It is estimated that 49 million people use LinkedIn to search for jobs, and ~87% of recruiters rely on LinkedIn to evaluate and communicate with potential candidates. As candidates increase their reliance on social media and recruiting websites, it is crucial for recruiters to benefit from web data in the hiring process.
Recruiters are leveraging web scrapers to automate the extraction of data from recruiting websites in order to analyze the job market, understand candidate qualifications, and optimize the hiring process.
What are the benefits of web scraping for recruiters?
There are more than 40,000 employment websites in USA alone which serve as a large pool of candidate data. Extracting relevant data from employment websites can be a tedious and time-consuming task, therefore, web scrapers can provide the following benefits:
- Reduce the time spent on manually extracting candidate data
- Reduce the costs of hiring a team to extract the data
- Provide almost real-time data about job postings
- Enable a data-driven decision-making strategy of hiring.
What are the use cases of web scraping in recruiting?
2 out of 3 recruiters do not have the tools necessary to understand the market and talent pool they are recruiting from. Therefore recruiters can leverage web scrapers for:
Building a talent pool
77% of recruiters say they are more efficient in their recruiting efforts when they have a solid understanding of the market and talent pool they’re recruiting from. A talent pool is a list of candidates who can be qualified for current or future job openings in an organization. Recruiters can use web scraper bots to collect lists of candidates from employment websites in order to create an up-to-date talent database for the organization and build relationships with candidates before they are ready to apply.
Targeting candidates in specific geographical regions
Some web scrapers integrate IP proxies to enable access to region-specific data. This enables recruiters to target candidates in a specific region in case the role requires employees to work on-site.
See our ultimate guide to proxy server types to learn more about the different proxy types, their benefits, and applications.
Bright Data’s Data Collector extracts real-time public data from online platforms and delivers it to businesses on autopilot in different formats. It also integrates residential and datacenter proxies to extract data from specific geographical regions, as well as bypass website protection mechanisms that limit bot access to data.
Comparing candidate qualifications
Web scrapers can gather data about candidates from targeted platforms such as their profiles on social media accounts and employment websites. Web scrapers can also be programmed to extract qualification-specific data such as education or skills fields in a candidate’s profile. Recruiters can leverage the extracted data to analyze candidates’ qualifications and estimate their match to specific positions.
Collecting candidate contact details
Web scrapers can collect candidates’ contact details such as email addresses and phone numbers from employment websites to enable recruiters to reach out and contact candidates qualified for open positions.
Job market analysis
Understanding salary ranges
Most recruitment websites, such as Glassdoor or Salary.com, provide data about salary ranges for specific roles, years of experience, and geographical regions. Web scrapers can be used to collect salary ranges for the organization’s job openings in order to help recruiters understand candidates’ expectations and optimize their salaries accordingly.
Identifying job requirements
Recruiters can understand the education and skill requirements for specific roles by monitoring what their competitors search for in a candidate. Web scrapers can collect data from a business competitor’s job listings and job post details to help recruiters create better job descriptions.
Create competitive job offers
Web scrapers can also gather information from competitors’ websites about training opportunities, flexibility in working hours or vacation days, benefits, etc. By understanding competitors’ offerings, recruiters can optimize their job offerings and benefits packages in order to attract candidates and avoid losing them to competition.
What are the best practices of web scraping for recruitments?
To get the most of web scraping capabilities in the recruiting process, businesses can follow best practices, which include:
- Choosing the right employment platform: There are hundreds of employment websites and platforms today, however, some platforms provide a wider scope than others, while others can be more position-targeted. For example, LinkedIn and Indeed have a wide database for all types of positions around the world, whereas GitHub and Stack Overflow are more focused on programming and tech positions.
- Updating candidate pools: There are ~15M job openings and 35,000 skills listed on LinkedIn alone, and being updated with every hiring. Therefore, it is important to scrape employment websites frequently in order to keep the talent pools up-to-date.
It is crucial to check if the website you want to scrape allows bot scraping, otherwise, you will face legal issues, such as the case of LinkedIn vs. hiQ Labs where hiQ breached the LinkedIn User Agreement, which specifically prohibits automated access and scraping.
Read our top 7 web scraping best practices guide to learn more about web scraping best practices.
For more on web scraping
To get a better grasp of web scraping and its applications, feel free to read our articles:
- 3 Powerful Business Use Cases of LinkedIn Data
- Social Media Scraping: Tools, How-to & Case Studies
- Top 18 Web Scraping Use Cases
If you believe your business could benefit from a web scraping solution, scroll down our data-driven list of web crawlers.
And we can guide you through the process
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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