What is Store Location Data? Examples, Datasets and Providers
What is Store Location Data?
Store location data refers to information that identifies the physical locations of stores or businesses. This data is valuable for businesses to analyze customer behavior, optimize store placement, and provide personalized services based on geographical proximity.
What Are Examples of Store Location Data?
Examples of store location data include datasets that provide insights into retail locations and their attributes. Key examples include:
- Store Addresses: Exact locations of retail stores, including street names and postal codes.
- Geocoded Data: Latitude and longitude coordinates for mapping and analysis.
- Operational Details: Store hours, available services, and amenities.
- Customer Foot Traffic Data: Visitor counts and patterns at specific stores.
- Catchment Area Data: Geographic regions from which a store attracts customers.
Best Store Location Databases & Datasets
The best store location datasets provide accurate geographic data, operational details, and customer insights for retail analysis. This curated list features the top store location datasets, selected for quality, precision, and trusted providers.
Store Location Data | Home Furnishing & Electronics Store Locations in US and Canada | Comprehensive Market Coverage
CPG Data | Retail Store Location Data | 52M+ POI | SafeGraph Places
Geospatial Data | Places Data | Polygon Data | GIS Data | Store Location Data | Global Coverage
Location & Territory Data |Geospatial, Sentiment (Reviews), Footfall, Business Listings & Store Location | 200 Million+ POI Data Mapped
Factori Geospatial Data | Global | Location Intelligence | POI , Foot Traffic, Store Visit
Ecommerce Data | Store Location Data | Global Coverage | 60M+ Contacts | (Verified E-mail, Direct Dails)| Decision Makers Contacts| 20+ Attributes |
Point-of-Interest Data | Asia/MENA | Monitor Store Openings and Closures for Leading Retail Brands | Business Location Data | Location Data
Grepsr | Comprehensive Dataset of Fast-food Chains' Store (Starbucks, Mcdonalds, Subway, & more) Location
Global Store Location Data | Business Location Data | Places Data: Categorized Branded Retail Locations
Catchment Data | DACH, Netherlands, Italy Places Data | Store Location Data | Business Location Data | Monthly Resolution
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Popular Use Cases for Store Location Data
Store location data is widely used in retail strategy and market planning. Common use cases include retail site selection, helping businesses identify the best locations for new stores based on demographic and geographic data, and catchment area analysis, determining the regions from which stores attract their customers. Another important application is location planning, where businesses optimize their store network to maximize market coverage and minimize overlap.
Store Location Data Attributes
Store location data includes key attributes such as addresses, geographic coordinates, operating hours, contact details, and nearby landmarks. These are the most important attributes:
| Attribute | Description | Example |
|---|---|---|
| Store Addresses | Physical locations, including street and postal codes. | E.g., 456 Elm St, Los Angeles, CA. |
| Geographic Coordinates | Latitude and longitude for precise mapping. | E.g., 34.052235, -118.243683. |
| Operating Hours | Store opening and closing times. | E.g., Mon-Fri: 10 AM - 9 PM. |
| Contact Details | Store phone numbers, emails, or websites. | E.g., (123) 456-7890, info@store.com. |
| Store Categories | Type or classification of the store. | E.g., Grocery, Electronics. |
| Nearby Landmarks | Key points of interest around the store. | E.g., Next to Central Park. |
| Parking Availability | Information about parking facilities. | E.g., Free parking available. |
| Accessibility Features | Details about accessibility for all users. | E.g., Wheelchair ramps available. |
Frequently Asked Questions
How Accurate is Store Location Data?
Accuracy levels can vary, but many providers offer a match rate of 80% to 95% or higher. Accuracy is especially critical for businesses that rely on precise location data for marketing, site selection, or geofencing. For highly accurate data, we suggest choosing providers that offer manual verification or polygon mapping techniques.
How Frequently is Store Location Data Updated?
Store location data is updated at various intervals, ranging from weekly to yearly, depending on the provider and use case. Some datasets even offer real-time updates, which are particularly useful for monitoring changes like store openings and closures. We recommend selecting a provider that aligns with your business needs — for example, if you’re in a fast-moving industry like retail, frequent updates (monthly or even weekly) can ensure you’re working with the most current data.
How is Store Location Data Delivered?
Store location data can be delivered in a variety of formats such as .csv, .json, or .xls, depending on the provider. Delivery methods include cloud storage (S3 buckets), email, or API integration, making it easy to access and incorporate into your systems. For businesses with real-time or continuous data needs, API integration is a highly recommended option.
How Much Does Store Location Data Cost?
The cost of store location data can vary based on several factors, including the dataset’s size, geographic coverage, and update frequency. For example, some pricing models start at $0.10 per record for one-off purchases, while more comprehensive datasets can cost upwards of $30,000 annually. Many providers offer free samples on Datarade, so you can test the data before making a full commitment. We recommend taking advantage of these samples to ensure the data meets your needs.
What Geographic Areas Are Covered by Store Location Data?
Store location data typically provides broad geographic coverage, with some datasets offering information across more than 180 countries. If your business operates internationally, we suggest confirming the geographic coverage of the dataset to ensure it meets your expansion or analysis needs.
Can Store Location Data Be Integrated with Other Data Types?
Yes, store location data can often be combined with other types of data, such as demographic information, foot traffic analysis, and customer sentiment data. Integrating different datasets can offer richer insights, such as understanding customer behavior in relation to store locations or tracking competitive store footprints. We recommend combining store location data with additional geospatial or demographic datasets for a more complete market analysis.
Can I Request Custom Store Location Data?
Absolutely! Many providers offer customized datasets based on your specific business needs. Whether you need specific geographic regions, additional attributes like foot traffic or customer sentiment, or tailored delivery formats, custom store location data can be crafted to meet your requirements.
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