Austria Grocery Locations Dataset – Top 14 Supermarket Brands
Austria Grocery Locations Dataset – Top 14 Supermarket Brands
Top 14 Supermarket Chains Across Austria: Premium Multi-Brand Location Dataset
Unlock a complete, ready-to-use dataset featuring the leading Supermarket brands in Austria. This expertly compiled Top Brands dataset consolidates all major Supermarket chains in Austria, delivering a clean, standardised and analysis-ready collection of 5,291 locations. Perfect for businesses seeking high-quality data for market sizing, competitive landscape analysis, network optimisation, and strategic planning.
Last updated: 27 May 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Latitude
- Longitude
- Street No
- Street
- City
- Admin_level_1
- Admin_level_2
- Gemeinde
- Region
- Population
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Opening hours
Brand distribution
- ADEG – 249 locations in Austria
- BILLA – 1,279 locations in Austria
- BIPA – 543 locations in Austria
- Denns – 27 locations in Austria
- EUROSPAR – 238 locations in Austria
- HOFER – 524 locations in Austria
- INTERSPAR – 75 locations in Austria
- Lidl – 250 locations in Austria
- MPREIS – 249 locations in Austria
- Nah&Frisch – 207 locations in Austria
- NORMA – 23 locations in Austria
- PENNY – 300 locations in Austria
- SPAR – 1,235 locations in Austria
- UNIMARKT – 92 locations in Austria
Supermarkets distribution by Region
- Niederösterreich: 1,123 supermarkets
- Wien: 888 supermarkets
- Oberösterreich: 816 supermarkets
- Steiermark: 773 supermarkets
- Tirol: 554 supermarkets
- Kärnten: 399 supermarkets
- Salzburg: 343 supermarkets
- Burgenland: 208 supermarkets
- Vorarlberg: 187 supermarkets
Direct access after purchase of the complete locations dataset for Austria
Download the dataset immediately in a clean, analysis-ready CSV format. Ideal for geospatial analysis, competitor benchmarking, market studies, site planning, and retail location intelligence workflows.
Evaluate the dataset before purchase
A free sample is available for download, allowing you to inspect the structure, fields, and geospatial precision before purchasing the full dataset.
Related geospatial datasets
- Administrative boundaries and full polygon dataset for Austria: Map these supermarkets locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Austria: Overlay demographic indicators to deeply understand the population structures and household types surrounding these supermarkets locations.
- Explore demographics data insights for Austria: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Explore a rich library of Austria-specific datasets on our dedicated country page: detailed demographics, wealth indicators, multi-level boundaries, and a broad spectrum of retail POIs. View demographics, retail POI, and administrative boundary datasets for Austria
Need the data in another format?
We can deliver this dataset in alternative formats upon request (GeoJSON, Shapefile, Excel, PostgreSQL import files, etc.). Contact us at contact@geolocet.com.
Frequently Asked Questions
Q: Can I use this dataset for proximity analysis?
A: Yes. The geocoded coordinates are suitable for drive-time analysis, catchment modeling, nearest-neighbor analysis, and accessibility studies.
Q: Can I combine this dataset with administrative boundaries?
A: Yes. The coordinates can be spatially joined with municipalities, census units, postal areas, and other administrative polygons.
Q: Can I request the data in GeoJSON or Shapefile format?
A: Yes. Alternative delivery formats such as GeoJSON, Shapefile, Excel, and PostgreSQL imports are available upon request.
Q: Does the dataset include unique identifiers?
A: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.
Q: Does the dataset include opening hours?
A: Yes, opening hours are included where publicly available and validated during the data standardization process.
Q: Are the datasets suitable for machine learning workflows?
A: Yes. The structured tabular format and standardized coordinates make the datasets suitable for machine learning and predictive analytics applications.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this this multi-brand dataset to identify underserved regions in Austria for potential market expansion.""Create a regional ranking of this multi-brand coverage efficiency using population-to-store ratios across Austria.""Identify municipalities in Austria with strong demographic potential but no nearby this multi-brand presence."