Tante-M Supermarket Locations Dataset – Germany
Tante-M Supermarket Locations Dataset – Germany
Tante-M is a modern German retail concept of self-service, unmanned grocery stores designed to bring local supply back to small villages. They operate 24/7 or with extended hours, relying on digital payment systems.
There are 74 Tante-M Supermarkets as of 27 May 2026 in Germany. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Tante-M locations, including full address details, administrative divisions, and precise WGS84 latitude/longitude coordinates - structured for GIS, retail analytics, mapping, and AI/RAG workflows.
Dataset Summary
- Dataset Coverage: 74 Tante-M supermarkets in Germany
- Contents: Coordinates, addresses, postal codes, administrative divisions, contact details, and popularity scores
- File Format: Fully geocoded CSV dataset (UTF-8)
- Free Sample: Instantly accessible dataset to verify structure and data quality
- Use Cases: Suitable for GIS, retail analytics, site selection, and AI/RAG workflows
- Last Updated: 27 May 2026
Dataset Methodology:
This dataset is compiled from publicly available business listings, official company sources, and geospatial validation workflows. Automated quality checks and manual analyst reviews are applied to improve coordinate precision, address standardisation, duplicate detection, and overall analytical consistency.
It is periodically reviewed and updated to reflect known network changes, closures, relocations, and newly identified locations.
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
Data Quality Scorecard
- Geospatial Accuracy: 98%+ (Verified WGS84 Coordinates)
- Contact Details (Phone)82%
- Web Address69%
- Opening Hours95%
- Popularity Score100%
Data Preview: Sample geospatial records from the Tante-M dataset in Germany
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| 8af8639... | Tante-M (Röslau) | 50.082473 | 11.977150 | 95195 | 1 Brückenstraße, Röslau, 95195, Oberf... |
| e09e14d... | Tante-M (Talheim) | 48.487691 | 8.663838 | 72160 | 60 Nagolder Straße, Horb am Neckar, 7... |
| 4c200fd... | City-M Backnang (Backnang) | 48.947539 | 9.430489 | 71522 | 10-12 Marktstraße, Backnang, 71522, S... |
| 8708447... | Tante-M (Bollschweil) | 47.921062 | 7.790061 | 79283 | 27 Hexentalstraße, Bollschweil, 79283... |
| db4eba3... | Tante-M (Kleinbottwar) | 48.980449 | 9.288845 | 71711 | 8 Kirchstraße, Steinheim an der Murr,... |
Note: Only a subset of the full dataset fields are displayed here. Download the free sample (option above) to view all fields and verify the data structure.
Why download from Geolocet?
- Instant download - full dataset available immediately after purchase, no waiting, no manual fulfilment
- Free sample first - verify structure, fields, and coordinate precision before you commit
- Analysis-ready CSV - clean, standardised, and compatible with Excel, Python, QGIS, Power BI, and PostgreSQL out of the box
- Regularly updated - last updated 27 May 2026
✅ Data looks right? Add to cart ↑ - or download the free sample first.
Regional Distribution Breakdown
Looking at the geographic distribution, the highest concentration of Tante-M locations in Germany is found in Baden-Württemberg (59 sites, equivalent to 0.53 Tante-M supermarkets per 100,000 residents). This is followed by Bayern (10 sites; 0.08 per 100,000) and Nordrhein-Westfalen (2 sites; 0.01 per 100,000). From a market-penetration perspective, Baden-Württemberg has the highest brand density at 0.53 locations per 100,000 people (population: 11,170,000), making it the most saturated region for Tante-M in Germany. By contrast, Nordrhein-Westfalen records only 0.01 locations per 100,000 residents (population: 17,995,000), indicating a potential white-space opportunity for network expansion or competitor analysis.
Learn more about the brand network in our report: View Report
Also available for Germany
Brand bundle
Top 27 Grocery Brands in Germany - €480
All major chains in one standardised dataset. Best for competitive benchmarking, network analysis, and market sizing across the leading brands.
View Top Brands dataset →Full market coverage
All Grocery Locations in Germany - complete POI dataset
Includes everything in the brand bundle plus independent operators, smaller chains, and local businesses not covered by the top brands. Best for full market mapping, territory planning, and white-space analysis.
View full POI dataset →Related geospatial datasets
- Administrative boundaries and full polygon dataset for Germany: Map these Tante-M 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 Tante-M 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.
Who uses this data?
- Urban Planning: City government agencies studying retail accessibility, neighborhood walkability, and commercial infrastructure.
- Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
- Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
- Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
- CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
- Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
- Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.
Frequently Asked Questions
Q: Does the dataset include unique identifiers?
A: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.
Q: Is this dataset useful for accessibility studies?
A: Yes. Analysts can combine the coordinates with mobility, transport, and demographics datasets to evaluate accessibility and service coverage.
Q: How recent is this dataset?
A: This dataset was last updated on 27 May 2026 and is periodically refreshed through automated collection and validation workflows.
Q: Does the dataset include administrative regions?
A: Yes. Administrative fields such as province, district, municipality, postal code, and city are included where available.
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: Are postal codes included for all locations?
A: Postal codes are included wherever available and validated as part of the standardization workflow.
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: 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 Tante-M dataset to identify underserved regions in Germany for potential market expansion.""Analyze the clustering behavior of Tante-M locations to determine whether the network prioritizes convenience, coverage, or saturation strategies.""Identify strategic locations in Germany where new Tante-M sites could maximize geographic coverage while minimizing overlap."
Disclaimer: All brand logos and trademarks displayed are the property of their respective owners and are used strictly for identification purposes. This product consists of geospatial location data only; no images, logos, or trademark rights are included in the downloadable files.