Netto Supermarket Locations Dataset – Denmark

Netto is the largest discount supermarket chain in Denmark and the flagship discount brand of the Salling Group. It is easily recognized by its yellow dog logo and offers a streamlined assortment of groceries at very low prices.

There are 574 Netto Supermarkets as of 3 August 2026 in Denmark. 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 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.

Data Quality Scorecard

Data Preview: Sample geospatial records from the Netto dataset in Denmark

ID	Location Title	Latitude	Longitude	Postal Code	Full Address
6b02955...	Netto (Vesterbro)	55.667777	12.555771	1717	2 Skelbækgade, København, 1717, Denmark
d98c8e3...	Netto (Varde)	55.609984	8.494064	6800	29 Ribevej, Varde, 6800, Denmark
0b42de7...	Netto (Jyllinge)	55.750336	12.106999	4040	1B Jyllingecentret, Jyllinge, 4040, D...
d8d6d06...	Netto (Varde)	55.626994	8.460256	6800	16 Håndværkervej, Varde, 6800, Denmark
797fad6...	Netto (Søborg)	55.747570	12.480814	2860	195B Stengårds Alle, Søborg, 2860, De...

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.

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Regional Distribution Breakdown

Looking at the geographic distribution, the highest concentration of Netto locations in Denmark is found in Region Hovedstaden (221 sites, equivalent to 11.39 Netto supermarkets per 100,000 residents). This is followed by Region Syddanmark (109 sites; 8.79 per 100,000) and Region Midtjylland (101 sites; 7.32 per 100,000). From a market-penetration perspective, Region Sjælland has the highest brand density at 11.46 locations per 100,000 people (population: 855,000), making it the most saturated region for Netto in Denmark. By contrast, Region Midtjylland records only 7.32 locations per 100,000 residents (population: 1,380,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Denmark

Related geospatial datasets

• Administrative boundaries and full polygon dataset for Denmark: Map these Netto locations against highly precise administrative polygons for territory analysis and spatial structuring.
• Demographics dataset for Denmark: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Netto locations.
• Explore demographics data insights for Denmark: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
• Income indicators dataset for Denmark (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.
• Income distribution and socio-economic patterns in Denmark - expert analysis: A detailed blog post explaining income geography, disparities, and how income-based indicators can support location intelligence and market analytics.
• Explore a rich library of Denmark-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 Denmark

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?

• B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
• Territory Management: Field sales directors partitioning regional territories and routing field agents efficiently using exact addresses.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
• Trade Area Marketing: Agencies planning direct-mail or localized out-of-home (OOH) billboard campaigns near high-density retail clusters.
• Franchise Expansion: Network development teams assessing market saturation and mapping open territories for new franchisees.
• Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
• Mobility Analysis: Transport consultants evaluating retail proximity to major transit corridors and parking infrastructure.
• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
• Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.

Frequently Asked Questions

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 Denmark for potential market expansion."
• "Using this Netto data, find high-traffic retail corridors in Denmark with low supermarkets density for competitive positioning."
• "Assess how effectively the current Netto network covers major commercial and residential hubs across Denmark."

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.