📒 Gelbe Seiten Scraper — German B2B Leads from the Yellow Pages

Extract business listings from Gelbe Seiten (gelbeseiten.de), Germany's largest business directory. Search any business type in any German city and pull clean, structured records with name, address, phone, email, fax and website — the exact contact data you need for B2B lead generation, sales prospecting and CRM enrichment across the DACH region. Export to JSON/CSV/Excel, run on a schedule, call via API, or connect to Make, Zapier or n8n.

📒 What is the Gelbe Seiten Scraper?

It turns any Gelbe Seiten search into a ready-to-use lead list. Give it a search term (Suchbegriff) like Zahnarzt, Restaurant or Rechtsanwalt and one or more German cities, and it searches every term in every city, pages through the results, deduplicates by business name, and optionally visits each detail page to enrich every record with email, fax and website. The output is a flat table — one row per business — that drops straight into your CRM, outreach tool or spreadsheet.

What data does it extract?

• Business name — the company / practice name
• Address — full street, postal code and city, combined into one field
• Phone number as listed on the profile
• Email address (decoded from the listing or pulled from the detail page)
• Fax number when published
• Website — the external company URL
• Gelbe Seiten URL (detail_url) — the source profile link
• Search query & city echoed back on every row, plus a scraped_at timestamp

⬇️ Input

Field	Type	Description
searchQueries	array	One or more business types or keywords, e.g. Zahnarzt, Restaurant, Rechtsanwalt, Physiotherapie. Every row names its query in search_query. Empty: Zahnarzt
cities	array	One or more German cities, e.g. Berlin, München, Hamburg, Frankfurt am Main. Every query is searched in every city. Empty: Berlin
searchQuery / city	string	One keyword / one city (older single fields, still accepted and searched together with the lists)
maxResults	integer	Maximum number of business listings to return per query and city (1–5000)
includeDetails	boolean	Visit each detail page to extract email, fax and website (slower, more complete)
maxLoadMore	integer	Older browser setting, still accepted and ignored: the engine pages through results on its own up to maxResults

Example input

{
  "searchQueries": ["Zahnarzt", "Kieferorthopäde"],
  "cities": ["Berlin", "Hamburg"],
  "maxResults": 100,
  "includeDetails": true
}

⬆️ Output

Every business is one clean row (view as a table, or export JSON / CSV / Excel):

{
  "name": "Zahnarztpraxis Dr. Müller",
  "address": "Friedrichstraße 100, 10117 Berlin",
  "phone": "030 12345678",
  "email": "praxis@dr-mueller-berlin.de",
  "fax": "030 12345679",
  "website": "https://www.dr-mueller-berlin.de",
  "detail_url": "https://www.gelbeseiten.de/gsbiz/...",
  "search_query": "Zahnarzt",
  "city": "Berlin",
  "scraped_at": "2026-06-26T14:00:00+00:00"
}

💡 Use cases

• 🎯 B2B lead generation: build targeted prospect lists by industry and city — every record comes with phone, email and website ready for outreach.
• 📇 CRM enrichment: fill in missing contact details for known German companies using name + city as the lookup.
• 📊 Market research: measure business density per category and region (e.g. how many Klempner in München vs Hamburg).
• 🗂️ Local-data platforms: power "find a business" directories and sales-intelligence products with fresh DACH company data.

❓ FAQ

How do I scrape Gelbe Seiten listings? Enter one or more searchQueries (business types) and one or more cities, set how many results you want, then Run. You get a structured table with name, address, phone, email, fax and website.

Does it extract email addresses? Yes. Emails are decoded directly from the listing where available, and with includeDetails enabled it also opens each profile page to pull email, fax and website for more complete records.

Should I search in German or English? German terms work best — use Klempner instead of plumber, Rechtsanwalt instead of lawyer. For cities with umlauts (München, Köln), pass them with the umlauts intact.

Can I search several cities in one run? Yes. Put them all into cities (["Berlin", "Hamburg", "München", "Köln"]): every search term is searched in every city, maxResults applies per term and city, and each row names its city in city. To cover a region, list its cities; the results land in one dataset.

How many listings can it return? Up to your maxResults cap (max 5000) per search term and city. The actor pages through Gelbe Seiten's results until it reaches that cap or the search runs out.

Why are some emails, faxes or websites empty? Not every business publishes all fields. Empty values simply mean that detail isn't listed on Gelbe Seiten for that company.

Do I need an API key? No. The actor reads publicly available directory data — just provide a search term and one or more cities. No Gelbe Seiten account or login is required.

Can I run it on a schedule or via API? Yes — schedule recurring runs in the Apify Console, call it via the Apify API/SDK, or connect it to Make, Zapier or n8n to keep your lead lists fresh automatically.

Is scraping Gelbe Seiten legal? It extracts publicly accessible business listings. Use it responsibly, respect gelbeseiten.de's Terms of Service, and handle contact data in a GDPR-compliant way (no spam, honor opt-outs).

🔗 You might also like

• Anwalt.de Scraper — German lawyers and law firms
• Jameda Scraper — German doctors and medical practices
• Business Contact Scraper — emails, phones & socials from any site
• Kleinanzeigen Scraper — German classifieds marketplace

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Keywords: Gelbe Seiten scraper, German yellow pages scraper, gelbeseiten.de scraper, B2B lead generation Germany, German business directory, DACH leads, sales prospecting Germany, company email scraper, German business data, CRM enrichment, local business leads, find German businesses, phone and email extractor, German company contacts.

Workflow and data quality

For best results, run this actor as part of a repeatable data pipeline rather than a one-off scrape. Start with a focused input, export the dataset to CSV or JSON, and keep a stable unique field such as URL, ID, domain, package name or title as your deduplication key. Schedule the same task daily or weekly when you need monitoring, then compare each new dataset with the previous run to identify fresh records, removed records and changed fields.

The output is designed to be practical for business workflows: spreadsheets, dashboards, enrichment steps, alerts, CRM imports and lightweight internal APIs. Null values mean the source did not provide that field, not that the actor guessed. For larger projects, combine this actor with related Apify scrapers and use the shared URL/company/domain fields to build a richer dataset without manual copy-paste work.