Are your clients confused by nonsensical or irrelevant job applicants? Does it seem like some candidates have not even read the job description? It probably means that they haven’t. This guide explains how to write job description text that works in the modern world.
The problem: your client is not receiving applications from the type of candidate that they hope to attract. Maybe the CVs they are receiving are all from a narrow demographic. Or worse – perhaps they are being inundated with resumes from candidates who appear to have not even read the job description.
You might look at this dilemma and think “excellent”.
After all, those bad applications will mean that your own, hand-picked candidates will shine all the more brightly. But what happens when your own job listings fall foul of the same problems? You put out a call for talent, but receive a tidal wave of applications that could be described – at best – as time-wasters.
The solution: write better job descriptions. Here is a short lesson that you, as a recruiter, can pass on to your clients and add real value to the hiring equation. It’s a simple one: learn how to write job description text that is suitable for today’s job market.
A true problem for the modern world: how artificial intelligence (AI) interprets human communication can be a head-scratcher. A simple turn of phrase or throwaway comment can send an AI off on a completely irrelevant mission. Above all else, employers must learn how to write job description text that does not confuse an AI reader.
Let’s choose an example that’s close to home. Jobfeed, our recruitment sales generation software, lists all available vacancies in the UK job market. A recruiter will enter their chosen field or sector into the search bar, and retrieve all of the available opportunities. Fantastic.
But what if you are recruiting into recruitment itself (rec2rec)? You’re might find a whole load of less-than-relevant jobs. Why? Because employers often use the keywords “hiring”, “recruiting”, or “recruitment” within the general language of their adverts.*
They may even use a specific email address such as “recruitment@business” for the post. But these are all errors in the modern world of automation.
It’s not intentional; they have no idea that they are setting themselves up for a string of false-positive matches. But it illustrates the attention to detail required in solving this problem. As the use of recruitment AI increases, so too will the severity of the false-positive problem.
So the first lesson is precision in language. Teach your clients to select their vocabulary carefully. Not only for fairness sake. But because a poorly chosen word may inadvertently open the floodgates to a deluge of bot candidates. And then, sorting the genuine applicants from the autofilled forms will be an additional – and unwelcome – workload.
*NOTE: In this example, a smart rec2rec consultant will pretty quickly build search rules in Jobfeed which exclude all of the false positives. Your agency shortlists will remain at a high standard. But what about your clients? As autofillers for forms and live job feeds become more accessible, this issue will become an increasing problem. Employers are facing a future where bot-like applicants become a worse problem - before it gets better.
Remove hiring Bias
Careless choice of language and vocabulary can have unexpected effects on humans, too. Language which contains too many gendered words can limit the spectrum of applicants to any given role. Just as we must learn to write for robots, we must also get better at writing for the whole world.
There is no secret that one of the biggest value offerings any recruiter can make is in helping clients to demonstrate an absence of bias in their hiring programme. Most HR departments are actively engaged in this issue. At the same time, few have the specialist tools required to make this part of the job easier. While you can introduce automation into your list-building and guarantee a bias-free range of candidates, your clients can help out by writing better job listings, too.
Some products like Paiger are already helping recruiters to remove bias from their language. these services will help you learn how to write job description text which is free from bias. It does this by returning job listings to you which contain too high a frequency of gender-biased words.
But it is important to remember that bias goes beyond gender. It covers ethnic, nationality, ability and age-based assumptions about the ideal candidate, too. When we are writing our job descriptions, are we paying close enough attention to ensuring they are inclusive – both in language and in tone?
Combine these elements of writing better job descriptions, and you cannot fail to improve the candidate and client experience.
While many of your existing client base will be aware of these difficulties, they may not be aware of the answers. Equally, they may not realise that their job descriptions are the reason why they are inundated with irrelevant applications, or a limited candidate demographic.
Perhaps, in your role as a recruiter, you can bring them the answers by way of consultation sessions on this subject?