SKU: 93948602483

Lidl Supermarket Locations Dataset – Germany

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Description

Lidl Supermarket Locations Dataset – GermanyQuick links: Dataset Summary Methodology Download Data Quality Regional Distribution Brand Bundle Related Datasets Use Cases FAQ Analyze with AI Lidl is one of Germanys most successful discount supermarket chains and a major competitor to ALDI. Part of the Schwarz Gruppe, it is known for its high speed checkout, fresh produce, and "Lidl Plus" loyalty app. There are 3,322 Lidl Supermarkets as of 27 May 2026 in Germany. This dataset is compiled and

Lidl is one of Germany’s most successful discount supermarket chains and a major competitor to ALDI. Part of the Schwarz Gruppe, it is known for its high-speed checkout, fresh produce, and "Lidl Plus" loyalty app.

There are 3,322 Lidl Supermarkets as of 27 May 2026 in Germany. 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 Summary

  • Dataset Coverage: 3,322 Lidl 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)
  • Contact Details (Phone)99%
  • Web Address97%
  • Opening Hours99%
  • Popularity Score100%

Data Preview: Sample geospatial records from the Lidl dataset in Germany

ID Location Title Latitude Longitude Postal Code Full Address
b3db2bb... Lidl (Altenmarkt) 49.202547 12.661234 93413 4 Rodinger Straße, Cham, 93413, Oberp...
a6f1d4c... Lidl (Denkendorf) 48.702466 9.310817 73770 2 Albstraße, Denkendorf, 73770, Stutt...
be657d8... Lidl (Holzminden) 51.834470 9.459872 37603 38 Allersheimer Straße, Holzminden, 3...
99fcd76... Lidl (Harsewinkel) 51.965498 8.244129 33428 40 Berliner Ring, Harsewinkel, 33428,...
4f4cc56... Lidl (Bad Salzungen) 50.811713 10.226243 36433 25 Rhönstraße, Bad Salzungen, 36433, ...

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 Lidl locations in Germany is found in Nordrhein-Westfalen (719 sites, equivalent to 4.0 Lidl supermarkets per 100,000 residents). This is followed by Bayern (488 sites; 3.69 per 100,000) and Baden-Württemberg (450 sites; 4.03 per 100,000). From a market-penetration perspective, Mecklenburg-Vorpommern has the highest brand density at 5.14 locations per 100,000 people (population: 1,615,000), making it the most saturated region for Lidl in Germany. By contrast, Thüringen records only 3.07 locations per 100,000 residents (population: 2,115,000), 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?

  • Economic Development: Agencies identifying underserved neighborhoods or "retail deserts" for targeted commercial investment.
  • 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).
  • Supply Chain Strategy: Distribution analysts evaluating competitor logistics networks and regional warehouse accessibility.
  • Consumer Behavior Analytics: Researchers correlating local demographics, foot traffic data, and proximity to physical stores.
  • Catchment Area Analysis: Analysts mapping 15-minute drive times to understand localized customer reach and accessibility.

Frequently Asked Questions

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: Is the dataset immediately downloadable after purchase?

A: Yes. The full dataset becomes available for instant digital download immediately after purchase.

Q: Does the dataset contain duplicate locations?

A: Duplicate detection and validation workflows are applied during processing to improve consistency and reduce redundant records.

Q: Does the dataset include unique identifiers?

A: Yes. Each record includes a GUID field to support deduplication, joins, and downstream database operations.

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 Germany for potential market expansion."
  • "Identify the most central Lidl locations in Germany to serve as hubs for a last-mile delivery network."
  • "Cross-reference these supermarkets with urban transit data to score each location's accessibility for non-driving customers."

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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NotJeffBezos
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It’s packed with buzzwords and adjectives, offering plenty of in-vacuum advice and self-promotional ‘hire-me’ vibes. There’s some real insight here, but it’s buried under a lot of noise.
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This book has a few pieces of good advice, but its buried under mountains of weird and amateur level musings. Example: Paul Singman advocates for eliminating ETL entirely. How? Just reprogram the applications to which you may or may not have the source code to handle your data processing. He calls Intention Data Transfer 🥴 Thanks for the advice Paul, I'll get right on that.
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Reading chapter 3. It was so far so good, but can't find the code in the repo. "All the related code can be found in the repository under project/hooks-notification." And in the repo I see no project folder. Please help!
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Good overview of the leading Agentic Framework. Will become outdated quickly.
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3.5 Stars rounded up. Not a bad place to start if you need to get up to speed fast with Claude Code, understand its vast feature set, how it works under the hood, best practices, and the various agent primitives and how to get the most out of them. Agentic frameworks (Claude Code in particular) are quickly becoming table stakes for anyone working in tech, so it's best to start now. I appreciated the author's ability to flesh out areas where Anthropic's documentation is lacking in depth and nuance, and for some not already working with Claude in their own repos, the fact that he provides "toy" repos where one can experiment with the tools without fear of consequence. Where the book falls short is that most of the stuff in here is already covered pretty well already in Anthropic's docs, or even better so in their free "Skilljar" courses. What's more, some areas are given a bit of a shallow treatment, while others are a bit better done. So it's a bit inconsistent in that sense. Also, I can see how this book will quickly lose its currency in a few months at the pace things are going. Ultimately, for me, the price of this book was a bit rich for my liking given the criticisms above. Still, I feel like I got valuable info that rounded up what I already knew from working with this agentic framework. Recommended.
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Brahmananda Reddy
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This book is not another “AI coding hype” book. A lot of books talk about agents at a very high level. This one actually explains how things work when you try to use them inside real development workflows. That was the biggest difference for me. What I liked most was the focus on context engineering, memory, MCP, hooks, subagents, and workflow orchestration instead of just “prompt better.” The author spends time explaining why long-running agent systems fail, how context grows over time, and why most AI coding setups become messy without structure. The examples also feel practical — The HookHub project, Next.js setup, GitHub workflows, Claude memory files, and MCP integrations make it easier to connect theory with actual implementation. From my retail domain experience perspective, I could immediately connect this to forecasting and pricing workflows. For example: * agents helping analysts generate specs before model development * automated code review for promo forecasting pipelines * isolated subagents for pricing, promotions, assortment * persistent memory for business rules across teams * MCP integrations to pull context from internal systems safely The section around context isolation and subagents especially stood out because that is very similar to how enterprise forecasting teams already operate in reality. Different teams own different decision spaces. One thing I appreciated: the author does not oversell AI. There is a strong focus on constraints, context pollution, hallucinations, performance degradation, and workflow reliability. That makes the book feel grounded instead of marketing-heavy. This is not for complete beginners though. If someone has never worked with Git, APIs, coding agents, or LLM workflows, parts of the book may feel overwhelming early on. The author clearly says this is not beginner-level content. Overall, probably one of the more practical books I have read recently on agentic coding systems. Good for: * software engineers * AI engineers * enterprise architecture teams * technical product teams * analytics leaders trying to operationalize AI development workflows Especially useful if your organization is trying to move from “AI demos” into actual production workflows.
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Reviewed in the United States on May 20, 2026

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