Netto Supermarket Locations Dataset – Poland

Netto is a discount supermarket chain in Poland owned by the Danish Salling Group. It significantly increased its market share in 2021 by acquiring the majority of the former Tesco stores in Poland.

There are 715 Netto Supermarkets as of 30 May 2026 in Poland. 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 Poland

ID	Location Title	Latitude	Longitude	Postal Code	Full Address
5d62c13...	Netto (Chojnice)	53.693752	17.564243	89-600	3A Plac Niepodległości, Chojnice, 89-...
020d9b8...	Netto (Rogoźno)	52.752477	16.991065	64-610	1C Fabryczna, Rogoźno, 64-610, Powiat...
04ce616...	Netto (Chorzów)	50.287065	18.949898	41-530	57 Hajducka, Chorzów, 41-530, Powiat ...
0d50b03...	Netto (Jelonek)	52.486039	16.855753	62-002	12 Obornicka, Jelonek, 62-002, Powiat...
2ab2140...	Netto (Międzyrzecz)	52.587162	15.495521	66-300	35 Garnizonowa, Międzyrzecz, 66-300, ...

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 Poland is found in Śląskie (93 sites, equivalent to 2.17 Netto supermarkets per 100,000 residents). This is followed by Wielkopolskie (92 sites; 2.63 per 100,000) and Zachodniopomorskie (88 sites; 5.43 per 100,000). From a market-penetration perspective, Zachodniopomorskie has the highest brand density at 5.43 locations per 100,000 people (population: 1,620,000), making it the most saturated region for Netto in Poland. By contrast, Podkarpackie records only 0.39 locations per 100,000 residents (population: 2,060,000), indicating a potential white-space opportunity for network expansion or competitor analysis.

Also available for Poland

Related geospatial datasets

• Administrative boundaries and full polygon dataset for Poland: Map these Netto locations against highly precise administrative polygons for territory analysis and spatial structuring.
• Demographics dataset for Poland: Overlay demographic indicators to deeply understand the population structures and household types surrounding these Netto locations.
• Explore demographics data insights for Poland: Methodology, use cases, and definitions explaining how demographic metrics support your location intelligence workflows.
• Income indicators dataset for Poland (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 Poland-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 Poland

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.
• Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
• Store Closure & Relocation Strategy: Corporate teams optimizing existing footprints by analyzing underperforming regions.
• Commercial Brokerage: Real estate brokers validating commercial property valuations based on proximity to major retail anchors.
• Vendor Distribution: FMCG and wholesale suppliers identifying specific retail locations for direct-store-delivery (DSD) pitching.
• B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
• Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
• 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 Poland for potential market expansion."
• "Identify regions in Poland where Netto has a disproportionately strong or weak presence relative to population density."
• "Identify fast-growing suburban areas in Poland that currently have limited access to nearby supermarkets."

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.