Store Location Data: What It Is, How It Works, and Where to Get It

A street address tells you how to reach a business. Store location data tells you everything else worth knowing about it.

This distinction matters more than it sounds. Whether you’re a retail analyst sizing up a new market, a commercial real estate firm advising on site selection, or a fintech company trying to match card transactions to real merchant locations, a simple address is where the analysis begins, not where it ends. Comprehensive store location data and business location data fill in the rest: the building footprint, the surrounding POI landscape, the classification codes, the operating hours, and the broader geospatial context that turns raw coordinates into actionable intelligence.

This guide covers what store location data is, what attributes it includes, how different industries use it, what challenges to watch for, and where to get it.

What Is Store Location Data?

Store location data refers to detailed information that identifies and describes the physical locations of retail stores, commercial establishments, and other non-residential businesses. At its most fundamental level, it anchors each location to a geographic coordinate pair, latitude and longitude, and then layers on a set of descriptive attributes that characterize what that location is, what it does, and how it operates.

The category sits inside the broader umbrella of point of interest (POI) data. POI data covers any non-residential place people might visit or interact with, from a coffee shop to a hospital to a transit hub. Store location data is the retail and commercial subset of that universe, though in practice the two terms are often used interchangeably across industries.

What makes this data category particularly valuable is its dynamic nature. Businesses change. Hours shift, locations move, brands close or expand, pricing changes relative to the competitive environment. A street address from six months ago may still be accurate. The richer attribute layer around it probably is not. That’s why freshness is a core quality dimension for store location data, and why providers like SafeGraph update their Places dataset on a monthly basis to capture these changes.

What Does Store Location Data Include?

Store location data is not a single attribute. It’s a structured set of fields that together describe a business’s identity, location, and operational profile. Here’s what a well-built dataset typically covers:

Core location fields: Name of the establishment, street address, city, state, country, postal code, and geographic coordinates (latitude/longitude) for precise mapping.

Classification and category codes: Industry classification identifiers such as NAICS codes that standardize what type of business a location is. These are critical for segmentation, filtering, and competitive analysis.

Operating hours: Standard open/close times by day of week. Some datasets capture extended attributes like holiday hours or temporary closures.

Brand and chain hierarchy: Whether the location belongs to a parent brand, which matters enormously for retail and QSR competitive analysis. Knowing that a location is a franchise of a national chain versus an independent operator changes how you interpret what’s around it.

Building footprint geometry: Polygon data that maps the physical shape and boundary of the building or parcel. SafeGraph’s Geometry product specifically addresses this, providing building footprints that allow for more precise spatial analysis and visit attribution. For a deeper look at how footprint data works and why it matters, see our guide on building footprints and geospatial analysis.

Address-level detail: Structured postal data including suite numbers, floor indicators, and in-building unit identifiers for multi-tenant properties. SafeGraph’s Address product product handles this layer specifically.

Unique persistent identifiers: Stable IDs that let you track a location over time, even through name changes or ownership transitions. Without these, longitudinal analysis becomes unreliable.

Beyond these core fields, store location data is frequently enriched by joining it to other data types from the broader geospatial ecosystem, which we cover in the next section.

How Does Store Location Data Work?

The core mechanism is straightforward: a geographic coordinate pair pins each store to a precise point on earth, and a set of structured attributes describes what exists at that point. What makes the data genuinely useful is how it integrates with the surrounding geospatial context.

Store location data doesn’t exist in isolation. It gains analytical power when layered with complementary data types. Here’s how those layers typically stack:

POI data provides the surrounding competitive and commercial environment. Knowing that a particular retail location sits inside a dense cluster of complementary businesses versus an underserved strip tells you something meaningful about its market position. This is foundational for trade area analysis and site selection.

Geometry/polygon data moves beyond the point coordinate to capture the actual shape of the building or parcel. This matters for understanding a store’s physical footprint, modeling occupancy, and reducing visit attribution errors when multiple businesses share a building or complex.

Foot traffic data from third-party providers (sourced through vendors like Veraset or Unacast) measures the volume of visits to a location over time. When joined to store location data, it enables understanding of how consumer behavior maps onto a retail environment.

Demographic data describes the population surrounding a location, including age, income, household composition, and other segmentation attributes. Combining store location data with demographics is how you build trade area profiles and understand who your customers actually are.

Address data structures the postal and routing information for each location, which matters for delivery logistics, address validation, and building directory-level precision for multi-tenant spaces.

The result of joining these layers is a rich, multidimensional picture of each store or business location that supports everything from basic mapping to complex market modeling. For a broader look at how these data types interconnect, see our guide on the uses of geospatial data.

How Different Industries Use Store Location Data

Retail

Retailers use store location data to understand their competitive landscape, optimize their physical network, and make better expansion decisions. By analyzing which brands and store categories are present in a given trade area, a retailer can identify gaps (where demand exists but supply doesn’t) and avoid oversaturated markets.

Retail site selection has historically relied on intuition and broad census-level data. Store location data makes it quantitative. A retailer evaluating a new market can overlay competitor locations, assess the density of complementary businesses nearby, and understand the demographic composition of the surrounding population before signing a lease.

Beyond site selection, retailers use store location data to power logistics decisions, from delivery routing to inventory positioning, based on where stores are physically situated relative to distribution infrastructure.

Commercial Real Estate

Commercial real estate investors and developers use store location data to evaluate the commercial viability of potential sites and to understand the tenant mix of existing assets. Before committing to a development project, teams need to understand what businesses are already operating nearby, whether the area is growing or contracting, and how current tenant performance compares to comparable locations elsewhere.

Property management teams also use this data to forecast tenant renewal likelihood, plan facility services based on traffic expectations, and benchmark a property’s commercial environment against competing developments.

Financial Services and Fintech

Banks use store location data for two distinct purposes. On the brick-and-mortar side, retail banks analyze store locations to identify underserved markets and position new branches strategically relative to competitors. On the transaction enrichment side, fintech companies use store location data to match card transactions to specific merchant locations, improving the clarity and usability of financial data for consumers and developers.

Retail banking site selection, in particular, requires understanding what businesses are present in a trade area, which census block groups feed into it, and what the demographic composition of those areas looks like. Store location data provides the foundational commercial layer that makes this analysis possible.

Insurance

For insurance carriers, where a store is located and what the surrounding commercial environment looks like can directly influence risk modeling. High-traffic retail locations in dense commercial corridors carry different risk profiles than isolated single-tenant properties. Store location data feeds into catastrophic risk modeling, underwriting workflows, and portfolio exposure analysis.

Quick-Service Restaurants (QSR)

Restaurant chains use store location data to identify expansion opportunities where conditions, nearby demographics, and competitive presence align with their brand’s target customer. Combining store location data with broader POI intelligence lets QSR operators evaluate potential locations against a detailed picture of what already exists in that trade area.

How Companies Use Store and Business Location Data

Trade Area Analysis

Store location data enables businesses to map and analyze the commercial composition of a specific trade area. Which business categories are present? Which are underrepresented? Is the area trending toward more food and beverage density, or toward services? These questions have real answers when you can see every commercial location in a defined radius.

By understanding what already exists in a market, companies make better decisions about entering, expanding into, or exiting a geography. The analysis doesn’t require building custom data infrastructure. It requires access to a POI dataset with sufficient completeness and freshness to be trusted. Our guide on the benefits of trade area analysis walks through exactly how this works in practice.

Site Selection and Deselection

Choosing where to open a new location is only half the challenge. Knowing which locations to avoid, because of oversaturation, poor accessibility, or adverse competitive dynamics, is equally important.

Store location data operationalizes both sides of this decision. On the selection side, teams can identify areas where their store category is underrepresented relative to available demand. On the deselection side, they can screen out markets where the competitive density is too high or where existing brand presence makes a new opening redundant. 

Customer Demographic Profiling

When store location data is joined to demographic datasets, it becomes possible to build detailed profiles of the populations that surround each location. These profiles inform decisions about product assortment, marketing messaging, store format, and service offerings.

Retailers with national footprints use this approach to tailor individual store strategies to local market conditions rather than applying a blanket national format to every location.

Location-Based Marketing

Store location data enables targeting and attribution for location-based advertising campaigns. By understanding where stores are and which audiences are nearby, marketing teams can design campaigns that reach people in relevant geographic proximity to their locations. For a deeper look at how geofencing intersects with store location data, see our guides on geofencing and location data marketing.

Real-World Results: SafeGraph Customer Case Studies

Plaid: Precision in Transaction Enrichment

Plaid, the financial data connectivity platform powering thousands of fintech applications, needed a way to resolve a persistent challenge: transaction data coming from financial institutions was noisy, ambiguous, and difficult for consumers to interpret. The specific problem was merchant recognition, particularly for smaller local merchants, and accurate pinpointing of transaction locations.

Plaid integrated SafeGraph’s Places dataset to enrich its transaction data with verified merchant location information. The result was substantial. The integration enabled Plaid to match approximately half of all card-present transactions to a precise merchant location, a meaningful jump in the granularity of data available to Plaid’s developer ecosystem. The enrichment also reduced their internal quality assurance overhead, freeing their team to focus on product development. Read the full case study.

Avison Young: Ground-Level Insight for Commercial Real Estate

Avison Young, a global commercial real estate firm, works with clients making high-stakes decisions about where to place offices, industrial facilities, and retail locations. Their clients don’t just want to know if a space is available. They want to know what’s happening around it: transit access, nearby amenities, competitive density.

SafeGraph’s POI data gave Avison Young’s team the ground-level commercial intelligence to answer these questions with specificity. Rather than relying on broad market reports, their advisors could point to exactly which businesses were present in a trade area and what that meant for a client’s location decision. Read the full case study.

Retail Banking: Strategic Branch Placement

A major retail bank used SafeGraph data to move beyond census-level analysis in its branch site selection process. The bank needed to understand what businesses were already present in candidate trade areas, what census block groups fed into those areas, and what the demographic composition of those populations looked like.

By layering SafeGraph’s Places data with demographic information, the bank’s real estate team built a more complete picture of each candidate market, one that revealed both underserved opportunities and markets already well-covered by competing branches. The result was a more disciplined approach to network optimization that reduced the risk of misplaced investments. Read the full case study.

Key Challenges and Best Practices

The Data Freshness Problem

Outdated store location data is not just inaccurate. It’s actively misleading. A location that closed three months ago still appearing in your dataset changes your trade area analysis. A business that changed hours and no longer aligns with your target window looks like a better neighbor than it actually is. For industries that move quickly, like retail and QSR, monthly updates are the minimum viable standard.

Accuracy and Coverage Gaps

Match rates for store location data vary significantly across providers. Many advertise 80% to 95% match rates, but what that figure covers, and what it excludes, matters. Gaps in coverage for smaller cities, non-US geographies, or specific business categories can silently distort analysis. Before committing to a provider, request sample data for the specific geographies and categories you care about and test it against a known ground truth. Our data quality checklist gives you a structured framework for doing exactly that.

Privacy and Compliance

Organizations sourcing any location-related data must be aware of applicable privacy regulations, including GDPR for European data and CCPA for California. This applies especially when store location data is being combined with consumer-level signals. Working with providers that source and structure their data in compliance with these frameworks from the outset reduces your compliance burden.

Getting the Right Infrastructure in Place

Complex store location datasets require appropriate tooling to analyze effectively. Teams without GIS expertise or spatial analysis capabilities often struggle to extract value from raw POI data. Before purchasing data, assess whether your current stack can ingest, join, and query geospatial data at scale. Our guide on geospatial data management covers the infrastructure considerations worth thinking through before you buy.

Best practices to follow:

Set clear objectives before purchasing. Store location data supports many different use cases, and the attributes you need for site selection differ from what you’d need for trade area analysis or transaction enrichment. Knowing your use case up front determines what coverage, freshness, and attribute depth you actually need.

Validate the data before relying on it. Request a free sample, run it against locations you can independently verify, and check both match rate and attribute accuracy before committing to a full dataset.

Plan for integration. Store location data becomes most valuable when joined to other layers: demographics, geometry, address-level detail, or third-party foot traffic from vendors like Unacast or Veraset. Build your data architecture with these joins in mind from the start.

Where to Get Business and Store Location Data

Collecting store and retail location data manually is slow, expensive, and nearly impossible to maintain at scale. Most organizations find that purchasing from a specialized provider is the faster and more reliable path. Here are four strong options.

1. SafeGraph

Cost: Contact for pricing | Free sample: Available

SafeGraph offers one of the most comprehensive global POI databases available, covering millions of businesses across 195+ countries through its Places product. The dataset includes geographic coordinates, business names, category classifications, operating hours, brand and chain identifiers, and persistent unique IDs for longitudinal tracking.

SafeGraph’s Geometry product complements Places with precise building footprint polygons, which reduce visit attribution errors and support more accurate spatial analysis. SafeGraph’s Address product provides structured postal and routing data for address validation, delivery logistics, and in-building precision.

Places data is updated monthly, making it one of the more current offerings in the market. The dataset is widely used for site selection, trade area analysis, transaction enrichment, mapping applications, and location intelligence platforms.

2. CAP Locations

Cost: Contact for pricing

CAP Locations (Competitive Analytics Professionals) specializes in mall and shopping center data, with coverage now spanning 46,000+ centers and 5.75 billion square feet of retail space across the US, Canada, and the UK. Their dataset includes shopping center polygons that are hand-drawn and verified using satellite imagery, in-mall store-level detail, and trade area definitions. They also maintain POI data on retail stores and restaurants, including persistent unique IDs, ownership hierarchies, and store categories. Coverage has expanded to include Great Britain in addition to North America.

3. SMR Research

Cost: Contact for pricing

SMR’s Enhanced Commercial Property Database adds attribute depth that public records often lack: building tenants, approximate square footage, owner contact information, specific property use, and approximate valuations. The dataset is built from over 200 data sources and also includes risk scores for mortgage default, insurance claims, and new buyers, making it particularly useful for insurance underwriting and commercial property risk analysis. Coverage is US-only.

4. CRED iQ

Cost: Enterprise subscription starts at $1,200/month for up to 4 users; customized plans available | Free access: Limited free tier available

CRED iQ focuses on the financial layer of commercial real estate, covering loan details for financed properties across CMBS, SASB, CLO, and GSE Agency loan data, lease terms, tenant financial summaries, and property valuations. The platform covers data on over $2 trillion in commercial real estate transactions. It’s most relevant for banks doing commercial credit risk analysis, CRE investors, lenders, and brokers evaluating acquisition targets or distressed assets.

Provider Comparison Table

Want to go deeper on location data?

Before choosing a provider, it’s worth understanding what separates high-quality POI datasets from the rest. Our guide on POI data providers walks through the key quality dimensions to evaluate. For a broader look at how geospatial data gets sourced and structured, see our guide to geospatial data sources. If you’re evaluating any provider’s data quality specifically, our data quality checklist gives you a framework for that assessment. And for a full breakdown of where to buy location data across all major data types, see our location data buyer’s guide.