BẢN CHÉP ÂM THANH 🔗
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Welcome to technically people, a community
conversation by and for workplace futurists brought to
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you by the tech recruitment platform built
in. The podcast features insights from leaders,
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thinkers and doers on the vanguard building
human centered workplaces of the future.
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Along the way, you'll hear concepts
that will stop you in your tracks,
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concepts that inspire you to ask yourself, what's the most future forward way to
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approach my people leadership? We all
know the future of work isn't waiting around,
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so let's get on with the show. Hello, welcome to technically people.
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I'm tiffany Myers. I'll be hosting
the episode today. have an exciting,
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interesting, fascinating conversation with Brett Wells. He is director of people analytics
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at perceptics. He has spent his
career using people analytics to help organizations like
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the navy, seals and Esta a
ladder. So pretty polar opposite. Companies
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attract, select and retain top talent. So he's going to explain what is
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really possible when companies leverage people analytics. He said something the other day,
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which is that he can predict who's
going to leave a company and the next
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twelve months with an eighty five percent
accuracy rate. That is pretty impressive.
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So Hi Brett, thank you for
being here. Thanks, Tiffany, it's
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my pleasure. Thanks for having me. Yeah, so let's get to it.
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I wonder if you can give the
sense of what the practice of people
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anatlets can do, and obviously also
have perceptics does it? Yeah, so
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to level set on what is people
analytics, it's really, in my mind,
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the practice of gathering and connecting the
dots between your people data and then
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a whole host of organizational data,
and the point of it all is to
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make smarter business decisions. People analytics
is often pointed at a variety of issues
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such as how do we attract,
select retain talent, how do we improve
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sales or productivity, or even things
like workforce planning and creating a diverse inclusive
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environment. So all these challenges that
were faced within organizations, people analytics can
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shed some science and some light to
inform leaders. So I really liked this
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idea you brought up, which is
that people analytics is actually not anything new.
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Now it's just, you know,
the what's the phrase, new wine,
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old wine, skins. So it's
been around for many, many years.
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We've been studying people at work in
industrial organizational psychology for fifty years plus
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and making strides in terms of scientifically
improving the work lives of people, and
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we do that because a cascades over
to the rest of our lives as well.
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But we've never been at critical juncture
where we have so much data,
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maybe too much data, to make
sense of. Big Data is all around
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us now. It's about finding the
signal among all that noise of what to
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focus on, because often that data
is messy and it's difficult to interpret,
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and a strong people analytics team can
really cut through all that noise and help
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leaders make the soundest decisions with what
they have at hand. It perceptics.
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We design, collect the analyze a
host of employee listening and sentiment data.
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So the typical annual census survey that
organizations do, and but also pulse surveys,
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life cycle surveys to get a sense
of what's happening at on boarding and
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exit as people transition throughout their career, what happens when they get promoted or
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do a job transfer? Three hundred
and sixty reviews. So we help connect
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those dots for our customers and then
the extra roll we also provide is a
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data aggregator, and that's where we'll
say, let us see all of your
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terminations and all of your hires for
the last few years. Let's put that
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into the analytics engine and see what
insights can be garnered from looking at who
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stayed and been successful versus those who
have left it. It's still hurting the
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organization. Fantastic, so let's break
down one of those factors. I know
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you can uncover predictive criteria for whether
candidate will be successful or not, and
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I'd love to hear about that.
Yeah, there's a lot of success stories
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over the course of my career of
design assessments all targeted at trying to identify
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successful people. We're going to make
a huge in
Load full transcript
aestment and someone think of
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that number one draft pick in the
NFL. You're going to lay out tens
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of millions of dollars. You want
to make sure you're making the right decision.
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Or even if it's the counter at
Bobby Brown, you want to make
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sure you're hiring the right beauty advisor
that's going to give the very best customer
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service. So we take a scientific
approach within the field and first is really
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understanding what is success look like in
the role and before working with us a
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ladder, I had no clue.
But applying scientific principles in terms of what's
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the knowledge, skills abilities that define
success? When people have failed in the
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roll, what are the reasons why? And you start coming out with hypotheses
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like, oh, they need to
have really high customer centricity and they need
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to be able to problem solve and
they need to be able to have a
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team work and you come out with
these characteristics and traits that would be ideal
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for successful performance. And then you
go out, there's already hundreds of people
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in these roles, some are more
successful than others, and you ask them
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to go through a battery of psychological
assessments and you test those hypotheses. So
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is it true that the very best
beauty advisors, who are selling more than
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anyone else, have the highest customer
centricity? Do they have the highest teamwork?
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And out of all that noise you
distill down what are those key factors
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that recipe for success, then you
can apply to every higher going forward and
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make smarter decisions. Yeah, so
you know the way that you describe it.
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And then more boil down senses that
you're looking at data of all the
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candidates of are the last several years, let's say, so data and aggregate
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over time who were hired and turned
out to be successful, comparing that to
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those who were unsuccessful and using that
to understand those criteria. Yeah, and
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all likelihood you're already using certain criteria
to make selection decisions and you can see
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under the people analytics microscope which of
those factors are more indicative of success.
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So, as an example, one
criteria that often has undue amount of weight
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in the selection equation as years of
experience, and typically that's a very poor
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predictor of performance. So organizations are
saying you need five to ten years experience
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and when you boil it down and
start looking at the data, it predicts
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pretty low especially is compared to other
criteria that would allow a much wider funnel
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of applicants to create a more diverse
applicant pool, like all these other pluses
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that go along with it. So
you know, my follow up is,
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what's a criteria that's super predictive?
Yeah, so if we look at the
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last, call it fifty years of
research and industrial organizational psychology, we look
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at cognitive ability, personality, interests
to knowledge skills, like all these different
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aspects, years of experience, age, education level, mean, the list
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goes on and on, and one
that routinely comes to the top is cognitive
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ability. It's probably the most predictive
across all those criteria, but it also
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comes with a little heartburn as a
result, because it creates something called adverse
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impact. So certain groups of individuals
tend to score lower on average than other
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groups, not because there's any inherent
differences, but just because of disadvantages in
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terms of equity standpoint. So we
tend to supplement cognitibility with other measures that
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are also predictive but don't come with
those biases like personality. So those in
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combination are very predictive and play well
off of each other, regardless of the
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role. And you know what,
maybe I skimmed over a little bit too
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quickly on this idea that years of
experience is less accurate predictor tell me your
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sense of why that is cheese.
Well, I think you're taking someone and
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there's transferable skills and knowledge, but
you're plucking someone out of this experience and
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putting them into entirely different experience,
even if it's the same industry, it's
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two different cultures, it's two different
groups of people you're working with. So
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just because I have ten years of
finance and experience in this one company where
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it's incredibly hierarchical, it
doesn't mean
that it's going to make me a great
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leader in an organization that's more collaborative
and people centered. So I think in
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large part it's because organizations are quite
varied. What I would say is too
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exactly identical candidates on the cognibility,
personality and like you go down the list
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of things, years of experience might
give you a little incremental bump. So
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instead of being, you know,
a success rate to fifty percent, maybe
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a bumps you up to fifty one
percent by going with the candidate with more
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experience. But by no means would
that be the first knockout that I ever
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look at when making decisions. Okay, so that reminded me of the Russian
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Roulette analogy you gave with and it
was specifically in regard to hiring. Let's
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hear it. Yeah, so I
think how most organizations approach selection is they're
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using old ways of thinking. So
let's look at years of experience in the
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school they went to and their grades
and all these things that can be scraped
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pretty readily from a resume. And
imagine you're that hiring manager. In essence,
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you're spinning that roulette wheel hoping that
you land on read and you get
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about just under fifty percent success rate
because you have the to the zero,
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the Green Zero and the Green Double
Zero. And with leveraging people analytics,
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imagine taking all the black spaces on
that roulette wheel and now shading about half
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of those two read that's really the
power of leveraging people analytics when making selection
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decisions and using science, because you're
all of a sudden focusing on for that
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role. Here's what success looks like. Here's the recipe, are Algorithm for
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success. It's these types of personality
traits, it's these types of interests and
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this type of background and skills.
And now, when you're spinning that roulette
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wheel, by no means is it
ever a hundred percent successful, because it's
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called science. It's not perfect,
but it's far better than just going at
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it with that gut reaction like,
Geez, I'm the hiring manager. I
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like this person because they're from Chicago
to and they grew up rooting for the
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bears and wow, they're just like
me and I love this candidate. As
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opposed to focusing on the things that
really matter, there's too many biases that
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come into play when using the old
way of thinking. Let's science do it
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for you and then have a final
reaction as a hiring manager of all the
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candidates that meet that criteria so you're
not making a bad decision. Then you
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can start going with more of that
gut feel and reaction and how that synergy
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is between the hiring manager in the
candidate. I know that they're maybe kind
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of myth floating around out there that
people think people analytics is science, it's
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going to be one hundred percent accurate
all the time and absolutely everything, and
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I think it's important to know that. It's going to make you more likely
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to win a Russian roulap, but
it's, as you said, not a
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hundred percent accurate. So take down
the house. Playing on the people analytics
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wheel, you would lose every time. Playing on the hiring manager with exactly
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exactly what it like, and with
people analytics you're spinning the wheel with a
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seventy percent success rate versus the typical
fifty percent success rate in terms of new
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hires. Yep, that's right.
So imagine, thinking your mind the best
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performer you know in a role and
you want more like him or her.
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You're spinning that wheel. You might
be lucky fifty percent of the time.
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So they turn out to be a
top performer and they stay long term in
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the organization. Now, coming to
the people analytics wheel, you're spinning it
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and you'll get that person about seventy
percent of the time. It's not perfect,
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but the cost of using the science
pals and comparison in terms of making
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the wrong selection decision. You think
of the miss of hiring the wrong CEO.
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It's estimated to be anywhere from ten
to twenty times their annual salary,
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and it's only more in terms of
like a fortune, five hundred, fortune
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one hundred organization. Even at an
hourly role in an organization, making a
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Misshire at that level still cost the
business tremendous amount of money, anywhere from
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twenty to fifty percent of their annual
salary. So I would rather spin on
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the seventy percent success and look for
opportunities to keep bumping that up. Yeah,
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me too. And Bright, you
have the distinction of being the first
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gambling conversation we've have on technically people. I was waiting for it and now
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we've got it. So okay.
So you've mentioned Dei. I would love
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to hear success story in terms of
how you've helped an organization get better.
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Adi. Yeah, if I think
of one healthcare organization that I helped,
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they had a big goal of how
do we attract, engage and retain their
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diverse talent, and in the beginning
that organization was very much soul focused on
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selection. So how can we put
better practice? This is in place,
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so we're not looking through the manager
Lens and more so looking through the Science
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Lens. So over the course of
a few years, their percentage of diverse
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hires increased by nearly forty percent,
which was outstanding. But over that three
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years span of time we got to
see who stayed and who left. And
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although they are bringing on diverse candidates
faster than they ever were before, they
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were also losing them at a much
faster rate than they ever were before.
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So when you look at their population, diversity really didn't change at all.
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So they focused on diversity as the
Noun as opposed to inclusion as a verb.
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And again looking at employee sentiment data. Connecting the DOTS will, we
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found was that diverse candidates were very
much attracted to the organization and sold on
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this idea of a diverse environment,
but when they got to their teams,
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their respective teams, they didn't see
a leader like them. So the path
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of career growth and development was very
much lackluster and they went and found that
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elsewhere. So they're almost sold a
false vision or promise of what it was
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going to be like. So the
organization immediately pivoted and started to put a
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lot of intentionality around how do we
identify and promote diverse talent, and that
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all of a sudden had a cascading
effect in terms of retaining talent like those
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leaders, because those fresh people they're
bringing on with diverse backgrounds, they want
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to see someone like them that it's
possible to succeed and go up the career
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ladder. So that organization is done
wonders and their communities to reflect the demographics
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of those they serve in are really
pioneering the way in terms of creating more
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of an inclusive environment and showing that
path forward. A lot of conversations I've
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had in particular a podcast with Louren
virgus towns, and her really strong point
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of view is that you have to
start with culture. You can hire diverse
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workforce the day before you roll out
your DII manifesto, but if your culture
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is not, as you said,
doesn't live up to the verb of inclusion,
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then those people are going to mass
exit. Is the BOTOM line.
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They'll find it somewhere else. So
all right, nice segue mass exit.
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So we established this incredible accuracy right
in terms of when you know a person
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may leave a company. But the
pandemic, of course, has made retention
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tripli important. So tell me where
retention and the pandemic intersect, Jay's well,
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first, in terms of our baseline
models, in terms of predicting nutrition,
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like you said, they're eighty five
percent accurate. So we collect a
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whole host of sentiment data from employees
and we get a really good sense of
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which way the winds blowing for them
in terms of are they committed to the
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organization, do they have this intrinsic
joy at work? Do they intend to
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stay for the next twelve months or
not? And those that do not are
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three hundred percent more likely to leave
the organization in the next twelve months and
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we're able to predict with that eighty
five percent accuracy if someone stays or leaves.
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Just being able to predict if they're
going to stay or leave. That's
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that's a nice parlor trick or parlor
game, being on the gambling hat,
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but the double click into that is
also being able to just to tell leaders
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why. What's the reason? Why? What are the barriers that are getting
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in the way of People's success at
work? Is it the relationship with the
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manager? Is it having more work
life flexibility? Yeah, and when someone
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is hired fully remotely, there is
not that honeymoon phase that you would have
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that if you're in person and getting
the chance to drink the Koolaid and everybody
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seems so nice. You're spot on
and again points to the importance of people
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analytics and why you should always be
looking at data. So if you would
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have asked me and my biased mind, I would bet my bottom dollar on
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always seeing this notion of a honeymoon
effect and the honeymoon effect and what we
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see in every survey administration are those
employees that are newly brought onto the organization
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are the most engaged they're seeing their
work lives through the rose colored glasses or
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the course of the pandemic, though, those that were hired and onboarded during
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the pandemic, they are acting,
behaving, feeling more are like they've been
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in the role for three to five
years, and that's the critical juncture where
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people tend to decide are they staying
or leaving? They're also the most at
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risk for leaving right now, those
that pandemic cohort is we've as we've termed
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it. And again, that's why
we need to keep looking at data,
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because these things change with with so
much pressure of an environment like a pandemic,
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on boarding look completely different. Even
if you were on boarding in person,
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it's not like you're sitting in the
break room talking to friends, sharing
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a meal. You're still very much
in your isolation during the pandemic. So
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it looked completely different and employees didn't
get that emotional attachment to their organization.
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They didn't fall in love with them
and have that honeymoon period. Yeah,
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so so many people in the HR
space are really rethinking how to on board
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and you know, in a way
this is a good framework. You know,
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create some onboarding that replicates the honeymoon
experience to whatever extent you can,
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and I would say one practical piece
of advice. The single most important relationship
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that we're finding right now that mitigates
many of the effects that we're seeing is
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the direct relationship with a manager,
and we're seeing overall that's pretty strong and
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when it is the strongest, that's
where we're getting that emotional attachment to the
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organization. But where employees are missing
out as on those fringe connection. So
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they're really close with their manager,
may be close with their direct a team,
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but when looking Cross Department or Cross
area where you'd be bumping into people
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in the office, they're really missing
those connections right now. So they don't
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have multiple connections, they're almost on
this little island that's pretty isolated. Yeah,
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the weak ties that they're most Rada, which is a misnomer, of
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course, because those ties are not
weak. They are very important for innovation
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as and, as you're saying,
for retention in this case. Okay,
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so let's see. Let us talk
about this concept. Office is m.
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So it is the idea that as
we move into the hybrid world, leaders
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could view remote employees as less committed. As you know, maybe less productive
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right because you can't see them,
can't see them other than you know are
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they logged on, and there are
all sorts of inclusion implications around that.
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We know, though, that the
majority of tech workers want flexibility. They
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want to choose how and where and
when they work, and the smartest companies
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are giving people that flexibility. However, if office is m becomes a reality
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and workers who do have that flexibility
face some form of discrimination, tell us
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what we can do about it.
Well, let me paint the horrible picture
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first, and I think we're going
to take decades step, words back in
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terms of inclusion in the workplace,
and the reason why is when we look
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at right now, the delicate balancing
active home work lives, what we're finding
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is that working mothers in particular want
to remain working remotely more so than their
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male counterparts, and for those that
do want to go into the office,
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they want to go in about one
fewer day per week on average. So
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the post pandemic world, where we
have this flexibility, where people can come
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and go as they want, you're
going to see a far more male dominant
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work environment. Now you throw into
the mix this notion of office is M
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we're just not leaders, but many
of us harbor these feelings. It's pervasive
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that those that go to the office
will have better relationships with their manager,
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are going to be the first ones
to be thought about for developmental or promotion
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opportunities. All of a sudden you
start seeing the glass ceiling that we've been
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trying to chip away at for decades
arise again and, if anything, turned
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into a concrete ceiling, because the
only ones that around the office, by
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large part, are heavily mail dominated. So I think that's the the picture
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that could go, that could get
painted in terms of why this is such
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a critical issue to focus on right
now and get ahead of it before it's
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too late. Yeah, and I
think compounding that, I would add that
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there's estimates that as many as two
million women could just split, could leave
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the workforce. We know they're downshifting
their hours or leaving their job at much
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higher rates and also considering career changes, and it seems like it could be
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pretty pretty dire. So tell me
how to fight this. Are there any
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any areas to look at? You
know, from a people analytics perspective,
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I would consider that group. So, whether you're in the office or you're
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working remotely or hybrid. I would
classify those is almost like a dei category.
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So for the analytics team you should
be focusing on making sure that there's
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equity among those groups. So what
do we look like in terms of pay
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for performance? What do we look
like at promotion rates? What do we
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look at in terms of retention?
So you want to make sure that you're
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creating a great experience for employees,
regardless if they're going into the office every
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day or they're working remotely every day, and make sure that those outcomes are
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very equivalent to the best you can
and have some intentionality around that and dispelling
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many myths. So the myths of
the people are in this productive with all
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of the digital
exhaust that we give
off in terms of workers for many roles,
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especially the roles that can be done
remotely, you can back up with
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some pretty concrete evidence from a people
analytics team perspective in terms of that productivity,
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that they're just as productive, if
not more productive. So I think
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you can dispel those myths. Start
Looking at it through a Dan Islands and
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the most progressive organizations are really thinking
about the future of work. They're creating
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roles where they're just responsible for this
hybrid so like directors of remote experience or
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directors of our extended community, or
so they're really putting intentionality on. We
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know that how the work gets done
is maybe a little bit different and what
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each group wants is a little bit
different. Let's make sure we're meeting the
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unique needs, and that's where the
employee listening strategy can play a large part,
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where your sense of seeing those groups
task. What do they want to
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be? What do they need to
be successful? What's getting in the way
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of their success? What are they
worried about? And really catering to those
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individual groups is opposed to some blanket
umbrella. The blankets statements or the blanket
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approaches rarely work because you have segments
of groups that want different things and need
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different things to be successful. Yeah, so, Brett, I read your
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article in Forbes and you mention that
you should move away or just totally forget
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annual performance evaluations where the goal is, to quote unquote, meat expectations.
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I thought that was interesting. So
we're moving away from meeting expectations. What,
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then, instead should we do?
And the rationale behind that is whenever
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I get performance data as a consultant, I start getting excited because wow,
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we have something tangible to predict.
This is what the organization cares about.
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And when we get the data finally
in hand, you find out that,
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you know, eighty percent of employees
meet expectations, very few are far exceeding
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and very few are struggling. So
it's meaningless for trying to predict anything.
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So instead it's thinking
about what does
success redefining what success looks like in the
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role. How can we measure it
more accurately with concrete things? Is opposed
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to abstract manager ratings, which are
riddled with air and are not the most
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reliable indicators of performance. So rethinking
performance in the hybrid world is going to
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be very critical and I think will
dispel some of the officism. What many
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of us think productivity means is we
go to an office and where they are
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nine and twenty five, or we
leave after the boss leaves. That's impossible
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to do in the remote world and
especially where employees are asking for flexibility.
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So instead let's redefine what productivity means
and measure it that way and give employees
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what their expectations are to meet,
regardless of where you're working all right,
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so, Brett, how about we
move into our two minute takeaway and actually,
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you know what I want to share
my key takeaway from this conversation,
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which is, you know, I
was always fascinated by your concept of officism.
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I just think it's brilliant. But
you know, when you paint to
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that picture and said that, you
be taking a giant step backwards in terms
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of Dei and inclusivity and creating a
sense of belonging and allowing people to be
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their whole salves at work. You
know, that really struck me as something
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people are going to need to think
about and are going to need to fight
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if they care in fact, about
creating a great workplace of the future.
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So, anyway, that my key
takeaway. So tell me a couple of
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things you want us to understand,
and I think your spot on, and
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the I'll just extend your great insight
and many organizations are probably feeling right now.
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We're doing exactly what our employees asked
for. They want to work remotely
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and we have plans of doing that. But now they need to think beyond
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just meeting that need and how do
we create equitable experiences for things that employee
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still value and want like career growth
opportunities and promotion, and how do we
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make sure that office is M isn't
getting in the way, regardless of where
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people
are working? Second one is
just to remind people that they're sitting probably
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on a treasure trove of data and
if they don't have a people analytics team
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today, there's still opportunities to go
in and get what I think is a
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00:27:19.410 --> 00:27:25.769
quick win. Start looking at your
turnover data, start looking at your selection
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decisions, start mining and finding those
nuggets, because I promise you they are
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there in terms of making smarter decisions. And then our final takeaway would be
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with so much change that we're experiencing
and pretty severe gravity into the changes,
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with all of that going on,
there's no better time to listen to employees,
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stop having the mindset of we think
we know what our employees want and
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instead just ask them the questions.
But then asking isn't enough. What we
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find in our research is that the
organizations that are performing the best frequently ask
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their employees for feedback but, most
importantly, they act on it quickly.
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So I can have a great conversation
with my spouse tonight and she could tell
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me that, you know, I'd
really appreciate it if you would put away
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the dishes more often, fold the
towels, you know, etc. Etc.
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Maybe this is fresh in my mind
exist what happened last night, but
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I need to act on that.
I can't just listen and say, Oh,
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good talk, honey. I need
to now today, act on it
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if I want to re engage her. And organizations need to do the same
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thing. It's no different in terms
of a relationship they're trying to create with
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each and in every individual employee.
So listen to your employees, but respond
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and help them connect the dots.
We value your input. That's why we
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asked. This is what you told
us and here's what we're doing about it.
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And not always will the organizational decision
align with every single employee and what
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they want, but showing that kind
of bread crumb trail for employees will incentivize
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them to share their voice more often. Maybe it wasn't the decision I wanted,
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but one time it will be.
So I'm going to give my voice
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because I don't want to be forgotten
about. So, okay, I think
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you've probably have a lot of people
analytics converts after hearing your conversation. So
377
00:29:18.529 --> 00:29:22.289
if they want to get in touch
with you and learn more about perceptics and
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00:29:22.650 --> 00:29:26.319
you, how do they reach you? Yeah, they can follow me on
379
00:29:26.920 --> 00:29:33.759
Linkedin. So just search Brett Wells
at perceptics and it's peer CEP ty x
380
00:29:33.279 --> 00:29:37.559
and you can see a lot of
our latest research, whether it's on office
381
00:29:37.720 --> 00:29:41.029
is mm or turn to work under
our resource library on our website. And
382
00:29:41.430 --> 00:29:45.869
we only got to skim the surface
on people analytics. Anyone that would love
383
00:29:45.390 --> 00:29:49.910
a deep dive conversation, here's some
case studies, success stories, or just
384
00:29:51.029 --> 00:29:53.380
want some help in terms of how
would we stand up people analytics when we're
385
00:29:53.420 --> 00:29:57.339
more in that infancy or adolescent stage? Reach out. I'm happy to do
386
00:29:57.380 --> 00:30:00.500
a one on one. Fantastic.
I think people will take you up on
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00:30:00.660 --> 00:30:06.299
that and my email. I'll just
throw out my email be wells at perceptixcom.
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00:30:06.460 --> 00:30:07.930
Awesome. Well, I can't thank
you enough. As you know,
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I really respect your work and your
insights as so happy to have this conversation
390
00:30:12.769 --> 00:30:18.369
and bring some of what you're up
to our listeners to thank you for being
391
00:30:18.450 --> 00:30:22.400
here. My pleasure, tiffany.
Thank you so for our listeners, you
392
00:30:22.519 --> 00:30:26.400
will not be surprised to hear me
encourage you to subscribe to technically people.
393
00:30:26.400 --> 00:30:30.680
All you need to do is visit
our site, technically peoplecom. I hope
394
00:30:30.720 --> 00:30:36.430
you enjoy the conversation and we will
talk to you next week. Whether you're
395
00:30:36.509 --> 00:30:41.349
looking to fill opportunities or find them, built in has you covered. If
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you're seeking to meet aggressive hiring goals, will help you attract sought after tech
397
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talent you might not otherwise reach,
and if you're on the market for a
398
00:30:49.940 --> 00:30:56.019
career change, visit our site to
explore exciting jobs with our customers and even
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with built in find talent, find
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