The Not So Perfect Match

I’ve been watching Married at First Sight recently, and yes this admission may impact on my Klout score but hey, sharing is caring. It’s a reality TV show where couples are paired together by match makers using assessment and their people matching prowess. Some work, some don’t.
Apart from match making, what does this have to do with talent acquisition?
Over the last few weeks I have been checking out a few applicant tracking systems. Some of them have matching technology which allows you to upload a job description, which when applicants apply, rates their suitability against the job description. You end up with an applicant rating of some kind whether it be a star rating, percentage rating, or numerical rating.
Now isn’t that pretty clever? Makes the recruiters job that little bit easier? Perhaps not, and here’re a few reasons why.
[bctt tweet=”Relying solely on the ATS to be your matchmaker might not be such a clever idea says @stanrolfe” username=”ATCevent”]
In recent years the more progressive employers are turning to more competency, soft skill based job descriptions. A good move in my opinion. But think about that for a moment when it comes to matching the job description to an applicant’s resume. The vast majority of resumes are not written with competency frameworks and soft skills in mind. Well, not yet anyways. So how exactly is the technology matching resumes to newer competency based job descriptions? I’d welcome comments from software vendors to explain this for us match makers.
Will ratings lure lazy recruiters into a false sense of quality? We are after all now a ratings bonanza society. We rate everything we see and do and someone somewhere can gather data and rate you too. So here a recruiter may open a vacancy to see an unmanageable number of applicants. They rely on matching technology, which as I understand it is similar to parsing technology, only not so perfect. The recruiter quickly makes those lower ranked applicants unsuccessful without reviewing them, and proceeds with those highly ranked only to miss out on one of those purple squirrels. We all know this happens.
Is anyone out there then measuring the success in relation to quality of hire on that matching technology? Or are your five-star rated matches dropping out within twelve months? How much do you know about how the technology works? If parsing technology is accurate to about 80 percent of the document being parsed, what about matching technology?
If you are using matching technology, it pays to understand how it works. Don’t accept the rating as close to accurate, otherwise you might risk losing your next perfect match.
Are you using matching technology currently? How do you feel about it? Love to hear your experience.
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