Job Description Language Needs To Be Effective, Not Just Common

According to the research team at Datapeople, hiring teams have created a job description echo chamber by 'borrowing' language from other companies' job posts.

Hiring teams often mimic other hiring teams’ job description language. This practice, according to the research team at Datapeople, has created a job post echo chamber.

People learn from other people, and recruiters can certainly learn from other recruiters. But, over time, the common practice of ‘borrowing’ wording from other companies’ job posts has created a dynamic where certain language is now ubiquitous.

“Let’s say a recruiter is filling a position they’ve never filled before, and they have a rough outline of what they need, but that’s it,” says Datapeople spokesperson Charlie Smith. “They’re going to do what everyone else does: look at other companies’ job posts. The problem, however, is that other companies don’t necessarily know what they need to write either.”

It comes down to data. Without data backing up language choices, hiring teams have no idea what language works and what doesn’t. And neither do their competitors’ hiring teams, so copying them doesn’t help.

Effective job description language isn’t intuitive, according to Datapeople. It’s not something a recruiter can determine through ‘gut feeling,’ ‘experience,’ or ‘expertise.’ For one thing, writers of job posts have limited exposure to the sheer number of job posts published every year. There are millions of them, but recruiters only see a small, anecdotal sample in what they’ve read or written themselves.

Also, people read job descriptions differently. They don’t have the same life experiences and don’t interpret language in the same way. It’s impossible for a job post writer to understand how each and every job seeker out there will react to certain language. They need a large dataset for that.

Meanwhile, real-world reactions are often counterintuitive. Language that suggests an applicant should be ‘aggressive’ or ‘assertive,’ for example, can be a deal-breaker for job seekers who identify as female. One assumption is that women don’t usually associate those terms with themselves because they are traditionally associated with men. But, in reality, women can be just as aggressive or assertive as men can be. And, certainly, many women would consider themselves a perfect fit for a job requiring assertiveness.

Yet they still may not apply. Not because they don’t think of themselves as a ‘go-getter.’ But because job description language like that can signal a work environment where others may make assumptions based on gender identity.

In the end, common doesn’t equal effective, according to Datapeople. The fact that everyone is adding the same language doesn’t mean it’s working. (By the way, ineffective job posts result in fewer qualified candidates and longer time-to-fill.) Actually, the only way to know what works and what doesn’t is through language analytics. Or, more precisely, analytics that reveal effective language, not just common language.

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