SKU: 57414480001

Netto Supermarket Locations Dataset – Germany

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Description

Netto Supermarket Locations Dataset – GermanyQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI 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

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 →

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 [email protected].

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.

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I was pleasantly surprised by this omega verse book. This 1 definitely had a few new twists And I really enjoyed reading it. The main female character was a badass and awesome. She had 1 best friend I wish we could have seen a little bit more of their friendship in this book. Her relationship with the male characters was good not to contrived or super instantaneous. And we had some fun plot twists that I didn't expect. I wish we had more of a follow up on the situation with her family her background and her mother who was a wench. I would definitely recommend this book.
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Let me preface this by saying I really liked the story and the characters however, this book is in desperate need of some sort of editing. It's not misspelled words or formatting, but continuous run on sentences. Redundancies within the sentences. There were a couple of paragraphs that I had to go back and reread 3 or 4 times. Overall, I'd say it was worth the read.
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I love omegaverses I had looked at this one multiple times thinking it's just another book about cocky rock star guys that let fame get to their heads and there are parts like that so I wasn't 100% off. I started reading the second book and met the fmc Astraea from the first one,after she had a run in with the aphla from the gym.😁 it gave me the push to stop and read book one first and im so glad I did this book was amazing their was so many characters I fell in love with hoping they will find their happily ever after. the guys were great, the plot was 🎯, and the ending had me 😭😭😭. I was wondering how nate could ever redeem himself, and he did. the last scene with him was sad, but I also felt it was beautiful. thank you to the author for making a beautiful omegaverse book that gave me all the feels. now I'm jumping straight into book two.
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