FirmenABC Scraper – Verified B2B Company Data & Leads with Email, Mobile, CEO, Website & Social Media (Austria) 🔍

This Python-based web scraper is designed to extract comprehensive and verified business data directly from FirmenABC.at. It's your go-to tool for collecting high-quality B2B leads, including detailed company information, essential contact details (phone, email, website), in-depth CEO profiles (including title, full name, salutation), and available social media links (LinkedIn, Facebook, Instagram, Twitter, YouTube, Xing). Perfect for sales teams, marketers, and researchers targeting the Austrian market, this scraper provides the structured data you need for effective outreach, CRM enrichment, and market analysis.

🔍 What It Scrapes

The scraper collects the following fields:

• ✅ Company Name
• 🏢 Street / Address
• 🏙️ ZIP Code & City
• 📞 Phone Number
• 📧 Email Address
• 🌐 Website
• 🙋 Gender & Salutation
• 🧑 CEO Title
• 👤 CEO First Name
• 👤 CEO Last Name
• 👔 Full CEO Name (as found)
• 🔗 Social Media (LinkedIn, Facebook, Instagram, Twitter, YouTube, Xing – if available)

✅ Why Use This Scraper?

• Easy to Use: Enter your search term and location (e.g., a city or state). You can also set parameters like require_manager, req_email, req_website, req_phone.

  search_term = 'tischler'
  loc = 'salzburg'
  require_manager = False
  req_email = True
  req_website = False
  req_phone = False
  

• No Page Limits: The scraper navigates through any number of result pages and automatically detects the end of the results list.

• Deep Extraction: It fetches not just superficial information but also deeper fields like CEO names, salutation, gender, email addresses (if available), and social links.

• Flexible Export Formats: CSV, Excel, JSON, XML – you decide how you want to process the data further.

• Continuous Development: The scraper is regularly updated to integrate new layout changes or additional fields.

• Error Tolerant & Stable: Even with faulty or incomplete datasets, the scraper runs stably and skips problematic entries.

• Deduplication Engine: Duplicate entries are automatically detected and excluded – perfect for clean databases.

• Resumption After Interruption: The scraper seamlessly resumes from where it left off, even after a termination or server change.

• High Performance: Despite detailed data extraction, the scraper processes over 10 entries per minute – ideal for large search queries.

🔧 Input

Required:

• search_term (string) The keyword or business category you want to search for (e.g. Elektriker, Architekt, KFZ Werkstatt).

Optional:

• location (string) A specific city or region to narrow down the search (e.g. Wien, Salzburg, München). If not provided, the scraper will search nationwide (Austria).

Filters (Booleans):

These control which data points are required in the final result. If set to true, the scraper will only include listings that contain this data.

• require_manager (true/false) Only include listings that have a named managing director or CEO.

• req_email (true/false) Only include results that contain a visible email address.

• req_website (true/false) Only include listings with a linked website.

• req_phone (true/false) Only include results that have at least one valid phone number.

The provided Python scraper is used to collect business data from the firmenabc.at website. The collected data is stored using the Apify platform (Actor.push_data(company_data)).

🔧 Output

The scraper outputs a collection of datasets, where each dataset represents the information of a single company. Each of these datasets is an object (a dictionary in Python) with the following fields:

• Name: The name of the company.
• Straße: The street address of the company.
• PLZ: The postal code of the company.
• Ort: The location/city of the company.
• Mobil: The company's phone number (often referred to as "Mobil", but can also be a landline number).
• Email: The company's email address.
• Website: The company's website.
• GF_Titel: Academic titles of the managing director (e.g., "Dr.", "Mag.").
• GF_Vorname: The first name of the managing director.
• GF_Nachname: The last name of the managing director.
• Geschäftsführer: The full, unformatted name of the managing director as found on the page.
• Anrede: The salutation of the managing director (e.g., "Herr", "Frau").
• LinkedIn: A placeholder for a LinkedIn profile.
• Facebook: A placeholder for a Facebook profile.
• Instagram: A placeholder for an Instagram profile.
• Twitter: A placeholder for a Twitter profile.
• YouTube: A placeholder for a YouTube profile.
• Xing: A placeholder for a Xing profile.

Since the scraper uses the await Actor.push_data(company_data) function, the data is stored in a dataset. Within the Apify platform, this is a structured data collection.

When you download the data from the Apify platform, you can usually choose from various formats, such as:

• JSON: A list of JSON objects, where each object represents a company with the fields mentioned above. Example of a single dataset in JSON format:

  {
    "Name": "Beispiel GmbH",
    "Straße": "Musterstraße 1",
    "PLZ": "12345",
    "Ort": "Musterstadt",
    "Mobil": "+43 1 2345678",
    "Email": "info@beispiel.at",
    "Website": "http://www.beispiel.at",
    "GF_Titel": "Dr.",
    "GF_Vorname": "Max",
    "GF_Nachname": "Mustermann",
    "Geschäftsführer": "Dr. Max Mustermann",
    "Anrede": "Herr",
    "LinkedIn": "",
    "Facebook": "",
    "Instagram": "",
    "Twitter": "",
    "YouTube": "",
    "Xing": ""
  }
  

• CSV (Comma-Separated Values): A tabular file where each row represents a company and each column represents one of the fields mentioned above.

• Excel (XLSX): A Microsoft Excel file, similar to the CSV format, but with Excel-specific features.

• Other formats supported by the Apify platform (e.g., XML, HTML table).

💼 Typical Use Cases

• Building B2B contact lists (including managing directors)

• Sales automation & outreach

• CRM data enrichment

• Competitive and market analysis

• Regional or industry-specific target group research