Germany Grocery Locations Dataset – Top 27 Supermarket Brands
Germany Grocery Locations Dataset – Top 27 Supermarket Brands
Top 27 Supermarket Chains Across Germany: Premium Multi-Brand Location Dataset
Unlock a complete, ready-to-use dataset featuring the leading Supermarket brands in Germany. This expertly compiled Top Brands dataset consolidates all major Supermarket chains in Germany, delivering a clean, standardised and analysis-ready collection of 32,239 locations. Perfect for businesses seeking high-quality data for market sizing, competitive landscape analysis, network optimisation, and strategic planning.
Last updated: 4 August 2026.
Dataset fields included in the CSV:
- GUID
- Title
- Latitude
- Longitude
- Street No
- Street
- Area
- City
- Admin_level_1
- Admin_level_2
- Gemainde
- Federal State
- Population
- Postal Code
- Address
- Wheelchair
- Popularity Score
- Phone
- Website
- Opening hours
Brand distribution
- ALDI – 4,344 locations in Germany
- Alnatura – 131 locations in Germany
- Combi – 207 locations in Germany
- Denns BioMarkt – 316 locations in Germany
- EDEKA – 6,480 locations in Germany
- EuroShop – 288 locations in Germany
- famila – 179 locations in Germany
- FRISTO – 207 locations in Germany
- GLOBUS – 85 locations in Germany
- HIT – 105 locations in Germany
- HOL'AB! – 163 locations in Germany
- K + K Klaas – 209 locations in Germany
- Kaufland – 822 locations in Germany
- Konsum – 120 locations in Germany
- Lidl – 3,327 locations in Germany
- METRO – 96 locations in Germany
- Mix Markt – 203 locations in Germany
- MÄC-GEIZ – 190 locations in Germany
- Nahkauf – 553 locations in Germany
- Netto – 4,777 locations in Germany
- Norma – 1,379 locations in Germany
- NP-Markt – 193 locations in Germany
- PENNY – 2,169 locations in Germany
- REWE – 5,032 locations in Germany
- SPAR – 289 locations in Germany
- Tante-M – 75 locations in Germany
- tegut... – 300 locations in Germany
Supermarkets distribution by Federal State
- Nordrhein-Westfalen: 5,952 supermarkets
- Bayern: 5,540 supermarkets
- Baden-Württemberg: 3,874 supermarkets
- Niedersachsen: 3,488 supermarkets
- Hessen: 2,452 supermarkets
- Sachsen: 1,808 supermarkets
- Rheinland-Pfalz: 1,492 supermarkets
- Schleswig-Holstein: 1,266 supermarkets
- Sachsen-Anhalt: 1,167 supermarkets
- Brandenburg: 1,161 supermarkets
- Berlin: 1,052 supermarkets
- Thüringen: 925 supermarkets
- Mecklenburg-Vorpommern: 875 supermarkets
- Hamburg: 583 supermarkets
- Saarland: 361 supermarkets
- Bremen: 243 supermarkets
Direct access after purchase of the complete locations dataset for Germany
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.
Learn more about the brand network in our report: View Report
Related geospatial datasets
- Administrative boundaries and full polygon dataset for Germany: Map these supermarkets locations against highly precise administrative polygons for territory analysis and spatial structuring.
- Demographics dataset for Germany: Overlay demographic indicators to deeply understand the population structures and household types surrounding these supermarkets locations.
- Explore demographics data insights for Germany: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
- Income indicators dataset for Germany (small-area income analysis): Download an analysis-ready dataset on income distribution across small geographic units - ideal for segmentation, customer analytics, location planning, and socio-economic scoring.
- Explore a rich library of Germany-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 Germany
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 this dataset be used for logistics planning?
A: Yes. Many customers use these datasets to optimize delivery territories, distribution networks, route planning, and last-mile logistics analysis.
Q: Can I integrate this dataset into a PostgreSQL/PostGIS database?
A: Yes. The dataset structure is compatible with PostgreSQL/PostGIS and other relational spatial databases.
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 this dataset be imported into Power BI or Tableau?
A: Yes. The CSV structure is compatible with Power BI, Tableau, Looker Studio, and other business intelligence platforms.
Q: Can this dataset support territory optimization?
A: Yes. The dataset is suitable for defining service territories, balancing regional coverage, and optimizing operational footprints.
Q: Does the dataset include opening hours?
A: Yes, opening hours are included where publicly available and validated during the data standardization process.
Q: Does the dataset include latitude and longitude coordinates?
A: Yes. Each location record includes precise WGS84 latitude and longitude coordinates for geospatial analysis and mapping workflows.
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 Germany for potential market expansion.""Identify regions in Germany where this multi-brand has a disproportionately strong or weak presence relative to population density.""Identify isolated this multi-brand sites in Germany that may face operational inefficiencies due to low regional clustering."