Lidl Supermarket Locations Dataset – Poland

Lidl Supermarket Locations Dataset – PolandQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Lidl is one of the most popular discount retailers in Poland, known for its high quality private labels and "Lidl Plus" loyalty program. It has built a massive presence across the country, consistently ranking high in consumer trust and price quality ratios. There are 1,002 Lidl Supermarkets as of 30 May 2026

Lidl is one of the most popular discount retailers in Poland, known for its high-quality private labels and "Lidl Plus" loyalty program. It has built a massive presence across the country, consistently ranking high in consumer trust and price-quality ratios.

There are 1,002 Lidl Supermarkets as of 30 May 2026 in Poland. This dataset is compiled and maintained by Geolocet and provides a complete, geocoded list of all Lidl 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 Lidl dataset in Poland

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 Lidl locations in Poland is found in Śląskie (142 sites, equivalent to 3.31 Lidl supermarkets per 100,000 residents). This is followed by Mazowieckie (126 sites; 2.29 per 100,000) and Wielkopolskie (99 sites; 2.83 per 100,000). From a market-penetration perspective, Pomorskie has the highest brand density at 3.86 locations per 100,000 people (population: 2,355,000), making it the most saturated region for Lidl in Poland. By contrast, Świętokrzyskie records only 1.73 locations per 100,000 residents (population: 1,155,000), 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 Lidl 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 Lidl 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 [email protected].

Who uses this data?

• CRM Data Enrichment: RevOps teams appending accurate, standardized contact details and coordinates to existing Salesforce/HubSpot records.
• B2B Telemarketing & Outreach: Sales teams using verified phone numbers to pitch localized services (e.g., POS systems, commercial cleaning, security).
• Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
• Last-Mile Delivery Routing: E-commerce and food-delivery planners optimizing localized courier routes and dispatch proximity.
• Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.
• Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
• Geofencing & Targeted Advertising: Media buyers executing hyper-local, location-based mobile ad campaigns around specific brand locations.
• Smart City Research: Academic researchers analyzing commercial density, urban growth patterns, and spatial economics.
• Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.

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 Lidl dataset to identify underserved regions in Poland for potential market expansion."
• "Create a regional ranking of Lidl coverage efficiency using population-to-store ratios across Poland."
• "Calculate the total population coverage for Lidl in Poland using a 10km catchment radius around each coordinate."

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.