to remember if characteristics they are reading on
the resume were some they remember from other
candidates that worked out—or didn’t, and more.
“GET ME MORE CANDIDATES LIKE HER”
Sometimes a hiring manager will comment—“she
was a great hire. Get me more candidates like her.”
It’s so frustrating to not know what it was about
the prior successful candidate that made them
successful. You can guess. (Was it their experience,
where they went to school, their references? How do
you know, for sure, so you can consistently replicate
success and avoid failure?)
TODAY’S CANDIDATE PRE-SCREENING PROCESS IS...
You get the point; today’s candidate screening
process is a losing battle. It’s not scalable. It’s
not repeatable. The process can’t learn from past
successes and mistakes. In six seconds, or less,
current recruiters aren’t giving candidates a fair
chance. They’re juggling 3,500 other things.
Naysayers of using AI or predictive analytics in
the candidate screening process talk about how they
don’t want to be treated as a number, or how they
are afraid of being misunderstood.
They aren’t “seeing” you as a person when
they review your resume in six seconds. There is
nothing personal about today’s typical candidate
CANDIDATE PRE-SCREENING—ONE OF HR’S BEST
“PREDICTIVE ANALYTICS PROJECTS”
Candidate screening is a process better handled
by algorithms that can effortlessly, accurately,
respectfully and predictively screen thousands
or millions of candidates per day (or hour) for
business success. All a predictive algorithm
cares about is predicting success.
Algorithms are fair. They are reliable. They
learn from their mistakes and can tell you what it
was about top performing candidates that made
them top—so the algorithms can find more.
Algorithms give the same amount of time and
energy to each candidate. They are unbiased. They
don’t get tired after screening 3 thousand (or 3
Predictive screening algorithms are developed to
screen-in candidates with a high probability of successfully performing what you need (i.e. make their
sales revenue, answer a large number of call center
calls, or have a high customer service rating, or last
in the role at least 12 or 18 months, accurately balance their bank teller drawers… ).
They also screen-out candidates with a low
probability of performing what you need.
Once candidates with a high probability of
success are identified, the Corporate Recruiter
begins their normal interview process. No more
6-second scans of a resume.
MACHINE LEARNING HELPS THE
PREDICTIVE MODEL TO “GET SMARTER”
To complete the predictive process, we
recommend that every 3 months, the predictive
model’s recommendations should be compared
with how the new hires are actually performing
in their job three, six, 12, 18 months later. (For
example, your data scientists or vendor should
regularly ask for actual performance data and
report on it. If someone was predicted to last in
their role for at least 12 months, you will want to
know if the new hire left prior to 12 months or if
they are still employed).
The only reason to keep using a model is if
it performs better than your current hiring and
LOOKING FOR A GREAT 1ST PREDICTIVE
PROJECT IN HR?
Candidate pre-screening is a wonderful choice.
Easy. Elegant. Releases your corporate recruiters to
interview, schedule, check references etc. and other
activities better suited for a human.
Greta Roberts is the CEO & Co-founder of Talent Analytics,
Corp. She is the Program Chair of Predictive Analytics World
for Workforce and a Faculty member of the International
Institute for Analytics. Follow her on Twitter @gretaroberts.