Kaufland Supermarket Locations Dataset – Germany
Kaufland Supermarket Locations Dataset – Germany
Kaufland is a leading German hypermarket chain and part of the Schwarz Gruppe, along with Lidl. It provides a massive selection of groceries and household items at competitive prices in large-format stores.
There are 821 Kaufland Supermarkets as of 27 May 2026 in Germany. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Kaufland 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: 821 Kaufland 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)97%
- Web Address91%
- Opening Hours98%
- Popularity Score100%
Data Preview: Sample geospatial records from the Kaufland dataset in Germany
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| 46f5fca... | Kaufland (Gera) | 50.875777 | 12.078721 | 07545 | 30 Heinrichstraße, Gera, 07545, Germany |
| e1bb3a1... | Kaufland (Pirna) | 50.979951 | 13.949090 | 01796 | 13 Lohmener Straße, Pirna, 01796, Ger... |
| 8af5834... | Kaufland (Freising) | 48.383244 | 11.777461 | 85356 | 25 Raiffeisenstraße, Freising, 85356,... |
| 6c4438a... | Kaufland (Hamm) | 51.664829 | 7.784628 | 59067 | 29 Lohauserholzstraße, Hamm, 59067, A... |
| 06c9fd3... | Kaufland (Offenburg) | 48.481583 | 7.946547 | 77652 | 74 Okenstraße, Offenburg, 77652, Frei... |
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 Kaufland locations in Germany is found in Nordrhein-Westfalen (148 sites, equivalent to 0.82 Kaufland supermarkets per 100,000 residents). This is followed by Baden-Württemberg (133 sites; 1.19 per 100,000) and Bayern (109 sites; 0.82 per 100,000). From a market-penetration perspective, Sachsen-Anhalt has the highest brand density at 2.44 locations per 100,000 people (population: 2,175,000), making it the most saturated region for Kaufland in Germany. By contrast, Schleswig-Holstein records only 0.34 locations per 100,000 residents (population: 2,930,000), 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 Kaufland 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 Kaufland 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?
- Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
- Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
- Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
- B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
- Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
- Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.
- Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
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 preview the dataset before purchasing?
A: Yes. A free sample is available so you can evaluate the structure, fields, and geospatial quality before purchase.
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: How are addresses standardized?
A: Addresses are cleaned and normalized through automated formatting and validation workflows to improve consistency and usability.
Q: Can this dataset support expansion planning?
A: Yes. Analysts often use the dataset to identify underserved areas, evaluate regional density, and support retail expansion decisions.
Q: Is the dataset immediately downloadable after purchase?
A: Yes. The full dataset becomes available for instant digital download immediately after purchase.
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.
Analyze this data with AI
Use these prompts with ChatGPT, Claude, or Gemini to extract strategic insights from this dataset:
"Analyze this Kaufland dataset to identify underserved regions in Germany for potential market expansion.""Create a regional ranking of Kaufland coverage efficiency using population-to-store ratios across Germany.""Assess the accessibility of Kaufland locations in Germany based on proximity to population centers and public infrastructure."
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.