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Transferable skills in a disrupted job market: a data perspective

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Transferable skills in a disrupted job market: a data perspective

By Kasper Kok, Product Owner of Knowledge Resources and Juliette Conrath, Team Lead of Proserv – Southern Europe

The global outbreak of Covid-19 has severely shaken up the job market, both in terms of demand and supply. Millions of professionals in tourism, hospitality and other areas are currently unable to perform their jobs and the possibility of an economic downturn has made organizations cautious to spend and hire. The recent changes in consumer behavior induced by the crisis also have a flip side, however: jobs in delivery services and telecommunications are trending and there is obviously unprecedented demand for medical personnel and volunteers. 

A disbalanced market disruption calls for mobility: in a time where entire industries are paralyzed by governmental restrictions, many people are left with no choice but to deviate from their current career path. But where does someone go who has been working in the tourism, entertainment, or hospitality industry all their life? Wouldn’t any employer outside these industries reject them at first glance of their resume, based on a lack of relevant experience?

Here’s some good news: modern staffing and recruitment experts are increasingly interested in tools that can help them identify transferable skills. Job titles can vary from industry to industry and even from organization to organization, so organizations are focused on identifying skill sets and looking at how to transplant skills gained from one experience into the next.

When looking at jobs in terms of the competences that are required to execute them successfully, the opportunities for mobility inside and across professional domains are vast, and opportunities go beyond the obvious. In this article, we’ll provide a data-based perspective on transferable skills across domains. The analyses we present build on three assets that Textkernel has developed over the last two decades: a substantial collection of over 1 billion vacancies, high-quality document parsers that can transform these documents into structured data, and taxonomies of professions and skills according to which vacancies can be classified.

Crawlers, parsers and taxonomies: a recipe for reliable analytics

A first step toward reliable analysis of skills in the job market is access to a large set of job market data. Textkernel web crawlers spider the web for vacancies from a large collection of job boards and staffing portals, totalling tens of thousands of websites. These vacancies are then transformed into structured data using Textkernel’s parsers, which decompose the documents into individual bits of information: job descriptions, candidate requirements, benefits, skills, etc. The final step is to normalize this information. That is, to relate the words found in the texts to the units that are of interest to HR and TA professionals, such as skills and professions and education levels. Normalization, in other words, is about recognizing synonyms that refer to the same concept to ensure that the analysis will be robust to linguistic variation. 

Normalization happens based on Textkernel’s various taxonomies. Our Skills Taxonomy contains over 11,000 skills and recognizes over 135,000 synonyms in 6 languages. 

Normalization happens based on Textkernel’s various taxonomies. Our Skills Taxonomy contains over 11,000 skills and recognizes over 135,000 synonyms in 6 languages. 

In addition to the skill taxonomy, Textkernel maintains a profession taxonomy, which contains 4200 professions. The analyses presented below are based on the millions of jobs in the UK job market, which are normalized according to these taxonomies.

Case studies: the booking agent and the warehouse worker

To show how a large database of parsed and normalized documents can help identify potential professional transitions, we look at two case studies. 

Jane is a booking agent, responsible for booking hotels and other travel arrangements. But the office hasn’t seen any orders coming for many weeks now and will be forced to let go of most of their employees. 

Jake is responsible for receiving and processing incoming stock in a commercial warehouse, but the warehouse has seen a drastic reduction in business and he is not optimistic that his temporary contract will get extended.
The current crisis has made both Jane and Jake wonder about the next steps in their career. Different from a normal job-transition, the overall decline of travel and retail activities impedes job transition within the same role of even professional domain. But what are their chances in a different role or even another industry, and which of their competences can be transferred to another type of job?

Machine learning for job titles

Frequent versus job-specific skills
First, let’s explore the core competencies we can assume Jane and Jake to have. Based on our enriched vacancy database, we can answer a simple question: what skills are most often asked for in job postings for booking agents and warehouse workers?
Skills most frequently found in vacancy postings

Booking agent: Customer Service, Communication, Sales, Attention To Detail, Passionate, Administration, Team-working, Telephone Skills, Stress Management, Self Motivation

Warehouse assistant: Stock Control, Passionate, Packaging and Processing Duties, Unloading, Communication, Forklift Truck, Hardworking And Dedicated, Logistics Operations, Expediting, Team-working

Upon inspection of these skills, you’re likely to notice that some of them are rather generic. Skills like communication and passionate are very frequently required in these professions, but they are not specific to these professions. This ubiquitous demand makes them important skills to develop, but they are unlikely to be among the key reasons for a hire. Rather than looking at the frequency of skills in requisitions for different jobs, we therefore need a measure of how strongly skills are associated with certain jobs. Instead of raw frequencies, we’ll therefore use a metric which we will call job-skill association strength. It has a high value for skills that are in high demand for a given job but not for many other jobs. Going back to our case studies, the following lists show which skills are most strongly associated with booking agents and warehouse assistants.

Skills most strongly associated with the target professions

Booking agent: Reservations Systems, Property Management Systems, Upselling, Arithmetics, Telephone Skills, Hospitality, Hotel Reception Duties, Amadeus CRS, Front Office, Attentive Service

Warehouse assistant: Unloading, Forklift Trucks, Handling and Load Carrying, Expediting, Palletizing, Stock Control, Packaging and Processing Duties, Warehouse Management, Systems, Manual Handling, Logistics Operations

The next best job: skill-based job transitions

Now that we have a way to characterize jobs in terms of which skills are most strongly associated with them, we can measure the distance between jobs based on overlapping skills. Here are the top 10 closest jobs to those held by Jane and Jake.

Jobs closest to the target professions, based on overlapping skills

Booking agent: Head of Reservations, Front Office Manager, Receptionist, Hotel Receptionist, Travel Agent, Travel Consultant, Night Porter, Receptionist-Telephonist, Account Manager Telesales, Head of Reception

Warehouse assistant: Order Picker, Warehouse Administrator, Forklift Truck Driver, Delivery Driver, Stock Clerk, Reach Truck Driver, Truck Driver’s Assistant, Logistics Assistant, Warehouse Associate, Packer

Visualizing the overlap between professions

To get a better grip on the overlap between our protagonists’ profiles and the jobs listed here, we can plot the skill-job associations for two professions against each other. In the charts below, we take the union of the 10 skills that are most strongly associated with each profession and plot their association strengths on the two axes of the chart. The upper-right corner of these plots then represent the set of skills that are associated with both professions. The top-left and bottom-right show those that are associated more with one of the professions than with the other. To aid the interpretation, we plot ellipses around the relevant parts of the axes of the plots, simulating a Venn diagram. 
Let’s first have a look at a possible job transition for Jake. From the above list of closest jobs, we can see that many of the possible job options are warehouse-related and therefore perhaps not the most promising options. There’s one job in the list for which there surely won’t be a lack of demand in a time where many people are homebound: delivery driver. Let’s have a look at which skills a warehouse assistant and delivery driver have in common.

This chart suggests that both professions are associated with various skills related to logistics, loading and unloading, and packaging. Jake won’t need his knowledge of Warehouse Management Systems anymore after the job switch, but he will need to learn a few things about defensive driving and tail lift trucks. All in all, these analyses suggest that there’s not much in Jake’s way of transitioning to a role as a delivery driver. 

Next, let’s look at the options for Jane. Here too we see that a lot of the most similar professions are within the same domain, or at least have to do with travel in some way. Assuming that hiring in the travel industry will be low in the considerable future, let’s look at a travel-unrelated option: Account Manager Telesales. Here we see how the typical skills of a booking agent overlap with those found in requisitions for telesales roles.

According to this data, both booking agents and account managers in telesales are acquainted with call centers, sales techniques and call reception management. Some further training in b2b principles and sales concepts might be required for her to make a job switch, but there seems to be a solid common ground between the two positions. 

But maybe Jane doesn’t like the sales part of her work all that much? Maybe she aspires to a more radical career switch, and hopes that she can contribute to healthcare? Using Textkernel’s profession taxonomy we could restrict the search for job transitions to the domain of healthcare. Based on skill-overlap, our data suggests that the closest medical job to Jane’s current profile is that of Patient Care Coordinator. Let’s look at what these two professions have in common:

Some upskilling will certainly be required for Jane, but judging from this analysis, there are several competences and activities that intersect the professions of booking agent and patient care coordinator. The ones that are more unique to the latter, shown in the top-left section of the plot, can be seen as areas where Jane will need to look for targeted training if she is to take this job transition seriously. 

Skill analytics beyond the crisis

Analyses akin to the ones presented here can have implications to mobility questions of any kind, not just those that arise in times of a pandemic. Challenges regarding internal mobility, outplacement, targeted upskilling and strategic workforce planning all benefit from a clear understanding of which skills are shared between different types of jobs. For instance, one might employ skill-overlap analysis to answer questions like “what skills represent the difference between an account manager and a commercial director?” or “what training would help a Java developer become a data scientist?” The charts shown in the appendix can help to answer these questions.

Not having access to the tooling required for skill-oriented profiling, recruiters and employers are often fixated on role descriptions and assume that different roles within or across industries don’t have enough in common to consider out-of-the-box hires. A good understanding of transversal skills is pivotal to a more agile approach to mobility challenges.

Using the powerful combination of crawlers, parsers and taxonomies, new opportunities for job transitions or upskilling can be identified that are fruitful in any state of the job market.

Appendix: additional analyses

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