Job posting data is any job-related information you find on a job board, job aggregator site, or a company’s careers webpage. So, if you have ever looked for a job offer on one or all these platforms, you were essentially perusing through multiple job posting data. While you may or may not have found what you were looking for, technology has made this perusal process a lot easier, as we’ll detail later. But first, we’ll discuss the entire scope of job posting data.
What is job posting data?
As stated, job posting data is any job-related data you find on a job board, job aggregator site, or careers webpage. This data can be grouped according to the following categories:
- Job title
- Job description and requirements
- Required skills
- Hiring location
- The name of the firm offering the employment opportunity and their contact details
Importance of job posting data
This data is useful to companies as well as individuals seeking employment.
Benefits to individuals
For individuals, it helps them identify companies that are hiring, thus setting the stage for them to begin the application process. For the employed, job search data provides insight into how other companies pay employees working in particular positions. They could then use this information to negotiate for a pay rise or know a better-paying employer.
Benefits to companies
Job posting data presents the following opportunities to companies that increase these firms’ revenue when used accordingly.
- Job aggregator sites use job posting data to keep their databases and sites updated.
- Research organization collects job data for analysis purposes – to establish market trends and the needs of the labor market.
- B2B firms looking to increase their customer rely on job posting data to identify companies that require the services they offer.
- Staffing organizations require job posting data to keep their databases up-to-date.
- Companies can use job posting data to monitor competitors’ moves with regards to the compensations and benefits they offer employees in different positions. This information could help these companies improve their hiring practices and strategies.
You first need to collect the job posting data for you to enjoy these benefits. However, the numerous job boards, job aggregator sites, and careers webpages could make this a time-consuming undertaking, especially when manual data collection is done. The inefficiencies of manual forms of data extraction can be resolved by going the automated route, i.e., web scraping job postings.
How to collect job posting data
Web scraping is the act of extracting data from websites. Although it refers to both manual and automated data harvesting, the term ‘web scraping’ is commonly used when talking about the latter. Thus, web scraping job postings is the use of automated tools to harvest job posting data from the various sources mentioned above.
Automated web scraping can be done using in-house tools or ready-to-use applications. As the name suggests, in-house tools are customized web scraping tools developed from scratch, using Python programming language. This alludes to the fact that having a technical background is a prerequisite to developing in-house tools.
Nonetheless, if your company does not have a team member with the technical know-how required to develop an in-house web scraping tool, you could go for ready-to-use applications. This second option is plug and play; that is, these apps do not require any adjustments save for the instructions regarding the websites or data they should look for over the internet.
Procedure for web scraping job search data
With that being said, both in-house tools and ready-to-use applications follow the same procedure when web scraping job postings. They first scour the internet, with web crawlers’ help, to identify websites that contain job posting data. They then inspect the webpages looking for the data, which they subsequently extract if it meets the instructions’ requirements. The tools then convert the data into a structured format that is easy to understand.
It is important to remember that a code should be written for each step when developing an in-house web scraping tool. However, using Python makes the process a tad easier given that this programming language has libraries where developers can access functions that perform the requests required for web scraping.
Anti-scraping
Though web scraping job postings is a convenient way of collecting job data, it is not always smooth. Large job aggregator sites and job boards have in-built measures that stop scraping. These measures, collectively known as anti-scraping techniques, are deployed to protect the information therein.
IP blocking is a common anti-scraping technique, alongside the use of CAPTCHAs, sign-in and log-in requirements, User-Agents, and honeypot traps, among others. Fortunately, thanks to proxy servers, you can prevent IP blocking. A reputable proxy service provider is a surefire way of ensuring that you collect job search data smoothly.
A good proxy server and your preferred web scraping tool are essential prerequisites when collecting job data. They help unlock the benefits of job posting data.
