LinkedIn Company Contact Scrape

@kabirchy

Hi there, I understand you need a structured Excel sheet with LinkedIn company profiles, including names, emails, phones, and profile URLs. I will use Python with Selenium for reliable data extraction from LinkedIn, ensuring compliance with their terms, and BeautifulSoup for parsing where appropriate. The data will be cleaned, validated for accuracy, and organized into a well-formatted Excel file with clear columns for each detail. I’m experienced in web scraping and data management, and I’ll handle this efficiently to deliver exactly what you need. Best Regards, Khorshed Alam, RS Software

@mahrsaad211

I can provide a clean, accurate LinkedIn company list with verified details and consistent formatting. I have experience in LinkedIn research, web scraping, data extraction, and Excel data cleaning, with strong attention to duplicates and data accuracy. I’ll deliver 51–100 company profiles with Company Name, Email, Phone Number, and LinkedIn URL in a filter-ready Excel sheet. I can also provide a small sample first so you can confirm the format and quality. I’m ready to start immediately and can complete the project quickly. Best Regards, Muhammad Saad

@sainijyoti1196

Hi, I can collect and organize LinkedIn company contact data based on your target criteria, including company details, decision-makers, job titles, LinkedIn URLs, and available contact information. I’ll ensure the data is accurate, relevant, properly structured, and checked for duplicates before delivery. Ready to start immediately

@Web1devloper

The main challenge here is not simply collecting 51–100 LinkedIn company URLs; it is producing a dependable contact dataset where every row is consistently structured, deduplicated, and clearly distinguishes verified details from unavailable information. Email addresses and phone numbers are not always publicly exposed on LinkedIn, so I would not fill gaps with guesses or inferred data. I would use Python for controlled extraction and normalization, then apply Excel/VBA validation to make the final workbook practical to filter and spot-check. The sheet would use consistent headers for company name, email address, phone number, and LinkedIn profile URL, with duplicate detection based on normalized company names and URLs. Publicly visible contact details would be captured as found, while missing values would remain clearly marked rather than presented as reliable contacts. The workflow would also include cleaning whitespace, standardizing URL formats, checking email and phone patterns, removing duplicate records, and validating that each row refers to a company profile. Extraction would be handled conservatively to respect LinkedIn access limits and avoid unnecessary requests. I can provide a small sample export using the proposed column structure so the formatting can be checked before the complete sheet is finalized. The deliverable will be a clean Excel file ready for filtering, with the processing logic kept understandable and reusable rather than leaving behind an unstructured scrape. I can also document any records where contact fields were not publicly available, which makes the spot-check process more transparent. Should the extraction be limited strictly to contact details publicly visible on LinkedIn, or may missing company email and phone data be cross-checked against publicly available official company sources?

@Cruz1217

Hi I can collect 51–100 LinkedIn company profiles and deliver a clean, duplicate-free Excel sheet with company name, available email, phone number, and LinkedIn URL. I have strong Python experience with data processing, extraction workflows, and Excel handling. I can use Python with appropriate scraping or browser automation methods where permitted, then validate the collected records and normalize the spreadsheet so every row follows the same structure. I’ll focus on accuracy rather than simply collecting volume. I’ll check for duplicate companies, validate URLs and contact fields where possible, and clearly distinguish unavailable contact information instead of filling the sheet with unreliable data. I can also provide a small sample in the requested format so you can verify the structure before the full extraction.

@nasrinkusum

Hi, This is Nasrin from Freelancer. I have read the project description & your requirement details. I’m clear with your requirements. I am ready to start right now. Waiting for your reply. Thanks

@Sasanka433

I work with Python-based web automation and structured data extraction, including Selenium-style browser automation and post-processing into clean Excel datasets. For this project I’d separate the work into company-profile collection, contact-detail verification, duplicate checking, and final Excel validation rather than relying on raw scraped results alone. I don’t want to misrepresent my background by claiming a previous LinkedIn-specific project I haven’t completed. My experience is primarily Python automation and data processing, and I can first prepare a small sample from a few target companies so you can verify the structure and accuracy before I continue with the full 51–100 records. I’ll also keep the collection method conservative and within the access limits available.

@Akash7015

Hello, i read your requirement. I have experience in excel and done many projects i give you best work on your time and budget. Thanks, waiting for your response...

@indreshpnk

Hello, I understand you need a clean Excel sheet containing 51–100 LinkedIn-sourced company profiles with company names, reliable email addresses, phone numbers, and LinkedIn URLs. The goal is to provide accurate, deduplicated, consistently formatted data ready for filtering and verification. Here’s what I can provide: Collect and structure company profiles with consistent fields and validated LinkedIn URLs Use Python, BeautifulSoup, Selenium, or suitable tools while respecting LinkedIn’s access limitations and terms Deliver a clean Excel sheet with duplicate checks, standardized formatting, and accuracy-focused validation I bring over 4+ years of experience in Python, data processing, web technologies, databases, and API integrations, with a strong focus on structured and reliable data workflows. I can efficiently organize extracted information into clean, usable datasets with appropriate validation and documentation. Just to clarify a few things: Do you already have a specific industry or list of companies to target? Should contact details be limited to publicly available business information? Please come to the chat box to discuss more about your project. Best regards Indresh Kushwaha

@surya8527

Hello, I have 4+ years of experience in data scraping, Excel and lead generation and have completed similar LinkedIn data extraction projects before. I can provide clean, accurate and duplicate-free company data with email, phone number and LinkedIn URLs. Available to start immediately.

@Jennycreation

Hello Sir, I have already worked on similar types of website and software development projects and I am confident that I can successfully handle your project. I am a full-time freelancer with **17+ years of experience** in web development, software development, automation, and API integrations. My expertise includes: * ASP.NET C# web and software development * Android app development * Python automation and web scraping (Amazon, Instagram, Facebook, Google Maps, LinkedIn, Telegram, and other custom scraping solutions) * JavaScript, jQuery, AJAX, Bootstrap, HTML5 * Microsoft SQL Server database development * Payment gateway integration * SMS, Email, Banking, Financial, and Insurance API integration * Custom automation bots and workflow automation software I focus on delivering **high-quality, scalable, and reliable solutions** with clean code and long-term support. Please contact me in private chat so we can discuss your requirements in detail. I can also show you a **demo of similar projects** I have completed. Looking forward to working with you. Thanks & Regards

@TrangLeTin

Hi, I can start immediately and deliver this today. I can collect the 51–100 company profiles into a clean Excel workbook with consistent columns for company name, email, phone number, and LinkedIn URL, then run a duplicate and formatting check before delivery. I work with Python, Selenium, web scraping, data extraction, and Excel processing, so I can automate the repetitive collection and validation steps while manually reviewing the final dataset for accuracy. I will only use contact information available from legitimate accessible sources and will clearly flag any email/phone field that cannot be reliably verified rather than inventing or guessing data. I can start right now and send you a small formatted sample within 1–2 hours, then complete the full 51–100 company list today depending on the available contact data. I’m PCPP1 certified in Python and can provide the final Excel file clean, deduplicated, and ready to filter. Ready to start now.

@Chirag3147

As an experienced Full Stack and AI Automation Developer, I offer a unique blend of skills that make me an ideal candidate for this LinkedIn scraping project. I'm proficient in both Excel and Python, two tools that will be pivotal in collating and delivering your desired dataset. Additionally, my deep understanding of other modern web technologies like Selenium and BeautifulSoup align perfectly with the needs of this project, ensuring a seamless LinkedIn scraping process while staying within LinkedIn's terms. Beyond just the technical aptitude, my approach to work involves not only solving problems but also improving functionality for the long-term. This ensures that even as your business grows, the solution we build will continue to serve its purpose without hiccups. If you're looking for someone who can mesh web development, mobile apps and AI automation seamlessly to create powerful scalable systems, look no further. Let's transform your manual task into accurate records by leveraging my expertise!

@abdulsattar3434

Hi there, I can extract and compile the 51-100 LinkedIn company profiles into a clean, duplicate-free Excel sheet with verified company names, emails, phone numbers, and LinkedIn URLs. I use reliable tools like Sales Navigator, Phantom Buster, and email verification tools to ensure accurate data formatting and 100% email deliverability without triggering LinkedIn rate limits. Which specific industry or target country should these 51-100 companies belong to, and would you like me to share a quick 3-row sample format first? Best regards, Abdul Sattar

@abhipatel8675

Hi, 100% Work Accuracy & Work Satisfaction I can work on this LinkedIn company data extraction requirement as an individual developer. I can provide a clean, deduplicated Excel sheet with consistent columns for company name, email, phone number, and LinkedIn URL, while respecting LinkedIn’s access limits and terms. I can use an appropriate Python-based workflow or other suitable tools depending on the data source and access available. Please share the target industry/niche, location (if applicable), and any specific LinkedIn search criteria or list of companies. Once I have these details and access requirements, I can confirm the turnaround time and get started. I can also share a small sample in your required format for approval first. - Abhi

@Vicar

With my extensive skills in data research and business consulting, paired with my adeptness in internet marketing and SEO optimization, I can assure you that I'm the perfect fit for your project. Using my knowledge of Python, Selenium, BeautifulSoup, and Phantombuster, I can guarantee a reliable and efficient extraction of data from LinkedIn while keeping within the platform's terms. Over the past 15+ years, I've developed a robust and adaptable skillset which spans across IT operations, web development, cloud computing and much more. This allows me to have a deep understanding of databases and APIs which are crucial in ensuring the integrity of the spreadsheet you require. Additionally, I've cultivated an ingrained respect for accuracy and efficiency in my work. As such, once we green-light this project, I'll ensure it is delivered promptly without compromising on quality. Let me share some sample files from my previous LinkedIn extraction projects so you can evaluate them for suitability. Looking forward to working together to give you exactly what you need for your LinkedIn Company Contact Scrape.

@marvind18

You need a clean, deduplicated company list with verifiable contact details—not a brittle extraction that risks LinkedIn restrictions or fills the sheet with guessed data. I can structure the workbook with consistent headers, source URLs, verification status, and duplicate controls, then validate each email and phone number against publicly available company information. I work with Python for data processing and API-based workflows, but I would not automate logged-in LinkedIn scraping, bypass access limits, or collect non-public contact data. The safest approach is to use LinkedIn only where permitted for company identification and source public business contact details from official company sites or approved data sources. Each record can include company name, LinkedIn company URL, public email, public phone, source link, and a note when a field is unavailable rather than inserting unreliable information. I’ll apply normalized formats and final duplicate checks so the file is ready to filter and spot-check. I do not have a public LinkedIn-extraction sample I can legitimately share, and I won’t invent one. Do you have a target industry, country, and approved data-source list for the 51–100 companies?

@balqeesg4

Hi, I can compile 51–100 accurate LinkedIn company profiles into a clean, filter-ready Excel sheet with company name, publicly available business email, phone number, and LinkedIn URL. I’ll carefully check each record for accuracy, remove duplicates, and keep the formatting consistent. I’ll also respect LinkedIn’s limits and avoid collecting private information. I’m ready to start immediately and can deliver the completed sheet within 1–2 days after approval. Best regards, Balqees Ahmed

@chamanrajragu

You asked for this to stay within LinkedIn's terms, and that rules out scraping: LinkedIn's User Agreement prohibits it, whichever tool is used, and accounts running Phantombuster-style automation get restricted. So I would use LinkedIn only to identify the companies, then take the contact details from each company's own website and public listings, which is where genuine contact information usually lives anyway. For each of the 51-100 companies: - Company name and LinkedIn company URL - Email and phone number, with the source page noted so you can spot-check it - One row per company, consistent headers, no duplicates, ready to filter I do not have a previous LinkedIn extraction to share, so I would rather send you 5 rows first. If the format and accuracy are right, I complete the rest within 2 days. Which industry and location should the companies be in?