Mar 25 2019
“Predictive analytics for hiring uses modern data and
assessment science to project candidates’ fit and likelihood for success based
on positive behavioural patterns among current employees”
Ask any HR recruitment professional and they will tell you that hiring predictors
to get right. In fact, the majority of today’s organizations struggle to
accurately identify the
right person for the job. If it is already expensive to hire, the cost of bad
hire is even higher.
Today, 95% of organizations admit to making bad hires every year, according to a research
by Brandon Hall Group.
An analysis published in Psychological Bulletin
concluded that statistically, a typical employment interview leads to the
correct hire only 57% of the time. Despite so much being done to assess and
select the candidate, the odds of a right hire are only slightly better than the
flipping of a coin. But employers must still make hiring decisions.
Even when there are no hiring predictors that can guarantee 100% success, some
are definitely worth looking at. Examples of better hiring predictors includes
critical thinking and cognitive abilities, skills, conscientiousness (one of the
five personality traits of the Big Five personality theory) and past behavior
(real world examples to support their claims).
On the other hand, some of the worst predictors cited include first impressions
and school grades. To make sense out of the importance of job fit or performance
of hires, it is often not about the lack of skills or ability, but rather lack
of “fit with job” itself, the hiring manager or the teams the person has been
assigned to (as observed from our experience with our recruitment clients).
So, what can a hirer do to improve the odds and build a more effective and
reliable hiring process. How can technology and data help?
Google was one of the first companies to effectively utilize predictive analytics
in its hiring process, collecting all the possible measurable data and test
scores they could find. However, when they analysed actual job performance, they
found that school grades are one of the worst predictors for hiring (according
to Laszlo Bock, former SVP of People Operations). The tech giant has invested
significant resources in developing analytical models that support human
decision-making. In particular, one of the team’s most interesting findings is
that the company experienced little benefit to conducting more than four rounds
of interviews. Instead, they today make use of a hiring committee where hiring
decisions are made through a team consensus.
Predictive analytics for hiring uses modern data and assessment science to
project candidates’ fit and likelihood for success based on positive behavioural
patterns among current employees. Instead of traditional hiring, which relies on
interviews based mainly on intuition and gut feel, predictive hiring relies on
data points and smart algorithms to recommend best fit candidates to recruiters
and hiring managers.
But the truth is, it will always be difficult to predict fit and performance,
because humans are complex and humans interacting in human systems are even more
Hiring is hard, expensive, time-consuming, and inherently uncertain. And nobody
is very good at doing it alone, whether you are a Google manager, a start-up
founder, or a sports manager. When it comes to identifying the best talent,
groups’ decisions are better than individuals’, and having data is always better
than not having it at all.
Apart from identifying the best talent, it is equally important to have means to
access to the talent pool (both active and passive). Researchers have long shown
that referrals can surface better job candidates. Referred candidates are more
likely to get call backs, more likely to be hired, and more likely to stay at
the company. Hiring is like attending a blind date, and referral will be the
introduction. They give both sides a little bit more certainty and information
about fit. A study on
the value of hiring through referral concludes that referrals
work because they yield better fits, not because they yield smarter workers. Today referrals now account for 30-50% of all hires at
large companies, according to a recent paper. Most referrals
also come pre-vetted and cost less to find.
The biggest disruptor in recruitment today is experimentation with HRTech
solutions and services. Recruiting has also become a digital experience as
candidates expect convenience and mobile contact.
At IoTalents, we develop HR Tech ideas and solutions centred around
cognitive technologies such as artificial intelligence (AI), process automation,
natural language processing, and predictive algorithms. We create objective,
data-driven profiling tools, allowing clients to target and surface high-fit
candidates. We also optimise sourcing channels through our popular and effective
referral-based recruitment platform, Jobs007, to get better referrals for better
Sei Wee is the founder and CEO of IoTalents. As IoTalents’ chief driving force, he leads the company in its goals to become an innovator and game-changer in the HR recruitment sphere, all through the empowering use of technology. As a serial entrepreneur, Sei Wee has a proven track record of success when it comes to business launches, and has at his disposal a network of invaluable resources built up over the years. This is a estament to his high-energy performance-driven leadership and strong work ethics. He sees IoTalents making a difference through HR Tech, and is also immensely passionate on the trend that an exploding shared economy of global IT talents will reshape the workforce of the future.
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