Netto Supermarket Locations Dataset – Germany
Netto Supermarket Locations Dataset – Germany
Netto Marken-Discount is a major German discount supermarket chain owned by EDEKA. It offers a wide range of groceries and is known for its yellow-and-red branding and extensive selection of beverages.
There are 4,765 Netto Supermarkets as of 27 May 2026 in Germany. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Netto 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: 4,765 Netto 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)
- Web Address97%
- Opening Hours99%
- Popularity Score100%
Data Preview: Sample geospatial records from the Netto dataset in Germany
| ID | Location Title | Latitude | Longitude | Postal Code | Full Address |
|---|---|---|---|---|---|
| bc10370... | Netto Marken-Discount (Aschersleben) | 51.762839 | 11.450424 | 06449 | 38 Magdeburger Straße, Aschersleben, ... |
| e7215be... | Netto Marken-Discount (Könnern) | 51.672548 | 11.774973 | 06420 | 5d Magdeburger Straße, Könnern, 06420... |
| 9fdfa99... | Netto Marken-Discount (Großheide) | 53.593284 | 7.345251 | 26532 | 7 Coldinner Straße, Großheide, 26532,... |
| c9679db... | Netto Marken-Discount (Stadtbezirk 2) | 51.214059 | 6.815864 | 40231 | 129 Fichtenstraße, Düsseldorf, 40231,... |
| 36abec8... | Netto Marken-Discount (Düren) | 50.795748 | 6.487367 | 52349 | 4 Nideggener Straße, Düren, 52349, Kö... |
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 Netto locations in Germany is found in Nordrhein-Westfalen (903 sites, equivalent to 5.02 Netto supermarkets per 100,000 residents). This is followed by Bayern (727 sites; 5.5 per 100,000) and Baden-Württemberg (460 sites; 4.12 per 100,000). From a market-penetration perspective, Mecklenburg-Vorpommern has the highest brand density at 15.05 locations per 100,000 people (population: 1,615,000), making it the most saturated region for Netto in Germany. By contrast, Hamburg records only 2.2 locations per 100,000 residents (population: 1,860,000), 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 Netto 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 Netto 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?
- Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
- Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
- Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
- Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
- Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
- Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.
- Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
Frequently Asked Questions
Q: Is the dataset standardized for analytics workflows?
A: Yes. Address formatting, administrative areas, and geospatial fields are standardized to improve consistency across analytical environments.
Q: Does the dataset include accessibility-related attributes?
A: Yes. Certain datasets include accessibility-related indicators such as wheelchair accessibility where publicly available.
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: How accurate are the coordinates?
A: Coordinates undergo automated validation and manual quality review processes to improve positional accuracy and analytical reliability.
Q: Can I use this dataset for competitor benchmarking?
A: Yes. The dataset is frequently used to compare retail footprints, market density, and regional presence against competing brands.
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 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 Netto dataset to identify underserved regions in Germany for potential market expansion.""Identify locations where multiple Netto sites compete within overlapping catchment areas in Germany.""Measure average inter-store distance between Netto locations across different regions of Germany."
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.