Job Market Monitoring with Job Data Feeds

job market

Markets across the world are influenced by changes in demand and supply. The global Job Market is no different. Data analysts and market observers continuously track multiple metrics to get the pulse of the job market and derive valuable insights. These often include finding out which positions are most “in-demand” and which sectors are seeing a downside. Policymakers and government institutions need to make sure that no alarming levels are breached while making the most of the opportunities arising in the job market.

For instance, as the Coronavirus spread this year, governments across the globe implemented different levels of lockdowns. People also stepped out less out of fear of the virus spreading. This led to more and more customers opting to shop online. As a result, job boards and agencies saw a higher number of job posts for delivery and logistic executives. If you look at the stocks of a lot of eCommerce stores and delivery businesses, you can see them soaring around the same time.

The Benefits of Using Job Data for Market Monitoring

Job Data isn’t studied only to understand how different sectors are performing. Its purpose varies based on the final objective

1. Governance of the Job Market

Job Data is used for governance across industries. The growth of newer technologies has seen new certifications that help many professionals stand out among the crowd. These professional courses and certifications also set a standard among adopters of these technologies. At times, analyzing the Job Market can also give insights into the growth of niche sectors. It may also bring to attention the requirement of bringing in new rules and legislations to tackle possible problems that may arise.

For instance, a steep rise in demand for blockchain developers signaled the fact that cryptocurrencies had become hot commodities in their newly found market. This led to crackdowns by different governments around the world, along with new regulations laid down by several central banks. This was done to make sure that the technology wasn’t misused for activities like money laundering by those who wanted to bypass traditional banking methods.

2. Recruitment Analysis of Job Market

Market researchers who want to spot trends in hirings usually go through the recent and historical Job Data feed. Trends like a peak in demand for “Data Scientist” can be spotted if you analyze job posts in tech-niche for a considerable period.

Human resource personnel also use such data to improve their recruitment methodologies, update job descriptions, and set clearer expectations among job seekers.

3. Policy-Making in the Job Market

Government agencies, both central and state often use Job Data to decide on certain policies. For example, governments in multiple countries expect the growth of renewable energy resources to bring in thousands of new jobs. This has led them to incentivize these companies through tax breaks and land acquisitions.

Policies like these help governments make the most of opportunities that arise in the Job Market and enable the creation of more jobs in their geographical locations.

4. Cost Optimization

Job posts can be analyzed by companies who are about to hire new employees. This can be done by studying similar job posts and finding out the average salary offered. Other data points can also be taken into accounts— such as years of work experience and the must-have requirements. The salaries for the posts that are closest to the job-post in question can then be picked up and those can be used as a reference when adding the expected salary package and its upper and lower limits. This data can also be used to negotiate salaries with new hires by human resources. Such data-driven practices enable companies to optimize their expenses.

How can you get this Job Data?

While the internet is the largest and most frequently updated source of job posts, scraping job data, and creating a continuous data feed is no simple task. The reason behind this is the fact that you will need to scrape multiple job boards and job websites to aggregate enough data to ensure both variety and quantity.

To ensure data quality on top of this (so that your research study does not go waste), you will need to make sure that you have proper data cleaning, validation, and verification modules placed on top of the scraping mechanism. You have three choices that you can choose from, and each would fit the bill for different types of requirements.

1. Writing a DIY code

It can be a solution if the Job Data to be scraped is a one-time requirement. Even then, you will need a developer who is experienced in scraping data from the web. You will also need to follow proper protocols and rules to make sure that your act of extraction and use of the data is legal. In case you do not have a developer who is well versed in one of the languages that are used for web scraping today, such as Python or Golang, you can also go for no-code software.

2. No-Code Based Software

This can enable your business team to fetch Job Data from various websites without the need for coding. This would still have a learning curve, but it is bound to take much less time than learning to code and writing a script to scrape job posts from the web. This software is usually paid and in case you want to switch from one software to another, your team will need to go through a re-learning process. Another downside of using a tool that you can subscribe to is that it will always come with some restrictions and pain points and for each issue that may arise, you will have to wait until the next version is released.

3. You can use an end to end Job Discovery and Analytics Solution

that enables you to focus on the research or study instead of worrying about the various aspects of the data at hand. Such a service would usually have all the data processing stages that need to be implemented after the Job Data is scraped, built-in.

Why JobsPikr?

Our team at PromptCloud understands the importance of Job Data in today’s world and hence we came up with an automated job discovery solution, called JobsPikr. With this solution, you will not just have a job feed that is updated in real-time, but also other features like:

Having such a solution will cover every aspect of data extraction and integration. This would help you to obtain more accurate results and make inferences that can boost your company to make data-backed decisions.

Key Takeaways from the World Economic Forum Report on The Future of Jobs

If 2020 taught us anything, it is that we should always be prepared for outliers that will eventually become the norm. In a paradigm-shifting year, it is important to analyze how the micro-level disruptions will play out at a macro level economic cycle. The World Economic Forum (WEF) is an international organization that consists of financial gurus across industries who cumulatively work together to analyze and predict what the future of the economy looks like by and large.

The WEF consolidates annual economic and business research documents, including the “The Future of Jobs Report, 2020,” which was recently published. This is usually regarded as The Bible of what is to come.  This year, the report talks about how the changes embraced in 2020 by the job market is here to stay for good. These changes are not a stop-gap solution. The future adoption of technology and therefore the future of job boards is highly dependent on the data derived from the study.

It was projected that by 2025, automation and a new synergy of labor between humans and bots will disrupt roughly 85 million jobs globally across 15 industries and 26 economies. But, “COVID-19 has accelerated the arrival of the future of work,” said Saadia Zahidi, Managing Director, World Economic Forum.

Fascinating? Beyond measure. Let us then break down the report and talk about the most interesting takeaways for the future of job markets.

A). New Sense of Urgency for Redefining the Job-Roles Revolution

With the shift brought about by the year that was 2020 and the parallel shift to automation, we are now galloping towards the fourth industrial revolution. Which will create 97 million new roles in industries like artificial intelligence, and in content creation fields. The future doesn’t belong to bots. But in the hands that shape and control them. The tasks where humans will always have a comparative advantage are managing, advising, decision-making, reasoning, communicating and interacting. Parallelly, there will be a surge in demand for candidates who can fill green economy jobs, roles at the cusp of the data, and artificial intelligence economy.

Therefore, nearly 50% of the current workforce will require to reskill or upskill their core skills. To meet the unabated pace of technology, it’s estimated that jobs that are considered redundant will go from more than 15% of a company’s total to less than 10%.

By this data alone, the WEF predicts that nearly 85 million jobs could be displaced for roles that divide human labor from machine labor! While almost 97 million jobs could be created that require skills around machine interaction and algorithmic expertise.

Graph1: Share of Tasks Performed by Humans vs Machines, 2020 vs 2025 (Expected) by Share of Companies Surveyed

B). Remote Working is The Future of Jobs, with Certain Adaptations

The biggest repercussion of 2020 is remote working. This is the future. Employers say there are the possibility and proclivity to move 44% of the workforce to operate remotely. But this hasn’t come at a real cost. Business leaders expect some unpleasant effects on lower productivity. That means that adopting remote-working technology has become an absolute need of the hour.

On the flip side of remote working comes a looming sense of disconnect between employees and the absence of an opportunity to feed off each other’s energy. To address this primary concern, about one-third of all employers said they will take steps to create a sense of community and belonging within the workforce.

Remote Working Suitability Graph
Graph 2: Suitability of Job for Remote Work by industry (Source: Slack)

C). Future-Proofing Jobs is The Future of Jobs

Career pivots are to become the “new normal”.  The WEF research indicates that a growing number of people are making career switches to entirely unchartered occupations. LinkedIn data gathered over the past five years says that almost 50% of career shifts into data and artificial intelligence are from fields at the opposite end of the spectrum. Karin Kimbrough, Chief Economist at LinkedIn, says that the transition of large populations into jobs of the future has a huge potential for the public and private sector.

If help can be provided to individuals (or companies) who are re-directing workforce funding and investment to identify the skills that would have a disproportionate impact on opening up more ‘secure’ career paths, we can make a real difference in addressing the unprecedented levels of unemployment that we’re seeing globally.          

According to WEF data, companies want to re-skill and repurpose nearly half the workers that find themselves displaced by technology instead of hiring a new set. This inclination towards retention and against layoffs represent a sentiment among business leaders that must be capitalized upon.

Graph 3: Planned Business Adaptation in Response to COVID-19

D). The Future Of ‘Skills’ and The Time it Takes to ‘Reskill’

According to The Future of Jobs Survey, core skills such as critical thinking, analysis, and problem-solving are unsurprisingly at the top of the reskilling and upskilling priorities for business leaders. That’s the age humans need to have over automation. Newly emerging in 2020 are left-brained and emotional intelligence skills such as resilience, stress tolerance, and flexibility.

Data from Coursera implies that employees could start gaining the top 10 skills for each emerging profession in people and culture, content writing, sales, and marketing in less than two months. Those wishing to expand their horizons to product development and artificial intelligence could do so in two to three months, and those switching into cloud computing and engineering could champion the new skillset under five months.

There has been a 5x increase in employers offering employees online learning opportunities, and a nine-fold enrolment increase in candidates accessing online learning through government programs.

Those in employment are more inclined towards soft-skills development courses; those who are unemployed are picking up skills such as data analysis, computer science, information technology, and coding.

Top skills till 2025 graph
Left Table: Top 10 in-focus skills of those in employment
Right Table: Top 10 skills for those who are unemployed

The Future of Jobs essentially maps the jobs of the future based on current data reaction to the pace of technology.

It also accounts for the pandemic led shifts and the accelerating effect it has had on the need for automation. Technology is going to be at the core of all tectonic changes in the future. Job automation, primarily. But like we mentioned, the future is not bots and codes. The future is in the hands that code and control.

Relying on a Job Parser for your Job Board?

Job Boards and job agencies are helping our generation tackle one of the greatest problems in today’s world — unemployment. The difficulty for fresh graduates or even those who are seeking new jobs isn’t always the lack of a job, but instead the difficulty in finding the right one. Job Boards in particular have proved helpful by aggregating jobs from thousands of job sites and company career pages. But the ability of a Job Board to help candidates is based on how well presented and updated the data on its website is.

Since most Job Boards use an internally handled or an externally subscribed Job Parser, data quality is often questionable. The problem with Job Parsers is that they lack an inbuilt intelligence. They can gather job listings from as many websites as you add to their system, but when pulling the data points related to each job listing, it is not able to ensure the correctness of the data. It may not be able to interpret data points from different websites in the same way, and this may lead to unclean, or non-uniform data on your website, which in turn would inconvenience candidates.

How Reliable is a Job Parser?

Job Parsers collect job data from different websites but are unable to provide any intelligence to interpret the data that it extracts. For example, suppose one website lists the required experience in months while other listing websites do it in years. A job parser would not be able to convert the experience required to years for the one where it is given in months.

Let’s take another example. Suppose there are two job listings- one for a Python Developer and another for a Backend Developer, where the must-have is Python. Both essentially are listings for a software developer who codes in Python. But a Job Parser would not be able to add these specific tags to the job listings. And thus, someone who searches for the term “python developer” would probably not be able to see these listings together.

Job Parser
Fig: Visualizing how Job Parsers Work

Using a job parser is a highly mechanical way of gathering job data for your website. It is an unintelligent box that scrapes job data from multiple websites, bunches them all together, and provides you with a single feed. Just as you can visualize in the image above. Its limitations are not just the fact that it cannot interpret the data it parses, but also that it cannot validate the data.

Take this for an example, just in case your parser encounters job listings of a company that doesn’t exist, it cannot use other data sources to validate whether the company is a genuine one. Job parsers can also produce job listings that may be stale or even expired since they lack the capabilities to weed out such listings.

Upgrading or Updating your Job Parser?

In case you are facing multiple bugs in your job parser, and the percentage of unclean data is rising, you may go in to update your parser, or make some configuration changes. You may even upgrade your parser by moving to better software, or by changing your in-house tool. These updates or upgrades will help you in the short term, but when you add a new website for scraping job data, or when one of the existing websites changes its UI, you will again have to take on the task of updating the parser.

Since the parser does not have built-in intelligence and errors and issues will only be spotted once the data flows into your website, you can encounter two major problems. First,  You might have to make frequent changes. Second, the amount of bad data on your website will only be limited by how fast you can update the parser.

Due to these issues, a one time fix is not a solution and will not enable you to provide a better experience to your customers. When it comes to upgrading the user interface or adding new filters, it may require a one-time effort. But unless you keep updating the Job Parser itself, dirty data will leave the other features useless. For instance, if your job listings do not have the correct tags, a well thought out filtering system with multiple options will be unusable.

Also, if you are maintaining the parsing software on your infrastructure, you will need to maintain and update the infra based on the amount of data that you need to collect in a given time. As your data requirements grow, your infra efforts and costs will also increase.

How can a DaaS Solution Help?

Based on what we have seen before, a Job Parser won’t reach the mark if you want to create a Job Board that stands out amongst the competition. Anyone can create a web scraper and generate aggregated job data from tens of websites. Developers can even write their DIY code to build such a system, given enough time. However, the process that we saw above needs a few more steps to truly create an end to end job feed that companies can use confidently and provide job seekers with the best experience. This is possible because the data produced at the end of this pipeline shown below is sorted, tagged, cleaned, validated, and verified. The result, in this case, can be vastly different from the pipeline that we saw above.

Fig: Visualizing how a Job Data Feed Solution Works
  1. Data Cleaning- A job scraping solution that provides you with a continuous job feed would automatically clean the data and check for imperfections. Job postings with erroneous data such as one with the last date of submission which lies in the past, may not be forwarded to you. Basic spelling checks and data quality checks are also made.
  1. Data Validation- Data Validation is very important to make sure candidates are not confused by mistakes in the job listing content. This can include different types of checks. If a data point should be numeric- like age or salary, then those checks are included in the data flow. If you only want job posts that have the salary included, then that can be used as a pre-filter for your job feed. You would want to make sure all the job posts have some specific data points that can be validated too.
  1. Data Verification- This step is not compulsory but can be used as an add on for providing even better results to job seekers. Certain checks like validating the companies that are mentioned in the job posts, or using Glassdoor data to recheck certain data points can be done to filter out any post that doesn’t fit the bill. These can be rejected entirely, or provided in a separate flow that would need manual verification.
  1. Data Enrichment– Not every website will provide you with the same key-value pairs. Not all will even list all the information in the same way. One site can have a salary in Dollars, while another can mention it in Euros. A single key can have different names on different websites. Required years of experience can also be Years of experience required in some websites.

Normalizing the data and collating data points from different websites that mean the same thing, can provide a more uniform job feed to your candidates and as they browse through multiple job listings, they can compare those more easily. Filters are also likely to work better when you enrich the Job Feed.

To Sum Up

Job Boards that want to take a leap, should move on from Job Parsers to Integrated Job Discovery Solutions. One of the leading data providers (as mentioned in a report by Forrester) in the Job Industry is our job scraping service JobsPikr. We have been providing clean job feed to multiple clients who use the data for anything starting from Job Boards to Market Research. Having an end to end solution like ours would mean that you can focus on your core business of maintaining a Job Board and providing additional services to companies and candidates while we provide you with a continuous job feed in the format and storage medium of your choice (you could also opt for API integration). Having the best quality data is the key to any successful digital business and Job Boards can revolutionize the industry by transitioning from Job Parsers to automated Job Feed solutions.

Harnessing Artificial Intelligence for Job Boards

Recruiting the right talent does not come easy. It is energy-intensive and costly. And if you think that hiring the right talent is costly, can you imagine the cost of hiring the wrong talent? A whopping $240,000! If the stakes are that high, it only makes sense to harness the power of Artificial Intelligence for your job board. A recent LinkedIn report suggests that 67% of recruiters surveyed, reported AI was saving them time.

Job boards have been around for a while. It is time for them to integrate what they have been doing, with the power of artificial intelligence (AI) and machine learning (ML). You cannot survive if your sole job is to connect candidates with job opportunities. You have to keep adding a whole range of products to make candidate mapping as robust and as interactive as ever.

There are thousands of job boards out there. How do you establish authority in this domain? Through AI and ML. Major companies across industries including Hilton, Humana, AT&T, Procter & Gamble, and CapitalOne are already doing it. If you need more convincing, here’s why you need to jump on this bandwagon today.

Better Candidate Matching

Job matching is the core reason for the existence of job boards. With both recruiters and candidates being spoilt for choice, you have to really nail this. If they don’t like what you have to offer, they will just as simply move on. In order to absolutely ensure this, some job boards have already started hiring AI vendors.

A quick and seamless approach to sourcing and qualifying candidates is paramount. How does AI ensure this? By matching candidates from the pool of resumes to the clients’ criteria, before flooding their internal pipeline with this top talent. You then essentially automate the most time-consuming part of the process: companies spending hours sifting through top talent.

Passive Candidates

There are innumerable great job candidates who may not be applying to your company simply because they are just not looking for it currently. Or have not thought about it later, but would be interested if a great opportunity came along. In fact, 70% of the workforce is not actively searching for jobs, but would not mind making that leap if the opportunity is great.

Hiring managers need to be able to find these candidates and seek them out with their pain points.  By using AI and NLP (natural language processing) to scan online CVs and profiles, you can send valuable leads to recruiters.

Enhancing the Candidates Experience

The power of top talent is unmatched. Potential candidates truly wield higher powers than recruiters. The matches they receive on a job board or job aggregator will determine the bounce rate. If they are bombarded with irrelevant jobs, they will just pull the plug.

Apart from this, a major advantage of AI is that it interacts with and engages candidates at each stage of their job search. From chatbots that can tell them about their potential KRAs, to automated interview scheduling; these tools can be a major differentiator and clutter breaker.

Top Talent Pool

Candidate matching is as much for the present as it is about maintaining a deep pool of resources for the future. The recruiter may not have an immediate requirement now but it can keep the candidates they like in the loop and well-acquainted with their company.

This is very important right now as employer branding is a top-notch priority for all companies; big or small. This may also help convert hesitant candidates and give them a nudge in the right direction.

Managing Administrative Tasks

While you can’t separate the human from Human Resources, most of the administrative processes have to be automated. The introduction of bots in recruiting workflow is making this process easier, better, and faster for HR teams. For example, in Sweden, a robot named Tengai, the product of Furhat Robotics is creating magic by conducting job interviews.

Job boards are also using AI to free up their time to focus on more agile work and actually getting to know their top talent. Artificial Intelligence and Machine Learning will only become more creative and effective over time.

Eliminating the Role of Unconscious Bias

Job boards are adopting AI and ML tools to manage the large volumes of resumes and candidates and eliminate the possibility of any kind of bias in the process. Machine-filtered data and predictive tools and analyses help to screen candidates for more objective decisions. However, it needs to be ensured that AI-based assessments are properly designed and tested to be free of any potential algorithm bias. Canada’s largest bookstore chain – Indigo experienced three times growth in the number of qualified talents matches. As a result of an efficiently optimized talent pool. If your bookstore chain can do it, so can you do it for your job board and become one of the most coveted ones.

Predicting Candidate Mobility

A fascinating area to explore AI for job boards is to predict candidate mobility. By using well-designed algorithms, job boards have the potential to notify when someone might be ready to look for a new job. This means these potential hires can be pushed to the most relevant opportunities. This is essentially harnessing AI and ML to in turn harness almost real-time data.

Not to forget, companies are most likely to invest in job boards that have the freshest resume databases.

Future of AI and Job Boards

When asked about the role of AI and machine learning in HR and job boards, Mark Brandau, principal analyst in Forrester’s CIO team said, “All vendors are moving in that direction without question. It’s the way of the future.”

The future belongs to the one who works in tandem with technology. That being said, it doesn’t belong to bots. It belongs to people who know who two harnesses the magic of bots.

Based on a recent Glassdoor report, 66% of millennials will want to leave their present jobs by the end of this year. If this were to come true, it will only add tremendous pressure to the workload and challenges recruiters face. It will also open the floodgates to a whole bunch of trained top line talents. However, despite the challenges, with the massive increase in the use of big data analytics, Artificial Intelligence, Machine Learning, and the Internet of Things, etc. The global human resource management market is expected to grow and reach USD 30.01 billion by 2025. The choice is now yours. Change with the times or get changed.

If you liked reading this blog, we are sure you might like this as well. Please leave us your valuable feedback in the comments section below. 

What can we learn from LinkedIn’s in-built job wrapping process?

There are job portals. And then there’s Linkedin. The search intent of the term “LinkedIn” on search engines has almost doubled in the past decade. Rightly so, not a lot of job boards match potential candidates to jobs the way LinkedIn does. LinkedIn uses job wrapping super successfully to cutting through the noise of half a billion LinkedIn users. Landing in a potential candidate search result is not just a happy coincidence.

Let us then crack the code. The code that sets LinkedIn apart from other job boards and how you can make your job board just as good with the help of premium job scraping services.

How LinkedIn Job Matching Works

Ranking in the upper rungs of a LinkedIn search requires optimizing for both LinkedIn’s tech and the human inclinations of the recruiters. The advanced algorithm considers the overall activities of the user account and studies where the interaction is the highest.

The profile headline and recent job title are probably allotted the most weightage and need to be very specific so that it seamlessly matches the searcher’s intent. An employer is highly likely to begin their search with a simple search string: specific job titles.

The headline works like an elevator pitch. It is the first thing people invariably read. It is usually the first filter employers use.

How do recruiters look for something niche on their broad list of KRA and experiences listed? LinkedIn Recruiters built-in skills is godsend for employers who want to add and advanced layer of filtering.

For example, you go into a keyword search and put in different buzzwords that you pull from the specific job description. If you are looking for a front-end developer, you could put in Javascript, CSS, HTML, HTML5 so on and so forth.

Candidates living in the vicinity have a higher chance of accepting your job offer than the one living on the other coast. Right skills don’t cut it anymore, selecting a candidate with the right skills in the shortest possible time is the need of the hour.

All said and done, one of the most powerful tools that draw employers to LinkedIn as a job board is its job wrapping. This tool allows recruiters to automatically post jobs from their career pages into LinkedIn job vacancies. Pretty neat. But how does it work?

What is LinkedIns Job Wrapping Tool?

LinkedIn’s job wrapping tool automatically crawls and lists job listings from employers’ recruitment software onto their pages: ATS and/or their websites. The wrapping tool also lets recruiters assign job slots and setup a whole lot of selection criteria for filtering the jobs that are posted on LinkedIn.

There are two types of job wrapping services on offer: job wrapping plus and job wrapping auto. Job wrapping plus allows employers to gain mileage on their job postings. Whereas, job wrapping auto is a simpler version of job wrapping plus. It automatically posts jobs uploaded on various channels. Only relevant and current openings crawled, the job wrapper keeps deleting antiquated postings and renews them.

This is the real deal. This is why other job boards need to up their ante when it comes to job wrapping.

Why should Job Boards Upgrade their Job Wrapping Systems?

Any job wrapping tool is employed to save time and energy to absolutely nullify human error. To match the high and mighty standards that LinkedIn has set, job wrapping services such as the ones provided by JobsPikr has become essential. These premium tools almost always match the right candidate to the right job to stay ahead in the cut-throat world of recruitment.

How can you beat LinkedIn at its own game? Figure out the loopholes and plug those gaps. For instance, LinkedIn’s job slots can get pretty cumbersome for a large organization. If the organization is fairly big, with multiple departments and sub-departments, employers will need to buy multiple job slots. Prioritizing each job in each section and sub-section in each department is a major drain of resources and time and energy. Hence, the opportunity for your job board spotted.

What We can Learn from LinkedIn

It took LinkedIn years to perfect the candidate matching algorithm, thanks to job wrapping. How your job board can build on that is primarily by integrating it with a premium service to at least match LinkedIn if not beat. It is really simple, either keep up with the times and see what the giants are up to or get lost in the cacophony of the world wide web. With more than 20 million open jobs on the largest recruiting platform. It is no surprise to find out that 87% of recruiters regularly use LinkedIn. In order to succeed, you have to see what the competitor is doing. And do it better.