전사 🔗
WEBVTT
1
00:00:00.560 --> 00:00:06.759
Welcome to technically people, a community
conversation by and for workplace futurists brought to
2
00:00:06.839 --> 00:00:11.509
you by the tech recruitment platform built
in. The podcast features insights from leaders,
3
00:00:11.550 --> 00:00:16.309
thinkers and doers on the vanguard building
human centered workplaces of the future.
4
00:00:16.309 --> 00:00:20.510
Along the way, you'll hear concepts
that will stop you in your tracks,
5
00:00:20.989 --> 00:00:25.780
concepts that inspire you to ask yourself, what's the most future forward way to
6
00:00:25.899 --> 00:00:30.500
approach my people leadership? We all
know the future of work isn't waiting around,
7
00:00:31.140 --> 00:00:35.619
so let's get on with the show. Hello, welcome to technically people.
8
00:00:35.659 --> 00:00:40.130
I'm tiffany Myers. I'll be hosting
the episode today. have an exciting,
9
00:00:40.490 --> 00:00:46.609
interesting, fascinating conversation with Brett Wells. He is director of people analytics
10
00:00:46.689 --> 00:00:53.000
at perceptics. He has spent his
career using people analytics to help organizations like
11
00:00:53.119 --> 00:00:57.719
the navy, seals and Esta a
ladder. So pretty polar opposite. Companies
12
00:00:57.840 --> 00:01:03.799
attract, select and retain top talent. So he's going to explain what is
13
00:01:03.079 --> 00:01:07.230
really possible when companies leverage people analytics. He said something the other day,
14
00:01:07.230 --> 00:01:11.310
which is that he can predict who's
going to leave a company and the next
15
00:01:11.349 --> 00:01:18.780
twelve months with an eighty five percent
accuracy rate. That is pretty impressive.
16
00:01:19.099 --> 00:01:22.579
So Hi Brett, thank you for
being here. Thanks, Tiffany, it's
17
00:01:22.620 --> 00:01:26.579
my pleasure. Thanks for having me. Yeah, so let's get to it.
18
00:01:26.180 --> 00:01:30.859
I wonder if you can give the
sense of what the practice of people
19
00:01:30.859 --> 00:01:34.489
anatlets can do, and obviously also
have perceptics does it? Yeah, so
20
00:01:34.849 --> 00:01:40.530
to level set on what is people
analytics, it's really, in my mind,
21
00:01:40.569 --> 00:01:46.250
the practice of gathering and connecting the
dots between your people data and then
22
00:01:46.290 --> 00:01:49.920
a whole host of organizational data,
and the point of it all is to
23
00:01:49.000 --> 00:01:57.000
make smarter business decisions. People analytics
is often pointed at a variety of issues
24
00:01:57.280 --> 00:02:00.750
such as how do we attract,
select retain talent, how do we improve
25
00:02:00.989 --> 00:02:07.469
sales or productivity, or even things
like workforce planning and creating a diverse inclusive
26
00:02:07.510 --> 00:02:13.110
environment. So all these challenges that
were faced within organizations, people analytics can
27
00:02:13.310 --> 00:02:17.099
shed some science and some light to
inform leaders. So I really liked this
28
00:02:17.219 --> 00:02:22.460
idea you brought up, which is
that people analytics is actually not anything new.
29
00:02:23.219 --> 00:02:27.379
Now it's just, you know,
the what's the phrase, new wine,
30
00:02:27.500 --> 00:02:30.810
old wine, skins. So it's
been around for many, many years.
31
00:02:31.050 --> 00:02:38.289
We've been studying people at work in
industrial organizational psychology for fifty years plus
32
00:02:38.569 --> 00:02:44.319
and making strides in terms of scientifically
improving the work lives of people, and
33
00:02:44.439 --> 00:02:47.000
we do that because a cascades over
to the rest of our lives as well.
34
00:02:47.199 --> 00:02:53.039
But we've never been at critical juncture
where we have so much data,
35
00:02:53.159 --> 00:02:57.159
maybe too much data, to make
sense of. Big Data is all around
36
00:02:57.199 --> 00:03:00.990
us now. It's about finding the
signal among all that noise of what to
37
00:03:00.030 --> 00:03:05.710
focus on, because often that data
is messy and it's difficult to interpret,
38
00:03:05.949 --> 00:03:09.789
and a strong people analytics team can
really cut through all that noise and help
39
00:03:09.949 --> 00:03:15.500
leaders make the soundest decisions with what
they have at hand. It perceptics.
40
00:03:15.659 --> 00:03:23.060
We design, collect the analyze a
host of employee listening and sentiment data.
41
00:03:23.419 --> 00:03:29.610
So the typical annual census survey that
organizations do, and but also pulse surveys,
42
00:03:29.849 --> 00:03:34.289
life cycle surveys to get a sense
of what's happening at on boarding and
43
00:03:34.490 --> 00:03:38.930
exit as people transition throughout their career, what happens when they get promoted or
44
00:03:38.969 --> 00:03:44.439
do a job transfer? Three hundred
and sixty reviews. So we help connect
45
00:03:44.520 --> 00:03:49.560
those dots for our customers and then
the extra roll we also provide is a
46
00:03:49.599 --> 00:03:53.159
data aggregator, and that's where we'll
say, let us see all of your
47
00:03:53.199 --> 00:03:55.830
terminations and all of your hires for
the last few years. Let's put that
48
00:03:57.030 --> 00:04:00.909
into the analytics engine and see what
insights can be garnered from looking at who
49
00:04:01.030 --> 00:04:05.069
stayed and been successful versus those who
have left it. It's still hurting the
50
00:04:05.110 --> 00:04:10.419
organization. Fantastic, so let's break
down one of those factors. I know
51
00:04:10.500 --> 00:04:15.979
you can uncover predictive criteria for whether
candidate will be successful or not, and
52
00:04:15.019 --> 00:04:18.300
I'd love to hear about that.
Yeah, there's a lot of success stories
53
00:04:18.420 --> 00:04:23.899
over the course of my career of
design assessments all targeted at trying to identify
54
00:04:24.569 --> 00:04:28.290
successful people. We're going to make
a huge in
Load full transcript
aestment and someone think of
55
00:04:28.370 --> 00:04:31.129
that number one draft pick in the
NFL. You're going to lay out tens
56
00:04:31.170 --> 00:04:34.009
of millions of dollars. You want
to make sure you're making the right decision.
57
00:04:34.009 --> 00:04:38.290
Or even if it's the counter at
Bobby Brown, you want to make
58
00:04:38.329 --> 00:04:42.519
sure you're hiring the right beauty advisor
that's going to give the very best customer
59
00:04:42.639 --> 00:04:46.519
service. So we take a scientific
approach within the field and first is really
60
00:04:46.560 --> 00:04:50.839
understanding what is success look like in
the role and before working with us a
61
00:04:50.879 --> 00:04:57.230
ladder, I had no clue.
But applying scientific principles in terms of what's
62
00:04:57.269 --> 00:05:01.350
the knowledge, skills abilities that define
success? When people have failed in the
63
00:05:01.470 --> 00:05:05.189
roll, what are the reasons why? And you start coming out with hypotheses
64
00:05:05.470 --> 00:05:10.500
like, oh, they need to
have really high customer centricity and they need
65
00:05:10.540 --> 00:05:12.899
to be able to problem solve and
they need to be able to have a
66
00:05:13.019 --> 00:05:16.819
team work and you come out with
these characteristics and traits that would be ideal
67
00:05:17.060 --> 00:05:21.889
for successful performance. And then you
go out, there's already hundreds of people
68
00:05:23.009 --> 00:05:26.370
in these roles, some are more
successful than others, and you ask them
69
00:05:26.410 --> 00:05:31.449
to go through a battery of psychological
assessments and you test those hypotheses. So
70
00:05:31.930 --> 00:05:36.240
is it true that the very best
beauty advisors, who are selling more than
71
00:05:36.279 --> 00:05:41.680
anyone else, have the highest customer
centricity? Do they have the highest teamwork?
72
00:05:41.839 --> 00:05:45.759
And out of all that noise you
distill down what are those key factors
73
00:05:45.920 --> 00:05:50.149
that recipe for success, then you
can apply to every higher going forward and
74
00:05:50.269 --> 00:05:55.949
make smarter decisions. Yeah, so
you know the way that you describe it.
75
00:05:56.069 --> 00:05:59.470
And then more boil down senses that
you're looking at data of all the
76
00:05:59.990 --> 00:06:02.750
candidates of are the last several years, let's say, so data and aggregate
77
00:06:02.790 --> 00:06:06.860
over time who were hired and turned
out to be successful, comparing that to
78
00:06:06.980 --> 00:06:13.860
those who were unsuccessful and using that
to understand those criteria. Yeah, and
79
00:06:14.019 --> 00:06:18.209
all likelihood you're already using certain criteria
to make selection decisions and you can see
80
00:06:18.769 --> 00:06:25.569
under the people analytics microscope which of
those factors are more indicative of success.
81
00:06:25.610 --> 00:06:30.170
So, as an example, one
criteria that often has undue amount of weight
82
00:06:30.569 --> 00:06:35.319
in the selection equation as years of
experience, and typically that's a very poor
83
00:06:35.439 --> 00:06:41.600
predictor of performance. So organizations are
saying you need five to ten years experience
84
00:06:41.800 --> 00:06:45.160
and when you boil it down and
start looking at the data, it predicts
85
00:06:45.240 --> 00:06:50.389
pretty low especially is compared to other
criteria that would allow a much wider funnel
86
00:06:50.430 --> 00:06:56.110
of applicants to create a more diverse
applicant pool, like all these other pluses
87
00:06:56.189 --> 00:06:59.509
that go along with it. So
you know, my follow up is,
88
00:06:59.709 --> 00:07:04.420
what's a criteria that's super predictive?
Yeah, so if we look at the
89
00:07:04.459 --> 00:07:10.939
last, call it fifty years of
research and industrial organizational psychology, we look
90
00:07:10.980 --> 00:07:15.209
at cognitive ability, personality, interests
to knowledge skills, like all these different
91
00:07:15.250 --> 00:07:19.970
aspects, years of experience, age, education level, mean, the list
92
00:07:20.050 --> 00:07:25.329
goes on and on, and one
that routinely comes to the top is cognitive
93
00:07:25.370 --> 00:07:30.480
ability. It's probably the most predictive
across all those criteria, but it also
94
00:07:30.519 --> 00:07:34.160
comes with a little heartburn as a
result, because it creates something called adverse
95
00:07:34.199 --> 00:07:40.959
impact. So certain groups of individuals
tend to score lower on average than other
96
00:07:41.040 --> 00:07:45.990
groups, not because there's any inherent
differences, but just because of disadvantages in
97
00:07:46.110 --> 00:07:50.829
terms of equity standpoint. So we
tend to supplement cognitibility with other measures that
98
00:07:50.870 --> 00:07:57.430
are also predictive but don't come with
those biases like personality. So those in
99
00:07:57.589 --> 00:08:01.100
combination are very predictive and play well
off of each other, regardless of the
100
00:08:01.139 --> 00:08:03.819
role. And you know what,
maybe I skimmed over a little bit too
101
00:08:03.819 --> 00:08:09.740
quickly on this idea that years of
experience is less accurate predictor tell me your
102
00:08:09.819 --> 00:08:13.689
sense of why that is cheese.
Well, I think you're taking someone and
103
00:08:13.889 --> 00:08:18.970
there's transferable skills and knowledge, but
you're plucking someone out of this experience and
104
00:08:18.050 --> 00:08:22.569
putting them into entirely different experience,
even if it's the same industry, it's
105
00:08:22.649 --> 00:08:26.639
two different cultures, it's two different
groups of people you're working with. So
106
00:08:28.360 --> 00:08:31.919
just because I have ten years of
finance and experience in this one company where
107
00:08:31.919 --> 00:08:35.120
it's incredibly hierarchical, it
doesn't mean
that it's going to make me a great
108
00:08:35.120 --> 00:08:39.919
leader in an organization that's more collaborative
and people centered. So I think in
109
00:08:41.000 --> 00:08:46.429
large part it's because organizations are quite
varied. What I would say is too
110
00:08:46.549 --> 00:08:50.590
exactly identical candidates on the cognibility,
personality and like you go down the list
111
00:08:50.629 --> 00:08:54.740
of things, years of experience might
give you a little incremental bump. So
112
00:08:54.860 --> 00:08:58.059
instead of being, you know,
a success rate to fifty percent, maybe
113
00:08:58.139 --> 00:09:01.379
a bumps you up to fifty one
percent by going with the candidate with more
114
00:09:01.419 --> 00:09:05.620
experience. But by no means would
that be the first knockout that I ever
115
00:09:05.779 --> 00:09:11.809
look at when making decisions. Okay, so that reminded me of the Russian
116
00:09:11.970 --> 00:09:18.409
Roulette analogy you gave with and it
was specifically in regard to hiring. Let's
117
00:09:18.490 --> 00:09:22.210
hear it. Yeah, so I
think how most organizations approach selection is they're
118
00:09:22.250 --> 00:09:26.759
using old ways of thinking. So
let's look at years of experience in the
119
00:09:26.840 --> 00:09:31.639
school they went to and their grades
and all these things that can be scraped
120
00:09:31.759 --> 00:09:37.240
pretty readily from a resume. And
imagine you're that hiring manager. In essence,
121
00:09:37.320 --> 00:09:41.029
you're spinning that roulette wheel hoping that
you land on read and you get
122
00:09:41.070 --> 00:09:46.070
about just under fifty percent success rate
because you have the to the zero,
123
00:09:46.269 --> 00:09:50.909
the Green Zero and the Green Double
Zero. And with leveraging people analytics,
124
00:09:50.990 --> 00:09:56.659
imagine taking all the black spaces on
that roulette wheel and now shading about half
125
00:09:56.700 --> 00:10:01.980
of those two read that's really the
power of leveraging people analytics when making selection
126
00:10:03.100 --> 00:10:07.889
decisions and using science, because you're
all of a sudden focusing on for that
127
00:10:07.049 --> 00:10:11.490
role. Here's what success looks like. Here's the recipe, are Algorithm for
128
00:10:11.690 --> 00:10:16.730
success. It's these types of personality
traits, it's these types of interests and
129
00:10:16.809 --> 00:10:20.559
this type of background and skills.
And now, when you're spinning that roulette
130
00:10:20.559 --> 00:10:24.799
wheel, by no means is it
ever a hundred percent successful, because it's
131
00:10:26.000 --> 00:10:30.600
called science. It's not perfect,
but it's far better than just going at
132
00:10:30.600 --> 00:10:33.399
it with that gut reaction like,
Geez, I'm the hiring manager. I
133
00:10:33.559 --> 00:10:37.350
like this person because they're from Chicago
to and they grew up rooting for the
134
00:10:37.429 --> 00:10:41.070
bears and wow, they're just like
me and I love this candidate. As
135
00:10:41.110 --> 00:10:45.990
opposed to focusing on the things that
really matter, there's too many biases that
136
00:10:46.110 --> 00:10:50.419
come into play when using the old
way of thinking. Let's science do it
137
00:10:50.580 --> 00:10:54.740
for you and then have a final
reaction as a hiring manager of all the
138
00:10:54.860 --> 00:10:58.620
candidates that meet that criteria so you're
not making a bad decision. Then you
139
00:10:58.700 --> 00:11:03.299
can start going with more of that
gut feel and reaction and how that synergy
140
00:11:03.460 --> 00:11:07.250
is between the hiring manager in the
candidate. I know that they're maybe kind
141
00:11:07.289 --> 00:11:11.049
of myth floating around out there that
people think people analytics is science, it's
142
00:11:11.090 --> 00:11:15.769
going to be one hundred percent accurate
all the time and absolutely everything, and
143
00:11:16.250 --> 00:11:18.960
I think it's important to know that. It's going to make you more likely
144
00:11:20.039 --> 00:11:22.120
to win a Russian roulap, but
it's, as you said, not a
145
00:11:22.200 --> 00:11:26.360
hundred percent accurate. So take down
the house. Playing on the people analytics
146
00:11:26.399 --> 00:11:31.639
wheel, you would lose every time. Playing on the hiring manager with exactly
147
00:11:31.840 --> 00:11:35.230
exactly what it like, and with
people analytics you're spinning the wheel with a
148
00:11:35.429 --> 00:11:39.110
seventy percent success rate versus the typical
fifty percent success rate in terms of new
149
00:11:39.149 --> 00:11:43.429
hires. Yep, that's right.
So imagine, thinking your mind the best
150
00:11:43.509 --> 00:11:46.940
performer you know in a role and
you want more like him or her.
151
00:11:48.259 --> 00:11:50.659
You're spinning that wheel. You might
be lucky fifty percent of the time.
152
00:11:52.059 --> 00:11:54.419
So they turn out to be a
top performer and they stay long term in
153
00:11:54.460 --> 00:11:58.299
the organization. Now, coming to
the people analytics wheel, you're spinning it
154
00:11:58.500 --> 00:12:03.090
and you'll get that person about seventy
percent of the time. It's not perfect,
155
00:12:03.730 --> 00:12:07.970
but the cost of using the science
pals and comparison in terms of making
156
00:12:09.129 --> 00:12:13.529
the wrong selection decision. You think
of the miss of hiring the wrong CEO.
157
00:12:15.049 --> 00:12:18.799
It's estimated to be anywhere from ten
to twenty times their annual salary,
158
00:12:18.120 --> 00:12:22.879
and it's only more in terms of
like a fortune, five hundred, fortune
159
00:12:22.879 --> 00:12:26.399
one hundred organization. Even at an
hourly role in an organization, making a
160
00:12:26.519 --> 00:12:31.389
Misshire at that level still cost the
business tremendous amount of money, anywhere from
161
00:12:31.750 --> 00:12:35.990
twenty to fifty percent of their annual
salary. So I would rather spin on
162
00:12:37.070 --> 00:12:41.509
the seventy percent success and look for
opportunities to keep bumping that up. Yeah,
163
00:12:41.669 --> 00:12:45.379
me too. And Bright, you
have the distinction of being the first
164
00:12:45.419 --> 00:12:48.860
gambling conversation we've have on technically people. I was waiting for it and now
165
00:12:48.940 --> 00:12:54.379
we've got it. So okay.
So you've mentioned Dei. I would love
166
00:12:54.539 --> 00:13:01.330
to hear success story in terms of
how you've helped an organization get better.
167
00:13:01.850 --> 00:13:05.490
Adi. Yeah, if I think
of one healthcare organization that I helped,
168
00:13:05.690 --> 00:13:11.610
they had a big goal of how
do we attract, engage and retain their
169
00:13:11.769 --> 00:13:18.200
diverse talent, and in the beginning
that organization was very much soul focused on
170
00:13:18.440 --> 00:13:20.480
selection. So how can we put
better practice? This is in place,
171
00:13:20.639 --> 00:13:26.120
so we're not looking through the manager
Lens and more so looking through the Science
172
00:13:26.240 --> 00:13:30.549
Lens. So over the course of
a few years, their percentage of diverse
173
00:13:30.590 --> 00:13:35.789
hires increased by nearly forty percent,
which was outstanding. But over that three
174
00:13:35.789 --> 00:13:39.629
years span of time we got to
see who stayed and who left. And
175
00:13:39.789 --> 00:13:43.940
although they are bringing on diverse candidates
faster than they ever were before, they
176
00:13:43.980 --> 00:13:48.980
were also losing them at a much
faster rate than they ever were before.
177
00:13:48.139 --> 00:13:52.259
So when you look at their population, diversity really didn't change at all.
178
00:13:52.539 --> 00:13:58.970
So they focused on diversity as the
Noun as opposed to inclusion as a verb.
179
00:13:58.409 --> 00:14:05.210
And again looking at employee sentiment data. Connecting the DOTS will, we
180
00:14:05.330 --> 00:14:09.639
found was that diverse candidates were very
much attracted to the organization and sold on
181
00:14:09.720 --> 00:14:13.600
this idea of a diverse environment,
but when they got to their teams,
182
00:14:13.679 --> 00:14:18.320
their respective teams, they didn't see
a leader like them. So the path
183
00:14:18.600 --> 00:14:24.669
of career growth and development was very
much lackluster and they went and found that
184
00:14:24.830 --> 00:14:30.029
elsewhere. So they're almost sold a
false vision or promise of what it was
185
00:14:30.110 --> 00:14:33.750
going to be like. So the
organization immediately pivoted and started to put a
186
00:14:33.789 --> 00:14:39.139
lot of intentionality around how do we
identify and promote diverse talent, and that
187
00:14:39.179 --> 00:14:43.620
all of a sudden had a cascading
effect in terms of retaining talent like those
188
00:14:43.700 --> 00:14:50.220
leaders, because those fresh people they're
bringing on with diverse backgrounds, they want
189
00:14:50.220 --> 00:14:54.450
to see someone like them that it's
possible to succeed and go up the career
190
00:14:54.490 --> 00:15:00.129
ladder. So that organization is done
wonders and their communities to reflect the demographics
191
00:15:00.210 --> 00:15:05.009
of those they serve in are really
pioneering the way in terms of creating more
192
00:15:05.090 --> 00:15:09.200
of an inclusive environment and showing that
path forward. A lot of conversations I've
193
00:15:09.240 --> 00:15:15.200
had in particular a podcast with Louren
virgus towns, and her really strong point
194
00:15:15.240 --> 00:15:18.159
of view is that you have to
start with culture. You can hire diverse
195
00:15:18.279 --> 00:15:22.389
workforce the day before you roll out
your DII manifesto, but if your culture
196
00:15:22.509 --> 00:15:26.909
is not, as you said,
doesn't live up to the verb of inclusion,
197
00:15:26.389 --> 00:15:31.110
then those people are going to mass
exit. Is the BOTOM line.
198
00:15:31.149 --> 00:15:35.470
They'll find it somewhere else. So
all right, nice segue mass exit.
199
00:15:35.710 --> 00:15:41.539
So we established this incredible accuracy right
in terms of when you know a person
200
00:15:41.820 --> 00:15:46.059
may leave a company. But the
pandemic, of course, has made retention
201
00:15:46.659 --> 00:15:56.090
tripli important. So tell me where
retention and the pandemic intersect, Jay's well,
202
00:15:56.210 --> 00:16:00.769
first, in terms of our baseline
models, in terms of predicting nutrition,
203
00:16:02.409 --> 00:16:04.759
like you said, they're eighty five
percent accurate. So we collect a
204
00:16:04.879 --> 00:16:10.279
whole host of sentiment data from employees
and we get a really good sense of
205
00:16:10.360 --> 00:16:14.200
which way the winds blowing for them
in terms of are they committed to the
206
00:16:14.279 --> 00:16:18.149
organization, do they have this intrinsic
joy at work? Do they intend to
207
00:16:18.190 --> 00:16:23.070
stay for the next twelve months or
not? And those that do not are
208
00:16:23.269 --> 00:16:27.750
three hundred percent more likely to leave
the organization in the next twelve months and
209
00:16:27.950 --> 00:16:33.500
we're able to predict with that eighty
five percent accuracy if someone stays or leaves.
210
00:16:33.779 --> 00:16:36.220
Just being able to predict if they're
going to stay or leave. That's
211
00:16:36.620 --> 00:16:40.700
that's a nice parlor trick or parlor
game, being on the gambling hat,
212
00:16:41.460 --> 00:16:45.299
but the double click into that is
also being able to just to tell leaders
213
00:16:45.379 --> 00:16:48.970
why. What's the reason? Why? What are the barriers that are getting
214
00:16:48.970 --> 00:16:53.169
in the way of People's success at
work? Is it the relationship with the
215
00:16:53.250 --> 00:16:59.730
manager? Is it having more work
life flexibility? Yeah, and when someone
216
00:16:59.850 --> 00:17:03.799
is hired fully remotely, there is
not that honeymoon phase that you would have
217
00:17:04.240 --> 00:17:10.240
that if you're in person and getting
the chance to drink the Koolaid and everybody
218
00:17:10.319 --> 00:17:14.160
seems so nice. You're spot on
and again points to the importance of people
219
00:17:14.200 --> 00:17:17.109
analytics and why you should always be
looking at data. So if you would
220
00:17:17.109 --> 00:17:19.910
have asked me and my biased mind, I would bet my bottom dollar on
221
00:17:21.109 --> 00:17:25.509
always seeing this notion of a honeymoon
effect and the honeymoon effect and what we
222
00:17:25.630 --> 00:17:30.940
see in every survey administration are those
employees that are newly brought onto the organization
223
00:17:30.980 --> 00:17:37.059
are the most engaged they're seeing their
work lives through the rose colored glasses or
224
00:17:37.140 --> 00:17:41.779
the course of the pandemic, though, those that were hired and onboarded during
225
00:17:41.859 --> 00:17:48.289
the pandemic, they are acting,
behaving, feeling more are like they've been
226
00:17:48.289 --> 00:17:51.930
in the role for three to five
years, and that's the critical juncture where
227
00:17:51.970 --> 00:17:55.970
people tend to decide are they staying
or leaving? They're also the most at
228
00:17:56.009 --> 00:18:00.440
risk for leaving right now, those
that pandemic cohort is we've as we've termed
229
00:18:00.480 --> 00:18:03.319
it. And again, that's why
we need to keep looking at data,
230
00:18:03.599 --> 00:18:08.039
because these things change with with so
much pressure of an environment like a pandemic,
231
00:18:08.559 --> 00:18:12.309
on boarding look completely different. Even
if you were on boarding in person,
232
00:18:12.910 --> 00:18:17.869
it's not like you're sitting in the
break room talking to friends, sharing
233
00:18:17.910 --> 00:18:22.430
a meal. You're still very much
in your isolation during the pandemic. So
234
00:18:22.069 --> 00:18:29.819
it looked completely different and employees didn't
get that emotional attachment to their organization.
235
00:18:30.019 --> 00:18:33.339
They didn't fall in love with them
and have that honeymoon period. Yeah,
236
00:18:33.420 --> 00:18:38.180
so so many people in the HR
space are really rethinking how to on board
237
00:18:38.500 --> 00:18:41.930
and you know, in a way
this is a good framework. You know,
238
00:18:42.450 --> 00:18:48.170
create some onboarding that replicates the honeymoon
experience to whatever extent you can,
239
00:18:48.930 --> 00:18:53.329
and I would say one practical piece
of advice. The single most important relationship
240
00:18:53.410 --> 00:18:57.680
that we're finding right now that mitigates
many of the effects that we're seeing is
241
00:18:57.759 --> 00:19:03.279
the direct relationship with a manager,
and we're seeing overall that's pretty strong and
242
00:19:03.440 --> 00:19:07.599
when it is the strongest, that's
where we're getting that emotional attachment to the
243
00:19:07.640 --> 00:19:11.829
organization. But where employees are missing
out as on those fringe connection. So
244
00:19:12.430 --> 00:19:17.150
they're really close with their manager,
may be close with their direct a team,
245
00:19:17.269 --> 00:19:21.789
but when looking Cross Department or Cross
area where you'd be bumping into people
246
00:19:21.950 --> 00:19:25.700
in the office, they're really missing
those connections right now. So they don't
247
00:19:25.740 --> 00:19:30.059
have multiple connections, they're almost on
this little island that's pretty isolated. Yeah,
248
00:19:30.180 --> 00:19:33.420
the weak ties that they're most Rada, which is a misnomer, of
249
00:19:33.500 --> 00:19:37.460
course, because those ties are not
weak. They are very important for innovation
250
00:19:37.500 --> 00:19:41.970
as and, as you're saying,
for retention in this case. Okay,
251
00:19:41.049 --> 00:19:47.369
so let's see. Let us talk
about this concept. Office is m.
252
00:19:47.769 --> 00:19:52.920
So it is the idea that as
we move into the hybrid world, leaders
253
00:19:53.039 --> 00:20:00.960
could view remote employees as less committed. As you know, maybe less productive
254
00:20:00.000 --> 00:20:03.200
right because you can't see them,
can't see them other than you know are
255
00:20:03.240 --> 00:20:07.710
they logged on, and there are
all sorts of inclusion implications around that.
256
00:20:08.390 --> 00:20:14.430
We know, though, that the
majority of tech workers want flexibility. They
257
00:20:14.470 --> 00:20:18.430
want to choose how and where and
when they work, and the smartest companies
258
00:20:18.470 --> 00:20:25.019
are giving people that flexibility. However, if office is m becomes a reality
259
00:20:25.420 --> 00:20:30.140
and workers who do have that flexibility
face some form of discrimination, tell us
260
00:20:30.339 --> 00:20:34.220
what we can do about it.
Well, let me paint the horrible picture
261
00:20:34.220 --> 00:20:38.089
first, and I think we're going
to take decades step, words back in
262
00:20:38.210 --> 00:20:44.089
terms of inclusion in the workplace,
and the reason why is when we look
263
00:20:44.089 --> 00:20:49.240
at right now, the delicate balancing
active home work lives, what we're finding
264
00:20:49.440 --> 00:20:56.319
is that working mothers in particular want
to remain working remotely more so than their
265
00:20:56.359 --> 00:20:59.599
male counterparts, and for those that
do want to go into the office,
266
00:21:00.200 --> 00:21:03.279
they want to go in about one
fewer day per week on average. So
267
00:21:03.750 --> 00:21:08.069
the post pandemic world, where we
have this flexibility, where people can come
268
00:21:08.109 --> 00:21:12.390
and go as they want, you're
going to see a far more male dominant
269
00:21:12.430 --> 00:21:17.349
work environment. Now you throw into
the mix this notion of office is M
270
00:21:17.789 --> 00:21:22.619
we're just not leaders, but many
of us harbor these feelings. It's pervasive
271
00:21:22.420 --> 00:21:26.380
that those that go to the office
will have better relationships with their manager,
272
00:21:26.420 --> 00:21:30.299
are going to be the first ones
to be thought about for developmental or promotion
273
00:21:30.420 --> 00:21:36.089
opportunities. All of a sudden you
start seeing the glass ceiling that we've been
274
00:21:36.130 --> 00:21:40.210
trying to chip away at for decades
arise again and, if anything, turned
275
00:21:40.250 --> 00:21:44.730
into a concrete ceiling, because the
only ones that around the office, by
276
00:21:44.769 --> 00:21:48.640
large part, are heavily mail dominated. So I think that's the the picture
277
00:21:48.720 --> 00:21:52.519
that could go, that could get
painted in terms of why this is such
278
00:21:52.519 --> 00:21:56.319
a critical issue to focus on right
now and get ahead of it before it's
279
00:21:56.359 --> 00:22:00.880
too late. Yeah, and I
think compounding that, I would add that
280
00:22:00.279 --> 00:22:06.029
there's estimates that as many as two
million women could just split, could leave
281
00:22:06.029 --> 00:22:11.390
the workforce. We know they're downshifting
their hours or leaving their job at much
282
00:22:11.390 --> 00:22:15.740
higher rates and also considering career changes, and it seems like it could be
283
00:22:15.980 --> 00:22:19.339
pretty pretty dire. So tell me
how to fight this. Are there any
284
00:22:19.500 --> 00:22:23.220
any areas to look at? You
know, from a people analytics perspective,
285
00:22:23.259 --> 00:22:27.819
I would consider that group. So, whether you're in the office or you're
286
00:22:27.859 --> 00:22:33.450
working remotely or hybrid. I would
classify those is almost like a dei category.
287
00:22:33.809 --> 00:22:37.730
So for the analytics team you should
be focusing on making sure that there's
288
00:22:37.769 --> 00:22:41.609
equity among those groups. So what
do we look like in terms of pay
289
00:22:41.690 --> 00:22:44.920
for performance? What do we look
like at promotion rates? What do we
290
00:22:45.079 --> 00:22:48.000
look at in terms of retention?
So you want to make sure that you're
291
00:22:48.119 --> 00:22:52.400
creating a great experience for employees,
regardless if they're going into the office every
292
00:22:52.400 --> 00:22:56.480
day or they're working remotely every day, and make sure that those outcomes are
293
00:22:56.640 --> 00:23:02.950
very equivalent to the best you can
and have some intentionality around that and dispelling
294
00:23:03.069 --> 00:23:07.390
many myths. So the myths of
the people are in this productive with all
295
00:23:07.509 --> 00:23:11.069
of the digital
exhaust that we give
off in terms of workers for many roles,
296
00:23:11.109 --> 00:23:15.740
especially the roles that can be done
remotely, you can back up with
297
00:23:15.859 --> 00:23:21.180
some pretty concrete evidence from a people
analytics team perspective in terms of that productivity,
298
00:23:21.539 --> 00:23:25.259
that they're just as productive, if
not more productive. So I think
299
00:23:25.299 --> 00:23:29.769
you can dispel those myths. Start
Looking at it through a Dan Islands and
300
00:23:29.930 --> 00:23:34.130
the most progressive organizations are really thinking
about the future of work. They're creating
301
00:23:34.210 --> 00:23:41.079
roles where they're just responsible for this
hybrid so like directors of remote experience or
302
00:23:41.359 --> 00:23:45.880
directors of our extended community, or
so they're really putting intentionality on. We
303
00:23:47.079 --> 00:23:51.000
know that how the work gets done
is maybe a little bit different and what
304
00:23:51.200 --> 00:23:52.960
each group wants is a little bit
different. Let's make sure we're meeting the
305
00:23:53.039 --> 00:23:57.950
unique needs, and that's where the
employee listening strategy can play a large part,
306
00:23:59.309 --> 00:24:03.029
where your sense of seeing those groups
task. What do they want to
307
00:24:03.069 --> 00:24:06.150
be? What do they need to
be successful? What's getting in the way
308
00:24:06.190 --> 00:24:08.750
of their success? What are they
worried about? And really catering to those
309
00:24:08.789 --> 00:24:15.299
individual groups is opposed to some blanket
umbrella. The blankets statements or the blanket
310
00:24:15.700 --> 00:24:21.500
approaches rarely work because you have segments
of groups that want different things and need
311
00:24:21.579 --> 00:24:25.730
different things to be successful. Yeah, so, Brett, I read your
312
00:24:25.769 --> 00:24:30.609
article in Forbes and you mention that
you should move away or just totally forget
313
00:24:30.650 --> 00:24:36.329
annual performance evaluations where the goal is, to quote unquote, meat expectations.
314
00:24:36.529 --> 00:24:40.400
I thought that was interesting. So
we're moving away from meeting expectations. What,
315
00:24:40.680 --> 00:24:44.880
then, instead should we do?
And the rationale behind that is whenever
316
00:24:44.880 --> 00:24:49.319
I get performance data as a consultant, I start getting excited because wow,
317
00:24:49.400 --> 00:24:53.230
we have something tangible to predict.
This is what the organization cares about.
318
00:24:53.349 --> 00:24:56.589
And when we get the data finally
in hand, you find out that,
319
00:24:56.829 --> 00:25:00.430
you know, eighty percent of employees
meet expectations, very few are far exceeding
320
00:25:00.470 --> 00:25:06.549
and very few are struggling. So
it's meaningless for trying to predict anything.
321
00:25:06.789 --> 00:25:11.259
So instead it's thinking
about what does
success redefining what success looks like in the
322
00:25:11.380 --> 00:25:15.619
role. How can we measure it
more accurately with concrete things? Is opposed
323
00:25:15.660 --> 00:25:21.259
to abstract manager ratings, which are
riddled with air and are not the most
324
00:25:21.259 --> 00:25:26.089
reliable indicators of performance. So rethinking
performance in the hybrid world is going to
325
00:25:26.130 --> 00:25:30.890
be very critical and I think will
dispel some of the officism. What many
326
00:25:30.930 --> 00:25:33.130
of us think productivity means is we
go to an office and where they are
327
00:25:33.170 --> 00:25:37.759
nine and twenty five, or we
leave after the boss leaves. That's impossible
328
00:25:37.880 --> 00:25:41.559
to do in the remote world and
especially where employees are asking for flexibility.
329
00:25:41.880 --> 00:25:48.039
So instead let's redefine what productivity means
and measure it that way and give employees
330
00:25:48.400 --> 00:25:52.589
what their expectations are to meet,
regardless of where you're working all right,
331
00:25:52.869 --> 00:25:57.349
so, Brett, how about we
move into our two minute takeaway and actually,
332
00:25:57.390 --> 00:26:00.390
you know what I want to share
my key takeaway from this conversation,
333
00:26:00.630 --> 00:26:06.420
which is, you know, I
was always fascinated by your concept of officism.
334
00:26:06.579 --> 00:26:07.819
I just think it's brilliant. But
you know, when you paint to
335
00:26:07.900 --> 00:26:12.140
that picture and said that, you
be taking a giant step backwards in terms
336
00:26:12.180 --> 00:26:18.339
of Dei and inclusivity and creating a
sense of belonging and allowing people to be
337
00:26:18.500 --> 00:26:22.250
their whole salves at work. You
know, that really struck me as something
338
00:26:22.369 --> 00:26:26.369
people are going to need to think
about and are going to need to fight
339
00:26:26.529 --> 00:26:30.490
if they care in fact, about
creating a great workplace of the future.
340
00:26:30.529 --> 00:26:33.599
So, anyway, that my key
takeaway. So tell me a couple of
341
00:26:33.720 --> 00:26:37.400
things you want us to understand,
and I think your spot on, and
342
00:26:37.359 --> 00:26:42.799
the I'll just extend your great insight
and many organizations are probably feeling right now.
343
00:26:42.839 --> 00:26:47.390
We're doing exactly what our employees asked
for. They want to work remotely
344
00:26:47.430 --> 00:26:49.829
and we have plans of doing that. But now they need to think beyond
345
00:26:51.069 --> 00:26:56.309
just meeting that need and how do
we create equitable experiences for things that employee
346
00:26:56.390 --> 00:27:00.630
still value and want like career growth
opportunities and promotion, and how do we
347
00:27:00.750 --> 00:27:03.980
make sure that office is M isn't
getting in the way, regardless of where
348
00:27:04.059 --> 00:27:10.779
people
are working? Second one is
just to remind people that they're sitting probably
349
00:27:10.980 --> 00:27:15.700
on a treasure trove of data and
if they don't have a people analytics team
350
00:27:15.890 --> 00:27:19.410
today, there's still opportunities to go
in and get what I think is a
351
00:27:19.410 --> 00:27:25.769
quick win. Start looking at your
turnover data, start looking at your selection
352
00:27:25.930 --> 00:27:30.039
decisions, start mining and finding those
nuggets, because I promise you they are
353
00:27:30.160 --> 00:27:34.359
there in terms of making smarter decisions. And then our final takeaway would be
354
00:27:34.640 --> 00:27:41.759
with so much change that we're experiencing
and pretty severe gravity into the changes,
355
00:27:41.039 --> 00:27:47.390
with all of that going on,
there's no better time to listen to employees,
356
00:27:47.430 --> 00:27:51.430
stop having the mindset of we think
we know what our employees want and
357
00:27:51.589 --> 00:27:55.829
instead just ask them the questions.
But then asking isn't enough. What we
358
00:27:56.029 --> 00:28:00.900
find in our research is that the
organizations that are performing the best frequently ask
359
00:28:02.019 --> 00:28:06.859
their employees for feedback but, most
importantly, they act on it quickly.
360
00:28:07.299 --> 00:28:11.660
So I can have a great conversation
with my spouse tonight and she could tell
361
00:28:11.779 --> 00:28:15.369
me that, you know, I'd
really appreciate it if you would put away
362
00:28:15.369 --> 00:28:18.450
the dishes more often, fold the
towels, you know, etc. Etc.
363
00:28:19.130 --> 00:28:22.009
Maybe this is fresh in my mind
exist what happened last night, but
364
00:28:22.650 --> 00:28:26.009
I need to act on that.
I can't just listen and say, Oh,
365
00:28:26.089 --> 00:28:29.519
good talk, honey. I need
to now today, act on it
366
00:28:29.799 --> 00:28:32.920
if I want to re engage her. And organizations need to do the same
367
00:28:32.960 --> 00:28:36.880
thing. It's no different in terms
of a relationship they're trying to create with
368
00:28:37.119 --> 00:28:41.230
each and in every individual employee.
So listen to your employees, but respond
369
00:28:41.269 --> 00:28:47.069
and help them connect the dots.
We value your input. That's why we
370
00:28:47.150 --> 00:28:49.470
asked. This is what you told
us and here's what we're doing about it.
371
00:28:49.950 --> 00:28:55.940
And not always will the organizational decision
align with every single employee and what
372
00:28:56.019 --> 00:29:00.339
they want, but showing that kind
of bread crumb trail for employees will incentivize
373
00:29:00.420 --> 00:29:03.819
them to share their voice more often. Maybe it wasn't the decision I wanted,
374
00:29:04.019 --> 00:29:07.059
but one time it will be.
So I'm going to give my voice
375
00:29:07.099 --> 00:29:11.930
because I don't want to be forgotten
about. So, okay, I think
376
00:29:11.049 --> 00:29:18.250
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
378
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
387
00:30:00.660 --> 00:30:06.299
that and my email. I'll just
throw out my email be wells at perceptixcom.
388
00:30:06.460 --> 00:30:07.930
Awesome. Well, I can't thank
you enough. As you know,
389
00:30:07.970 --> 00:30:12.609
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
396
00:30:41.430 --> 00:30:45.750
you're seeking to meet aggressive hiring goals, will help you attract sought after tech
397
00:30:45.869 --> 00:30:49.779
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
399
00:30:56.180 --> 00:31:00.500
with built in find talent, find
opportunities built incom