firms, there is a growing expectation that alternative data must map cleanly to financial instruments. High-level foot traffic trends or regional summaries are no longer sufficient on their own.
What investors are asking for instead is location data that maps cleanly to how capital is actually deployed. That means:
Ticker-aligned reporting that ties real-world activity directly to public equities
Open and close visibility to distinguish temporary volatility from structural change
These capabilities allow real-world activity to be translated into insights that are comparable across companies, regions, and time. In short, location intelligence is being held to the same standards as traditional financial data—if it can’t support rigorous analysis, it won’t be used.
Global Coverage Is the Differentiator That Matters Most
While U.S. data remains table stakes, what generated the most interest at the summit was coverage beyond domestic markets. Investors increasingly recognize that alpha is harder to find where everyone is looking and that international markets often offer earlier, indicative signals.
Global location intelligence enables teams to:
Identify leading indicators in undercovered markets
Monitor multinational brands consistently across regions
Support portfolios that span North America, Europe, and beyond
Risk Assessment and Fraud Detection
Location intelligence is increasingly being used to support risk assessment and fraud detection across financial services.
By adding a geographic layer to traditional data, firms can better understand regional exposure, monitor shifts in demand that may signal emerging risk, and evaluate performance at a more granular level. Location data also helps surface anomalies that can indicate potential fraud or operational issues such as unexpected changes in activity patterns or mismatches between reported and real-world behavior.
As a result, location intelligence is not only helping firms identify opportunities, but also providing an additional lens to manage downside risk and detect issues earlier across portfolios.
Complementary Data, Not Just More Data
Another recurring theme was the reality that most investment organizations already have a wealth of data. The challenge is not access, it’s integration. Teams are actively looking for datasets that complement existing models rather than duplicate them. Location intelligence stands out because it provides real-world validation of trends that may first appear in financials, earnings calls, or consumer data.
Used correctly, it can serve as:
A leading indicator ahead of reported revenue
A real-world check on company narratives and performance claims
A way to understand how broad market trends play out on the ground
In this sense, geospatial data is increasingly viewed as connective tissue that bridges qualitative narratives and quantitative outcomes.
From Public Markets to Private Capital
These dynamics extend well beyond public equities. Private equity, private credit, and real estate investors are increasingly applying location intelligence to due diligence, portfolio monitoring, and geographic expansion analysis.
As portfolios grow more international, consistent visibility across markets becomes critical. Location data helps private investors understand how assets perform on the ground, not just in financial models—particularly for consumer-facing businesses with physical footprints.
Why This Moment Matters
The overarching message from Neudata was clear: location intelligence has matured. Investment teams expect data that is globally consistent, analytically rigorous, and ready to integrate into existing workflows.
This is exactly where Dataplor supports financial services organizations. With global places and foot traffic coverage, consistent brand and category labeling, and visibility into store openings and closures, Dataplor helps investors translate real-world activity into investment-ready signals. For teams trading equities, managing global portfolios, or evaluating expansion strategies, this means having consistent signals they can use across regions, brands, and asset classes to make faster and better decisions.
As alternative data becomes more embedded in investment processes, the ability to rely on scalable, investment-grade location intelligence is no longer a differentiator. It is a requirement.
If you’re evaluating how global location intelligence and foot traffic data can function as leading indicators within your investment process, the Dataplor team is here to support.
