As a region, the United Kingdom is reeling from the need to hire. Almost nine in 10 managers (89%) said their business currently has vacancies and more than half (55%) said finding new staff is harder now than before the pandemic hit, according to Bloomberg.  U.K. businesses have seen rising numbers of employee resignations during 2021, and many employers are preparing for more resignations as the hiring crunch continues in 2022.

Open Recruiter Positions: >12,000

This urgency for talent is exacerbated by a huge increase in the number of open positions for recruiters themselves. According to a new study by Textkernel, there was a significant rise in job listings for these roles both in corporate/direct hire positions as well as in staffing and recruitment agencies. 

At the close of the third quarter, there was a total of 12,100 recruiting positions open across the UK. Staffing and recruitment agencies saw the majority of those positions, with 6,500 posted jobs. Direct hires accounted for 5,600 recruiter openings.

These numbers represent enormous growth.

Where the Gaps Are

Most of the top industries across the UK have more openings for recruiters than they have applicants. The top five industries, however, that have the most positions open are:

The industries with fewer than 100 open recruiting positions were utilities, Pharmacy/Chemicals, Facility/Cleaning, Agriculture/Fishing, and Administration/Call Centers (where only 9 positions were listed). 

As of November 2021, 8% of open positions for recruiters came from these 10 organisations:

OrganisationCategoryJobs
NHSHealthcare / Welfare510 
Amazon.com, IncTrade / Retail112 
Womble Bond DickinsonBusiness services  96 
ButlinsTrade / Retail  47 
Voyage CareHealthcare / Welfare  47 
PwCBusiness services  44 
Devoteam S.AIT  38 
The Gap, Inc.Trade / Retail  38 
Citigroup Inc.Finance / Insurance  33 

Staffing and recruitment agencies’ top 10 companies had 22% of all the open recruiter positions. Those companies were as follows:

Regional Insights

By the numbers, the larger the employment base of a region, the stronger the need for recruiters. To illustrate: more than 1/4 of all recruiter jobs are found in the greater London area. Of the top 10 highest numbers, 9 regions were in England and just one in Scotland, although Edinburgh is number 11 and not far behind Glasgow. 

RegionDirect EmployersAgencies
Greater London3,4003,800
Manchester650900
West Midlands425725
City and Borough of Leeds300500
Hertfordshire250330
Surrey200280
Hampshire150270
Sheffield140240
Kent125240
Glasgow City160200

Transferrable Skills? 

With this kind of recruiter demand, now may be an opportune time for professionals in other roles to retrain and take on new positions in recruiting. To this end, an analysis of the skills that employers are searching for is in called for.

Textkernel’s analysis of both professional and soft skills included in job descriptions lead us to consider which careers may be a good jumping point. Our interpretation of the data indicates that people with experience in sales, communications, consulting, and customer service may be strong candidates. Consider the following skills that appear in each respective percentage of skill requirement:

Professional SkillDirect Employer/CorporateStaffing/Recruitment Agency
Stakeholder Management28%21%
Administrative Operations20%22%
Social Media23%18%
Sales16%17%
Candidate Experience22%12%
Customer Service14%13%
Human Resources14%11%
Coaching and Mentoring15%10%
Innovation16%9%
Consulting13%9%

Staffing firms and direct hires have more emphasis on different skill sets. For example, candidate experience is more important for direct employers than at staffing firms, as are skills such as innovation, coaching, and mentoring. Yet we’ve also seen that, across the board, data skills are in high demand although seldom included in job descriptions.

High Demand, Across the Board and the Pond

Demand for recruiters is happening at scale, across industries, and around the world, according to LinkedIn. In fact, job postings for recruiters on LinkedIn have steadily increased since the beginning of the year and have surpassed pre-pandemic levels – in the UK and the US. 

The post-pandemic growth percentages are quite different in the US. While direct employers have doubled the number of recruiter job listings over a three-year period in the States, the UK’s corporate recruiter listings are 3x those at pre-pandemic levels. Similarly, US agencies doubled the number of open recruiter positions, but in the UK the number topped 500%. 

The striking difference is in the sheer numbers of jobs available for recruiters:

USUK
Direct Hire/Corporate5,600206,000
Staffing/Recruiting Agencies6,500171,000

Location no longer is the driver. Whether the organization is in the US or the UK, in Maine or Massachusetts, Textkernel will be watching and reporting on trends in the industry.

Recruiting Recruiters: Looking Forward

There is currently no indication that the tight labor market will loosen up significantly in 2022. Certain economic pressures, such as from the expected rise in interest rates, may dampen some growth, yet job expansion will be offset by continued baby boomer retirement, upskilling needs, and global competition.

Staffing firms and direct employers alike will be using the restricted pool of applicants more intensively. Applicants that have applied for one specific job, for example, could also be matched on other open jobs at the same organization. Some large corporations and staffing agencies find that the numbers alone require greater process automation. 

Remote workers may help the savvy company fill its open positions. But equally important will be the need to focus more on retaining existing staff and identifying internal candidates more frequently. Additionally, greater attention will need to be paid to those who have been recruited, assuring their commitment with pre-onboarding and regular communication.

AI can help HR with all of these recruiting needs.  If your organization could use greater intelligence, whether it’s market insights or artificial intelligence, Textkernel is ready to help you see your success rates soar. Schedule a demo. We’d be pleased to share more details about our technology, how our Jobfeed tool helps you gather the real-time job market data, deliver great talent acquisition and management solutions.

The struggle to fill the talent pipeline has continued ever since the global pandemic struck!

As if that weren’t enough, we now are struggling to fill the roles of those team members that can help us recruit needed talent.

New data from Textkernel’s Jobfeed, the labor market data resource for HR and recruiters, shows that the United States is in dire need of people with these skill sets. In fact, as of Q3 in 2021, direct employers had a 123% increase in recruiter job listings over a three-year period; staffing or recruitment agencies saw a 131% increase in recruiter positions over the same period.

Staffing Agencies and Direct Employers Alike

As of November 2021, there were more than 206,000 corporate recruiter positions available. The greatest concentrations were in the professional, scientific, and technical services industries, with nearly 38,000 jobs listed, or 18% of the total. Not surprisingly, transportation and warehousing recruitment positions were next in line, with almost 21,000 openings, or 10% of the total number of listings.

The top three corporations with the most recruiter positions open were:

Staffing and recruitment agencies also illuminated the tight market by listing more than 171,000 recruiter roles as open as of November 2021. The three agencies with the greatest number of vacancies for recruiters were:

Stately Insights

By the numbers, it’s clear that the states with the greatest populations have the greatest number of openings. But these are also the states with the greatest concentration of workers in the top markets: professional/scientific/technical and transportation/warehousing.

Figure 3: Recruiter Demand Per State

California led the country with the most recruiter job listings, at 26,800. Of those, 16,630 were direct employers and 10,150 were for agency roles.  

Texas and New York were next in line, with 21,200 and 19,800 total recruiter openings respectively. 

Direct employers in Texas had 12,800 recruiter roles posted; New York had 12,700. Agencies in Texas listed 8430 talent agents and New York agencies listed 7100.

Skills in Demand

As recruiters know, keywords are key to identifying the perfect match for a role. It’s no wonder then that the word recruitment was used 55% of the time as skills required in the job description for direct employment roles. Human Resources, on the other hand, was used only 23% of the time, indicating that perhaps corporate hiring might be happy to poach from those with agency experience. 

A similar percentage was seen in the percentages of word choice by agencies, with recruitment used 52% of the time, and Human Resources used only 18% of the time. Certain skills, such as sales and customer service, lend themselves more to agency recruiters than to corporate recruiters.  

Strong communication skills remain the most sought-after traits for recruiters. The following top ten soft skills are required by those hiring:

Skill Direct EmployersAgency
Communication46%42%
Self-Motivation35%32%
Teamwork22%18%
Strategic Thinking23%15%
Hardworking/Dedicated21%16%
Networking18%17%
Coordinating19%15%
Attention to Detail18%15%
Leadership19%12%
Passion17%12%

As we compare skills required by recruiting firms versus direct employers, strategic thinking appears to be a skill that seems to be much more important for recruiters at direct employers. This differential is also seen in demand for leadership skills and organizational commitment. 

High Demand, Across the Board

Demand for recruiters is happening at scale, across industries, and around the world, according to LinkedIn. In fact, job postings for recruiters on LinkedIn have steadily increased since the beginning of the year and have surpassed pre-pandemic levels. 

Because the pace is fast and the demand is high, corporations and agencies alike are increasingly looking for experienced talent. There is little time to get those with less expertise up to speed, as there are so many jobs to be filled.LinkedIn states that before COVID only one-third of recruiters were coming from other recruiting roles; since COVID, that share has jumped to 59%. 

Whether the recruiter specializes in hospitality, where there were 5,456 recruiter positions open in the US, or in technology/professional services, where there were 37,828 recruiter roles to be filled, it’s obvious that recruiters are in the catbird seat just as candidates are in any field.

High Demand, Across the Ocean

Textkernel has also gathered and analyzed data about the increased need for recruiters in the UK. The increase in available positions, as a year-over-year percentage, was even greater than that of the US but disproportionately higher for agency recruiters. Corporate/direct hire saw a 309% increase in recruiter position postings between 2019 and 2021; Agency listings increased by 513% in the same period.

Location no longer is the driver. Whether the organization is in the US or the UK, in Massachusetts or Manchester, Textkernel will be watching and reporting on trends in the industry.

Recruiting Recruiters: Looking Forward

There is currently no indication that the tight labor market will loosen up significantly in 2022. Certain economic pressures, such as from the expected rise in interest rates, may dampen some growth, yet job expansion will be offset by continued baby boomer retirement, upskilling needs, and global competition.

Staffing firms and direct employers alike will be using the restricted pool of applicants more intensively. Applicants that have applied for one specific job, for example, could also be matched on other open jobs at the same organization. Some large corporations and staffing agencies find that the numbers alone require greater process automation. 

Remote workers may help the savvy company fill its open positions. But equally important will be the need to focus more on retaining existing staff and identifying internal candidates more frequently. Additionally, greater attention will need to be paid to those who have been recruited, assuring their commitment with pre-onboarding and regular communication.

By Karlijn Dinnissen, Research Engineer in Textkernel’s R&D team and Chao Li, Team Lead in Textkernel’s R&D team

If you are using Jobfeed for lead generation or labor market analytics, chances are that it matters to you which type of organizations are posting the jobs. Therefore, Jobfeed distinguishes between two advertiser types: direct employers and staffing/recruitment agencies

The challenge is that the advertiser type is typically implicit in a job description. Best case scenario is the advertiser literally mentions “we are a staffing agency”, but then there are still countless ways to phrase this same thing. Therefore, we need to infer the advertiser type ourselves.

We developed a new multi-step Deep Learning AI system that first classifies whether a job posting comes from a direct employer or staffing agency. Then it uses all posting-level classifications to infer whether an organization is a direct employer or agency. This approach results in much better accuracy but also consistency for the postings coming from the same organization. Read on to learn how we did it.

Our previous method

Because we knew how important it is to know the advertiser type for each company, we started building a knowledge base of organizations and their types since the very start of Jobfeed. In the beginning, this was all done manually by reading the job descriptions or researching the advertiser.

But the bigger Jobfeed grew, the more new organizations we found. Therefore, maintaining the knowledge manually simply became unsustainable. Straightforward logic was added to get automatic ‘staffing agency’ signals from job postings, of which the most effective was pattern matching: staffing agencies typically use very similar ways to describe themselves and the job they are posting.

For example, these type of phrases may look very familiar to you:

And if you see these organization names, what do you think their advertiser type is?

To our knowledge, our competition uses a similar approach. The quality achieved is good but for such an important field, good is not enough. Every mis-tagged advertiser can be a major annoyance for users. We needed a better, more scalable solution to this problem.

Deep Learning for text classification

Textkernel has nearly 20 years of experience of applying the state-of-the-art in Machine Learning to the Recruitment domain. Therefore it made sense to also apply our expertise to classify advertisers automatically.

As a job always comes from one of the two advertiser types, this means we are dealing with a binary text classification task, which is a common task in the Natural Language Processing (NLP) research field. It lends itself especially well for applying a Deep Learning classification model. 

Deep Learning is an advanced technique that can automatically discover patterns from large amounts of data. We have used it successfully in our Extract parsing models since 2017 (more details on the approach here) and it has resulted in great performance improvements. It was time to apply it to a new problem. In our case, this means that instead of thinking of good patterns that indicate a certain type of advertiser ourselves, we can let a Deep Learning classifier do the job for us. This model is very likely to find a lot more useful patterns, even patterns that a human would never think of.

In order to come up with all those patterns, the model needs to see as many examples of job texts from both advertiser types as possible (supervised learning). Normally this can be a big bottleneck: every text you use to train the model needs to have a label (“staffing agency” or “direct employer”), and you can imagine that manually annotating 100,000 job postings this way would take a long time. But in our case we had our big knowledge base and historical data: we could use all Jobfeed jobs from every organization we had categorised throughout the years as training data. No need to annotate anything!

A typical Machine Learning model training process (blue) and prediction process (green)

We started by training a CNN (Convolutional Neural Network) classifier using English job postings from multiple countries. To make sure the new system is future proof, we evaluated its quality by manually checking the classifier’s output on a sample of jobs from organizations that did not yet exist in our knowledge base. Compared with the old system, our new method found significantly more staffing agencies compared to the old rule-based system. This means that we are able to identify many new staffing agency postings within the long tail of organizations for which we had no prior manual classification.

This convinced us that Deep Learning was the correct path towards solving this problem, so we invested more time into optimizing our training process, collecting more data from more organizations and Jobfeed countries, optimizing the model’s hyperparameters, and finally we also trained models for all other Jobfeed languages (Dutch, German, French, Italian and Spanish).

Ensuring consistency: a second Deep Learning model

Once we enabled our new classifier in Jobfeed, there was already a big increase in advertiser type quality throughout all countries. There was, however, one caveat: not necessarily all job postings from an organization contain the same type of signals. Therefore there is a chance that certain organizations will have 90-95% of their jobs classified as one of the advertised types and 5-10% as the other.

We wanted to make sure all jobs from an organization would have the same advertiser type, to keep our data consistent. The most logical solution was to use the posting-level classifications to infer new knowledge on organization level. 

We created a process that regularly aggregates the job postings from one organization, and uses the individual advertiser type classifications to infer whether the organization is a direct employer or staffing agency. If the final prediction is certain enough, we can even update our knowledge base automatically! The threshold we use for ‘certain enough’ can be different per language model and therefore country, which we kept in mind while designing the process.

A naive approach was to simply add up counts per organization and take the most frequent advertiser type (e.g. 20 staffing agency postings & 5 direct employer postings ⇒ staffing agency). However, this did not give us the accuracy and yield that we needed. Therefore, we created another Deep Learning model that makes the final decision based on the output from the first one.

Its input consists of statistical features derived from predictions on all postings from one organization. In addition, we also used the organization name and the website on which the job was posted. An added benefit of using mostly statistical features was that we could train a language-agnostic model which can be applied to any organization’s posting-level output, regardless of country or language.

We again trained the model in a supervised way (using labeled training data) and evaluated it on data from unknown organizations. The results showed us that our classifier automatically identifies with high confidence between 55% and 85% of the new staffing agencies (depending on the country). Since our system runs at regular intervals to detect new staffing agencies, we noticed that its performance and confidence increases as new postings arrive from the yet unclassified staffing agencies. The more data our system sees, the better it gets. 

As a result, since enabling the classifier we have seen a 20-50% increase in staffing agencies in Jobfeed.

While initially we focused on identifying staffing agencies, we found that we could kill two birds with one stone. Not only could we use very high confidence staffing agency predictions to automatically identify agencies, but we could use very high confidence predictions to identify the opposite category: the direct employers. Therefore we added automatic direct employer detection as well, further increasing our Jobfeed data quality and consistency.

Conclusions

This solution has already allowed us to identify with high confidence the advertiser type of over 55,000 new organizations across all countries. Since our process is organized as an iterative self-feeding loop, many more are being added continuously and automatically.

We are very excited that we have solved a major customer pain point by applying our experience in AI and leveraging our own data and knowledge. It also opens up new possibilities to improve other aspects of the data in Jobfeed. Our continued investment in the Jobfeed data will ensure you keep saving time and stay ahead of the competition.

Amsterdam, 30 November 2021 – Textkernel, backed by strategic software investor Main Capital Partners (“Main”), has further strengthened its position as a global leader of search and match technology through the add-on acquisition of US-based AI company Sovren. 

Sovren, which delivers a similar product suite, predominantly within the North American market, is based in Texas, USA. The combination of both companies creates a market leading player in the AI-based search and match technology space. This seamlessly fits into Textkernel’s mission to connect people and jobs, better.

With this strategic step, Textkernel continues its exciting growth trajectory and strengthens its North American and APAC footprint. Both Textkernel and Sovren have achieved impressive growth in recent years, both inside and outside the USA. After joining forces with Sovren, Textkernel serves over 2,500 clients including some of the largest staffing companies worldwide, working from offices in The Netherlands, USA, France and Germany.

Connecting People and Jobs, Better

“With the acquisition of Sovren, Textkernel enters into a very exciting next phase of its 20 year anniversary as a truly global AI technology leader for talent acquisition and management. Since our inception as an R&D-focused company 20 years ago, Textkernel has aspired to help people and jobs connect better.”

With the acquisition of Sovren, Textkernel is one step closer to creating an AI platform which makes all talent and labour market data meaningful, actionable and globally accessible for everyone.

Gerard Mulder, CEO of Textkernel

Robert Ruff, CEO of Sovren, added: ‘I am thrilled to have found the best possible company to join forces with Sovren. This acquisition ensures continuity for our employees, our customers, and our products. I believe the Sovren customers can immediately benefit from the strong knowledge and functionality of Textkernel around classifications on skills, professions and semantic enrichments for better search and match results.’

Pieter van Bodegraven, Managing Partner Benelux at Main Capital and Chairman of the Supervisory Board of Textkernel, commented: ‘We expect to become more active in the United States in the short to medium term. This is a very exciting moment for Main as we see great potential for many of our European platforms to rollout operations in the USA through buy-and-build strategies.’

About us

Textkernel

Textkernel is the international leader in Artificial Intelligence (AI), Machine Learning and Semantic Technology for matching people and jobs. Textkernel enables thousands of recruitment and staffing agencies, employers, job boards, HR software vendors and outplacement and  redeployment agencies worldwide to work smarter and more effectively by creating efficiencies in the HR and recruitment process. Textkernel is headquartered in Amsterdam, with operations in Germany, France, UK and USA. Including Sovren, the group employs ca.140 people. In November 2021, Textkernel took first place in the Main Software 50 ranking, for most successful software companies in the Netherlands.

Main Capital Partners

Main Capital Partners is a leading software investor in the Benelux, DACH and the Nordics. Main has almost 20 years of experience in strengthening software companies and works closely together with management teams of its portfolio companies as a strategic partner, in order to realise sustainable growth and build excellent software groups. Main counts over 45 employees and has offices in The Hague, Stockholm, and Düsseldorf. In October 2021 Main has over 2.2 billion euros under management and invested in more than 120 software companies. These companies create jobs for approximately 4,000 employees.

Sovren

Sovren Inc. is a globally leading software developer of enterprise-grade resume/CV parsing and semantic matching / artificial intelligence matching (AIM) technology. Sovren serves a customer base that spans over 1,000 staffing and recruitment agencies, HR software vendors, job boards and large corporations. Sovren’s broad suite of intuitive, accurate and highly efficient recruitment solutions are used by customers across the globe in more than 70 countries. The company is led by its founder and President Robert Ruff and all employees operate out of the United States.

Curious about how new vaccine mandates are being expressed in the labor market?

We analyzed recent job postings to help answer your questions about new vaccine mandates on the US labor market.

Topline:

Finding employees to fill open positions has never been more difficult. Yet the need to assure the safety of workers and work environments is critical, with mandates being set for vaccination at the Federal level in the United States. Since the mandate set by the White House was temporarily halted, workplace are free to set their own conditions of employment when it comes to COVID-19 vaccinations. 

In fact, the Equal Employment Opportunity Commission (EEOC), which periodically updates its guidance for workplace vaccination questions, said that the federal anti-discrimination laws it enforces don’t prohibit employers from requiring all employees who physically enter the workplace to be vaccinated for COVID-19. 

In response to the changing nature of worksite vaccination requirements, Textkernel has undertaken research to shed light on the status of open positions. In this article, we take a close look at vaccination mandates across different industries, job categories, and states from September 1 until November 9, 2021.

What is vaccination mandates across different industries, job categories, and states?

When we look at all jobs in the US, across each sector and location, we clearly see a dramatic rise in full vaccination requirements.

Yet, there is a clear and somewhat surprising disparity between industries.

Certainly it isn’t a big surprise that jobs in the public sector have the highest vaccine mandate percentage, at 14.6% of all jobs requiring the shots. Education and health care are both above average, with figures that may seem low to some. Yet there is no clear explanation for the high percentage in agriculture and real estate.

However, it does come as a worrysome surprise that jobs in accommodation and food service are not requiring vaccination at significant rates, especially given that these are the employees with the most frequent public interaction.

The sciences, including life science, physics, social science, computer science and mathematics, consistently list required vaccinations over 10% of the time. Yet, like hospitality, the jobs in production, sales, and construction professions mandate vaccines only around 3% of the time.

How states differ

As we analyze the map, several points of interest come to light. Of each of the jobs listed for work in a particular state, three states lead the country: Rhode Island, Washington, and the District of Columbia, each with more than 10% of positions listed requiring full vaccination.

At the bottom of the list are Texas, Wyoming and Montana, each of which has only 4% of their job listings requiring COVID-19 vaccines.

Political Alignment?

Based on the results of the 2020 Presidential Election, states that were won by Democrats averaged 7.0% of all jobs requiring full vaccination. States that were secured by the Republican nominee listed only 5.0% of positions with the same requirement.

Companies

Zooming in on some of the big corporations in the US, the following demonstrates key disparities in vaccination requirements:

1. Three healthcare related companies topped the list with the percentage of jobs requiring vaccination pre-employment:

2. Amazon: 26%

3. At Wal-Mart: 3%

4. UPS: < 1%.

International comparison

Comparing the US data with other countries shows that Canada is leading the list, with the US close behind. In Europe, only in the United Kingdom there some jobs that require a vaccination, predominantly in health care roles.

It is important to note that European regulations do not allow employers to ask employees to be vaccinated again COVID-19. This is true even in the very jobs that one would desire vaccinations.

“We help with making our clients’ data analyzable, searchable and by creating smart recommendations in their existing workflows”.

Gerard Mulder, CEO, Textkernel

From predicting talent shortages to recruiting the right talent, AI can do it all if it’s done right.

That however is the critical part. According to Gartner analyst, Nick Heudecker, over 85 percent of data science projects fail. Add to that the existing issues faced by the recruiters, the likelihood of getting a stable HR decision science system is already grim.

The lifecycle of skill is shorter than it used to be, making it difficult for the recruiters to capture and maintain an up-to-date view of the talent landscape. In case they manage to do so, sourcing the right talent for a role is another issue. It is further imperative to have a view of the future of the dynamic labour market across industries in order to be ahead of the competition in the recruitment landscape. For that, they need to have access to the mammoth of information hidden in the current and historical data. Considering these dynamics, Amsterdam-based Textkernel has developed a stable AI solution that employs a vast database and generates accurate recommendations for recruiters.

“We help with making our clients’ data analyzable, searchable and by creating smart recommendations in their existing workflows”

Gerard Mulder, CEO, Textkernel

“We give information about the job market in real time based on the current and the historic representation of all the jobs in Western Europe, US, and Canada.” 

Next to that Textkernel delivers foundational technology for optimization, digitalization, and data science projects. It has been offering AI-enabled solutions to connect people and jobs from as early as 2001. Founded as a spin-off of three universities researching the application of machine learning to natural language processing, Textkernel uses machine learning and deep learning models to create taxonomies and ontologies to structure and enrich data. It classifies the customers’ data into taxonomies and ontologies to analyse and derive knowledge to understand the talent pool’s skills better. While an organization can use Textkernel’s AI tools to leverage talent already in their networks, close potential skill gaps, and anticipate workforce trends, a staffing agency can source and place top talent faster, engage with their database, and be the effective bridge between candidates and employers. 

A major advantage of employing Textkernel is that the private data of an organization is connected with the publicly available data about the labour market to mimic what’s actually happening in the world, making the information provided truly actionable. That’s why one of the global leaders in staffing is now deploying Textkernel’s solutions across geographies. With multiple offices in various locations, the client organization was facing a challenge in having a quick look at the talent pool to source and address the job requisitions in an effective manner. Textkernel employed their taxonomies to structure the data based on the historic demand that the organization received over time. “We helped them  see trends in that data and create particular profiles of people who had been sourced,” says Mulder. By predicting the demand of customers, the client was able to source ahead of the demand (just in time) the right amount of talent that they could place across organizations, and therefore, their placement ratios increased. In addition, Textkernel’s technology enabled them to compare every incoming candidate with all their jobs, make suggestions to them, and increase the ROI on their Recruitment spend . 

It was made possible because Textkernel’s technology has been able to gain a holistic view of the clients’ data as well as the entire recruitment landscape, taking the information boundaries out of the equation. Mulder believes that a labour market, without information boundaries, creates a more prosperous world for people and companies, and that’s what the team at Textkernel is aiming for.

Source: CIO article.

The spectrum of strategy to execution requires technology that will support the goals of the organization and will ensure that the right people are in place.  Today, implementing AI tech that will help you win the talent battle is a must-have.

In this webinar, Textkernel teamed up with UNLEASH to provide an overview of the current state of tech demand across Europe and offer concrete recommendations and best practices to remedy the challenges of high-tech talent demand.  Watch this on-demand webinar to hear from Textkernel leaders Stephan Menge, VP Sales EMEA, and Grant Telfer, Sales Director for UK and Ireland.