Using various types of job postings data, you can answer multiple questions, like
However, all of these questions can be answered only based on the quality and quantity of data you can get.
If the data sample is too small or too dirty (multiple duplicates), you can't trust your results.
So, let's have a look at how the best dataset should look like
First of all, we need to decide on the context of Job Data Usage to talk about the best datasets.
There are a few most common job posting data buyers and/or users -
All of them will have very different needs regarding data volume, quality, and delivery technology.
Job boards may need automatic plug-in in the system, real-time data gathering capabilities, strict deduplication process. In many cases, the uniqueness of job data will be higher in priority compared to volume.
Research agencies may need big datasets with a strict split from what industries that hiring request came from. Also, they may have a project to make a historical analysis of a particular set of companies. In that case, you will need a clear enrichment and matching mechanism to connect hiring data with the raw version of "Company Name" in many cases to an actual company ID. In terms of data quality and quantity, it is on the higher level of need based on company reputation, maturity, and team seniority. When a new research project starts, a data analyst job posting is widespread for many companies to start hiring for. The same logic goes to data entry job posting.
In-house Data Science Team may have similar requests as a research agency, but with more niche business needs. Compared to the agency with changing needs in terms of the final dataset, the in-house team, in many cases, will be more structured in that sense. In terms of quality and quantity, frequently project-based work from private businesses is not on top-level and requires general data coverage.
Recruitment agencies will need clear separation of hiring data from recruitment and staffing agencies. Also, the ability to check their own set of current customers is essential. Finally, multiple firmographics and hiring filtering capabilities are excellent to have!
Sales/Marketing Teams will have a strict number of data points they need to have to make data work -
As a result, if the company provides analytical data service, they will review data posting jobs.
So what dataset can we consider as the best for all mentioned buyer personas?
Where can you look for job posting activity datasets? Every provider has a different offer -
In the context of a job-posting-matching system, the data are a good source of structured information on active job postings. They are suitable for determining who might or might not apply for an open job based on answering a specific set of questions.
Data about online job postings can be helpful for the following purposes:
A) Match applicants to employers to determine who is hiring
B) Help employers get in touch with applicants to learn their level of interest
C) Identify people who shouldn't be placed in a job if they don't fit a specific set of requirements.
D) Find companies in buying mode for particular service or expertise based on job posting data
The key benefits of using this data are that it provides a lot of information about who is posting positions at any given moment, how long they post positions for, and who they post them for.
One thing to note is that this data is not as complete as what the HR office can provide.
Job Description can show what skills and tech company possess currently and what expertise it is lacking.
Job Name can show a generalized summary of job description datapoints and size down the search.
Job Compensation can show company maturity, need, and health. Many companies with no clear goals and vision about the role put many requests in the job description, keeping compensation as low as possible.
Job Seniority can be used as an additional filter or a comparison between job name/description and a vision about role seniority.
Job Remote Policy can be used as another filter and present company working culture/policy. You can find parameters as Only In-house, Remote Only, Open to remote, Travel required, and some other points.
There are several benefits of using active postings data based on user profile - Business Entity or Consumer (Job Seeker).
As for job seekers, there is no denying that finding jobs nowadays is more important than ever before. Finding a job and having a stable job that you look forward to each day are the keys to happiness in today's world. Many people go for so-called 'recruitment sites' to search for a company to work for and then apply online. This is the way most job searchers still search for jobs as they've seen others trying their luck on these websites (and websites like these).
For them, sites with deep filtering capabilities give the best benefits to find as fast as a possible desired list of hiring requests and apply to them.
As so business goals and hiring companies, in particular, different actors have different benefits.
So, hiring managers out there are aware of the 'active job postings' and 'job ads' that they see these days and if they have been following any 'recruitment campaign' using job posting as the tool of recruitment. They have a pretty good idea of what the search for a job entails. That's why they use these data to find out a lot about the job seekers and how well they can adapt to any changes that the company will bring.
Hiring companies can check what skills are in significant demand on the market, train their internal employees, or double-check on where the market is going.
As such analytical business goals, companies can get a pretty inexpensive dataset with a current industry snapshot. For example, compared to interviewing 100 CTOs of Fortune 500 companies in a particular industry on their future goals and initiatives, the researcher can buy a specific hiring dataset to get an external view of the situation.
As for growing business decisions, based on service and product offering, many marketing and sales teams can have better targeting in their outreach and digital ads campaigns using the latest job postings data as intent and/or sales triggers.
There are a few options on the market in terms of data pricing, but mostly all of the offerings will be fixed monthly fees with some number of records to use/export/consume.
Let's compare a few -
Most data providers collect data via web scraping job postings. They go on job boards daily using custom crawling bots. Also, some vendors develop in-house parsing farms to collect data directly from company websites. Finally, Job posting data can be collected from search index sites like Google.
Based on the Privacy Policy and Terms of Usage on several websites, the collection of postings can be confidential for non-registered users. Also, let's take a more global view on the problem. Some industries have a secret policy and needs in the security industry, so hiring is done only via trusted recruitment agencies with the anonymity of the hiring company.
Job post should contain a clear list of required skills, job compensation, level of seniority needed, list of perks, and benefits for employees. Based on the shortage of talent, the different job postings will have another description. The more competitive the position (many candidates available on the market), the less clear and appealing the hiring post will be. Also, it might depend on the company HR talent who was writing a copy. Based on current trends, compensation and a clear list of duties will be the most critical parts of a resume. Company culture, work conditions, and opportunities will go next in importance.
Using various types of job postings data, you can answer multiple questions, like
However, all of these questions can be answered only based on the quality and quantity of data you can get.
If the data sample is too small or too dirty (multiple duplicates), you can't trust your results.
So, let's have a look at how the best dataset should look like
First of all, we need to decide on the context of Job Data Usage to talk about the best datasets.
There are a few most common job posting data buyers and/or users -
All of them will have very different needs regarding data volume, quality, and delivery technology.
Job boards may need automatic plug-in in the system, real-time data gathering capabilities, strict deduplication process. In many cases, the uniqueness of job data will be higher in priority compared to volume.
Research agencies may need big datasets with a strict split from what industries that hiring request came from. Also, they may have a project to make a historical analysis of a particular set of companies. In that case, you will need a clear enrichment and matching mechanism to connect hiring data with the raw version of "Company Name" in many cases to an actual company ID. In terms of data quality and quantity, it is on the higher level of need based on company reputation, maturity, and team seniority. When a new research project starts, a data analyst job posting is widespread for many companies to start hiring for. The same logic goes to data entry job posting.
In-house Data Science Team may have similar requests as a research agency, but with more niche business needs. Compared to the agency with changing needs in terms of the final dataset, the in-house team, in many cases, will be more structured in that sense. In terms of quality and quantity, frequently project-based work from private businesses is not on top-level and requires general data coverage.
Recruitment agencies will need clear separation of hiring data from recruitment and staffing agencies. Also, the ability to check their own set of current customers is essential. Finally, multiple firmographics and hiring filtering capabilities are excellent to have!
Sales/Marketing Teams will have a strict number of data points they need to have to make data work -
As a result, if the company provides analytical data service, they will review data posting jobs.
So what dataset can we consider as the best for all mentioned buyer personas?
Where can you look for job posting activity datasets? Every provider has a different offer -
In the context of a job-posting-matching system, the data are a good source of structured information on active job postings. They are suitable for determining who might or might not apply for an open job based on answering a specific set of questions.
Data about online job postings can be helpful for the following purposes:
A) Match applicants to employers to determine who is hiring
B) Help employers get in touch with applicants to learn their level of interest
C) Identify people who shouldn't be placed in a job if they don't fit a specific set of requirements.
D) Find companies in buying mode for particular service or expertise based on job posting data
The key benefits of using this data are that it provides a lot of information about who is posting positions at any given moment, how long they post positions for, and who they post them for.
One thing to note is that this data is not as complete as what the HR office can provide.
Job Description can show what skills and tech company possess currently and what expertise it is lacking.
Job Name can show a generalized summary of job description datapoints and size down the search.
Job Compensation can show company maturity, need, and health. Many companies with no clear goals and vision about the role put many requests in the job description, keeping compensation as low as possible.
Job Seniority can be used as an additional filter or a comparison between job name/description and a vision about role seniority.
Job Remote Policy can be used as another filter and present company working culture/policy. You can find parameters as Only In-house, Remote Only, Open to remote, Travel required, and some other points.
There are several benefits of using active postings data based on user profile - Business Entity or Consumer (Job Seeker).
As for job seekers, there is no denying that finding jobs nowadays is more important than ever before. Finding a job and having a stable job that you look forward to each day are the keys to happiness in today's world. Many people go for so-called 'recruitment sites' to search for a company to work for and then apply online. This is the way most job searchers still search for jobs as they've seen others trying their luck on these websites (and websites like these).
For them, sites with deep filtering capabilities give the best benefits to find as fast as a possible desired list of hiring requests and apply to them.
As so business goals and hiring companies, in particular, different actors have different benefits.
So, hiring managers out there are aware of the 'active job postings' and 'job ads' that they see these days and if they have been following any 'recruitment campaign' using job posting as the tool of recruitment. They have a pretty good idea of what the search for a job entails. That's why they use these data to find out a lot about the job seekers and how well they can adapt to any changes that the company will bring.
Hiring companies can check what skills are in significant demand on the market, train their internal employees, or double-check on where the market is going.
As such analytical business goals, companies can get a pretty inexpensive dataset with a current industry snapshot. For example, compared to interviewing 100 CTOs of Fortune 500 companies in a particular industry on their future goals and initiatives, the researcher can buy a specific hiring dataset to get an external view of the situation.
As for growing business decisions, based on service and product offering, many marketing and sales teams can have better targeting in their outreach and digital ads campaigns using the latest job postings data as intent and/or sales triggers.
There are a few options on the market in terms of data pricing, but mostly all of the offerings will be fixed monthly fees with some number of records to use/export/consume.
Let's compare a few -
Most data providers collect data via web scraping job postings. They go on job boards daily using custom crawling bots. Also, some vendors develop in-house parsing farms to collect data directly from company websites. Finally, Job posting data can be collected from search index sites like Google.
Based on the Privacy Policy and Terms of Usage on several websites, the collection of postings can be confidential for non-registered users. Also, let's take a more global view on the problem. Some industries have a secret policy and needs in the security industry, so hiring is done only via trusted recruitment agencies with the anonymity of the hiring company.
Job post should contain a clear list of required skills, job compensation, level of seniority needed, list of perks, and benefits for employees. Based on the shortage of talent, the different job postings will have another description. The more competitive the position (many candidates available on the market), the less clear and appealing the hiring post will be. Also, it might depend on the company HR talent who was writing a copy. Based on current trends, compensation and a clear list of duties will be the most critical parts of a resume. Company culture, work conditions, and opportunities will go next in importance.
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