Staffing 2.0 – Programmatic Matching 1/2

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This blog post is the first in a series of two. Read the second blog post here.

It probably already reached your attention but here at UnderstandLing we have been very active to make our self-invented term Programmatic Matching a thing. We are talking, of  course, about the staffing & recruitment industry with hints of flavour from assessment and HR.

With our client base being well-established in the staffing & recruitment industry, we created a couple of interesting propisitions so far already, most importantly we are known for our Employer Branding tool that we created together with Timing, receiving lots attention from peers in the industry.

Knowing this industry quite well, we distilled the essence of it –  bringing a candidate and a role together as best as we can. We quickly realized that in the current market, scarcity of candidates is a huge factor. The implications hereof is that competition for the best candidates is increasing, recruitment is becoming more important than placement and a mismatch in hard-skills is becoming less of a factor than a match in motivation and personality. Exactly this latter bit, we can work with!

Resuming with no resume

The CV or resume is under heavy critique lately. The way staffing & recruitment currently operates is by taking the role or vacancy as reference and just trying to find a candidate that has the skills and education mentioned on that vacancy matched as best as possible. In essence, it’s a big CTRL+F fest.

So what happens if you are very adept in Hadoop but just didn’t mention it because you haved moved on from it? You will be ignored. And what happens if you are a very clever person that didn’t have a formal IT education but you are a very capable programmer? You will be ignored.

We’ve seen multiple ocassions where our clients experimented with putting forward people that did not meet the background and education of the role at hand. Instead these candidates were educated and put the job at the same time; call it a traineeship. What turned out is that more often than not, these employees used different approaches to challenges faced and hence came up with far more creative solutions, arguably better ones.

To us, it is clear that we needed to move on from CV matching.

Matching 2.0

A happy employee is a good employee. Quantifying what constitutes an employee to being happy, is not possible from CV matching, often only looking at hard skills. The motivation an employee has and the fit with the company (s)he is working for as well as – more importantly so – the role being performed and direct peers is what constitutes to satisfaction. It even turns out that happier employees lead to a happier employer, and that employer, is your customer.

But how to quantify this fit? It turns out that a great deal of studies have been performed on the correlation between personality and job satisfaction (or other indicators of fit). Of course there are a lot of dimensions to consider, such as the industry, type of role, seniority, culture, etc. but all of them show that correlations exist one way or another.

So we know correlations between personality and job indicators exist, next question is how to extract personality in the least invasive way so as to bother the candidate and recruiter as little as possible. Of course you can hire an assessment agency and have every candidate screened thoroughly. This works. But this is also very costly. Nowadays you can do the same with a remote assessor having a Skype interview with your candidate for far less cost. Of course this makes it more interesting, but it still doesn’t scale.

We need something better.

We have used our experience in AI and profiling to come up with a unique way of extracting personality. It turns out that the way you write tells a big deal about yourself and your personality and we can use this to extract a personality profile automatically. The key here is that it doesn’t matter that much what you say (or write) but how you say it. This takes down an often-brought up objections against assessments where the candidate being assessed is gaming the assessor just because (s)he knows (s)he is being assessed.

Continue to read the second part of this blog post.

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