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3 Powerful Business Use Cases of LinkedIn Data in 2024

With more than 120 professionals joining every minute, LinkedIn is a continuously growing venue for business professionals and content. Beyond its personal use cases such as professional networking or applying for jobs, LinkedIn is also a rich and tailored data source for many business use cases. LinkedIn profiles are very structured and rich in content, enabling you to find individuals with certain backgrounds, posting about a certain content or looking for a certain job at a large scale.

In this article, we will introduce the top 3 business use cases that LinkedIn data can transform.

1. Reach out to righ people for lead generation:

In 2019, US marketers ranked email as the tool with the highest ROI for B2B lead generation. Email enables you to personalize your message for your potential customer and show your willingness to respond to their questions personally. However, if your email does not find the right person in the right time, your effort put in curating the right email message may not generate successful leads.

LinkedIn comes into play to find you the right contacts for your email lead generation efforts. As of today, LinkedIn claims that they are the largest professional social network online with 810 million members. In fact, it is such a rich source for potential customers that finding each contact can take long time. Instead, you can define the type of profiles you want to access, run automated scraping queries and download an up to date list of LinkedIn profiles with all public information you needed.

  • Top Benefit: The profiles you scrape can be filtered as detailed as you want, such as company names, shared connections or keywords in the bio e.g. “responsible for, looking for solutions…”
  • Top Challenge: Information you can scrape from LinkedIn is limited to publicly available data. If a B2B contact is open for being contacted, they will provide their work email publicly. If not, you will not receive it in the scraped data. In such cases, your only option for contacting that person would be connecting them organically on LinkedIn or using LinkedIn’s paid marketing tools such as LinkedIn Lead Generator.


Building web scraper algorithms in-house require restructuring the code for each website. If you need to understand how Linkedin profile data can help your business needs, you can leverage a no-code LinkedIn scraper to access and extract LinkedIn data automatically.

Bright Data allows you to specify people and companies of interest from a user-friendly interface and provides you with a complete and clean dataset. Check out this 1-minute video to see how Bright Data’s Linkedin data collector works.

Instead of data collection, you can utilize ready-use LinkedIn datasets to reduce the chance of errors and inconsistencies in the data. Using ready-use datasets allow users to focus on analysis instead of data gathering, cleaning, and formatting.

2. Take data-driven investment decisions:

According to a 2019 research by LinkedIn based on independent sources, 63% of investors use social media for financial investment decisions and LinkedIn is the top source they consult with. As we described in our article about influencer marketing, scraping social media posts of certain influencers in your industry is key to stay up to date with the latest trends.

Similarly, targeting public posts of key finance executives and investment banks will help build a “radar” for the trending industries and stocks in the financial investment world. Similarly, top technology executives and venture capital firms’ posts will help you identify emerging startups and new products in the market.

  • Top Benefit: LinkedIn will give you access to the business-focused audiences to start building your “target influencers”. Currently, there are more than 2000 groups on “venture capital” and than 4000 groups on “financial investment” that you can join as a member and find out the profiles of people who may not be as prominent as top executives in the field but are quite active to generate content on LinkedIn.
  • Top Challenge: The startup ecosystem is very dynamic and financial markets are very volatile. Despite being a good source, you may need to track the pulse of political and financial news by scraping the web searches and other social media resources, such as Twitter.

3- Optimize your recruitment processes:

More than 80% of recruiters use LinkedIn as their primary source of hiring. However, it is also challenging to put the right keywords and requirements for a job description and also reaching out to best talent. Scraping LinkedIn profiles can help recruitment in two ways:

  1. Optimize your job posting: Job postings cost companies money and time and it is important to optimize the content to find the right talent as fast as possible. If you limit your job posting to a certain list of keywords, degrees, certificates, prior titles, you may miss out some candidates who have a new version of the same experience. It is good practice to have a pool of candidates that you perceive to be a good candidate for job search and analyze top schools, backgrounds and experiences they come from. Another gem in the LinkedIn profile data is the short bio that people put “what they are looking for” which will shape the keywords and call to action you use for your job description.
  2. Reach out to right candidates: Recruitment is a two-way process. Sometimes, especially for urgent roles or need for a specific experience, recruiters will need to reach out to candidates directly. However, it is a very manual task to search for all candidates on LinkedIn, take notes and prepare a rank of preference to reach out. Scraping a pool of candidates will help recruiters to process people’s profiles quickly and prepare a short list to reach out to.
  • Top Benefit: As the most popular professional social network, LinkedIn will be the most up-to-date and organic information source you can find in terms of education and job experience trends of your potential candidates.
  • Top Challenge: A complete LinkedIn profile will also have many redundant data points such as early job experiences, skills, endorsements which will add on your data processing time. It is essential to tailor your web scraping query or inform your web scraper service to narrow down the data points scraped, such as pulling only the job titles related to position you look for or only the most up-to-date education completed. Remember, you already have a solid understanding of what you are looking for, and this is supposed to be a final touch to fine tune your message and requirements.

Currently, it is legal to scrape data from LinkedIn. However, the company had a major lawsuit with an analytics company in 2019 based on violating LinkedIn’s terms and conditions. The lawsuit has not concluded on a ban for scraping publicly available LinkedIn data yet, however as an active debate, you should double check the limitation of scraping LinkedIn lists on their site or work with an external web scraper to benefit from their legal counseling. For more details on how to find terms of web scraping from a website and LinkedIn’s lawsuit, check out our article on the subject.

For more on web scraping:

To explore lead generation sources and web scraping use cases for different industries, read our articles:

For guidance to choose the right tool, check out data-driven list of web scrapers, and reach out to us:

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This article was drafted by former AIMultiple industry analyst Bengüsu Özcan.

Access Cem's 2 decades of B2B tech experience as a tech consultant, enterprise leader, startup entrepreneur & industry analyst. Leverage insights informing top Fortune 500 every month.
Cem Dilmegani
Principal Analyst
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Cem Dilmegani
Principal Analyst

Cem has been the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per similarWeb) including 60% of Fortune 500 every month.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised businesses on their enterprise software, automation, cloud, AI / ML and other technology related decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

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