Welcome to our webcast.
4 steps to becoming an HR
analytics champion.
We're so excited to have you
join us today.
My name is Sarah O'Brien, and I
lead LinkedIn global insights
capability.
My team is responsible for
leveraging LinkedIn's data to
help companies acquire and
develop talent and grow their
businesses.
Many of the insights created by
my team have been developed into
our new self-serve analytics
offering talent insights, which
you will hear about later.
Prior to LinkedIn, I played
leadership roles in the advanced
analytics practice and the tech
media telecom practice at Bain
and Company, a strategy
consulting firm where I helped
companies across the world
become data driven
organizations.
I'm joined here by David Green.
>> Sarah, it's a pleasure to be
here with you today.
For those who don't know me, my
name is David Green.
I've been in the HR talent
acquisition space most of the 20
years in the last five years,
within the analytic space so I
work for a company called
insight 2 to 2 and we work with
people, analytic leaders and
their teams to have more impact
with data driven impact within
the organization and make sure
they're using the data in an
ethical and wise way.
I'm also very fortunate, I get
to speak a lot about people
analytics, traveling the
conference around the world and
in fact that's why I'm in the
Bay Area this week because I've
been cochairing at a conference
which I believe you are speaking
at
the
>> I am come I'm very excited.
I'm speaking on using people
data for good so I am very
excited about that.
We're excited for the webinar
today, but before we dive in,
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>> So let's take a look at the
agenda that we are going to
cover today.
Basically, I think you would
agree with me Sarah, talent has
never been greater.
We are living and working in
perhaps the most destructive
times any of us have ever
experience of its becoming even
more important to leverage all
the data that we've gotten human
resources but certainly a
data-driven function or a team
across every dimension of HR
from talent acquisition,
performance, workforce planning
and retention all to solve
business challenges that your
organization has can be a very
complex and challenging thing.
>> Exactly.
And that's way earlier this
year, LinkedIn ran an analysis
of the data of our on our
platform and conducted in-depth
research sessions with top HR
leaders in industries like
retail, tech, finance, and
government all across the globe
to unearth the latest trends and
talent analytics and gather
color into how companies are
tackling their journeys and
developing talent analytics
capabilities.
In this web cast, we'll sharing
these findings including
everything from the state of
adoption to how to answer your
toughest talent questions with
data and analytics.
We'll start with a broader look
at the rate of adoption talent
analytics which will cover why
we're seeing such a significant
rise in the last few years.
We will also discuss how early
adopters are using data and
insights for both traditional
and more unique use cases.
Then we'll outline what you can
do to build a data-driven
culture in your organization.
Lastly, we'll cover how you can
apply analytics to answer talent
questions like who are my
emerging telling competitors and
how can I ensure that my talent
brand will hold up to a attract
and develop the right
candidates?
>> Thanks, Sarah.
So let's get into the thick of
it.
What does the landscape of
analytics and HR look like?
Well, certainly something that
I've seen the last 18 months,
people analytics is really
started to surge forward.
One thing we want to highlight
really off the bat is that the
conversations about applying
data analytics to HR isn't new.
This has been around for quite a
while.
The interest levels and adoption
levels have certainly increase
and there's a buzz about it
which is fantastic and again,
the reality is that one of the
most important assets of a
company is its people data so
I've been in this space for five
years but certainly been talking
about it for 10 years I think.
Organizations are at different
maturity levels when it comes to
people analytics
adoption.
Even though in a lit axis been
on the minds of HR leaders and
business leaders for some time,
it's only really in the last
five years, certainly looking in
North America, we see through
times increase in HR
professionals who list analytic
skills and keywords on their
LinkedIn profiles, while many HR
professionals leverage analytics
and data as part of their
general rule we are seeing an
increasing number now
professionals focusing primarily
on HR analytics and we are
seeing some of these teams
really grow quite significantly.
There are number of names for
these teams, talent analytics,
HR, talent insights, people
insights and particularly we're
seeing these teams emerge at
larger, more forward thinking
enterprise companies are
>> interesting.
So we see tremendous growth in
North America.
What about in other regions
across the globe?
>> I think that's the beauty of
your research because you get
regional reports so if we look
here, we see a 61% increase in
just a single year of the number
of professionals on LinkedIn
with HR analytics in their
profile and similar to North
America over a five-year span
and a pat we saw an increase of
70% as well.
You know putting these trends
together is a signal that we
really started to reach her to
"with people in politically.
We become more integrated into
the way the HR teams are
building their strategies and
maybe a few years ago the
analytics team was sitting on
the periphery of HR now we're
releasing it to driving HR
surgeon moving forward and as I
said, the trend is a new but
what is new is the rates of
growth, so we're going to look
at a few things, try to
understand what's behind the
growth, why now, and how it's
impacting the way organizations
are leveraging people analytics.
>> And fantastic.
So let's talk about some of the
drivers.
>> So I will take the first 1.
I think the first driver of this
growth is the relationship
between the CEO in the seat HRO.
Key questions on talent, top of
mind for CEOs is organizations
seem to be perpetually in
reinventing themselves,
developing new products and
services, if we look at the
survey for 2018, 77% of CEOs
believe that the biggest out to
their businesses the lack of
availability of key skills.
CEOs are so concerned about
talent, they're putting some of
that pressure on talent teams
and see HRO's to help plan for
the workforce of the future
As many of our listeners and
viewers know, when you're trying
to develop your retention
strategy, build a pipeline of
candidates, find that key guy
that will transform your
business, understand how teams
get things done within
organizations, you want to use
everything that you can within
your toolkit and HR data and
people analytics can help answer
many of those critical concerns
that see HRO's much must grapple
with.
We can talk about some of those
later but we talk about
diversity, we're going to talk
about geolocation decisions, we
will get to hiring strategy and
competitive bench marking, the
breath of HR.
>> Exactly right, and as talent
becomes more of a CEO level
topic, we also see the HR Tech
landscape innovating to be able
to meet demand, so the second
driver of talent analytics
adoption is the growth of HR
Tech and innovation.
For anyone that's been to an HR
Tech conference recently, you
will have noticed the expansion
of vendor offerings across
really all aspects of the HR
lifecycle.
Will that means that the
landscape may seem more
fragmented and complex than
ever, there's also an
unprecedented amount of
innovation happening.
The development that I'm
personally most excited about is
the shift towards making data
and analytics more accessible.
In the past, it was much more
difficult to get data,
especially the relevant data you
needed to answer the questions
you cared most about and sorting
through internal data in an
organization is quite
challenging.
You needed a full team with
really specialized skills like
data mining, including languages
that a lot of companies didn't
have.
The model wasn't as scalable or
as accessible for the recruiter
or the average HR VP but what
I'm excited about is the static
HR management systems of the
past are now moving toward a
much more dynamic, real-time
cloud and mobile-based tools and
platforms.
What that means for talent
leaders is that getting the data
you need is becoming much more
about cliques then code.
Since that data doesn't require
as much work to manipulate, now
what you have to be is just a
problem solver in order to get
to know the right questions to
ask the data and therefore be
able to make sense of the data.
>> Yeah, and I think the third
area that we want to highlight
outside of this, I think this is
something that talent
professionals feel pretty
acutely is that we're working in
an increasingly competitive
landscape.
We have more data as HR
organizations but so do
candidates as well and that
limited pool of top talent have
a lot of people create more
scarcity for the talent that you
need most and what people
analytics doing what data can do
is give you that competitive
edge.
You can support more impactful
conversations for recruiters and
HR professionals with the
business.
I'm seeing more companies use
predictive in the Lititz to help
build and manage great teams and
we're also sing the muse
analytics to actually gain
insights to individuals and
managers about their own
behaviors well and folks around
them as well.
We talked about how the rate of
people in a lid exit option has
grown over the last five years
and particularly in the last
year and there's still an
opportunity to become one of the
early adopters to help shape
this new trend within HR.
>> This is really exciting.
If you look at these three
drivers, it's not just about
what's driving the increase in
numbers, it's also completely
changing the role of an HR
professional and making the HR
team much closer to the business
and making really important
decisions, so with that, let's
talk about the state of
adoption.
>> Let's look at some of the
results.
So just to your point, HR is an
exciting place to work, it's
there's a lot of change going on
and HR professionals to learn
and acquire new skills and as we
see from the data itself, this
was across all organizations of
all shapes and sizes, 22% of
companies have now adopted HR
analytics but not only just the
companies, they've built teams
as well and certainly a lot of
the teams I work with are
scaling quite considerably the
moment as well so it's clear
that the adoption of people
analytics is happening quickly,
but the data seems to suggest
it's not quite table stage yet
further the majority of
companies across North America,
and yes I think we're now going
to look at some of the sections
we did some analysis of the
sector in an HR in here the top
six leading the charge I'm sure
like me you weren't that
surprised to see that tech and
finance are at the top.
It makes sense.
There's already a high emphasis
on quality of data within those
sectors and probably also within
many of those organizations a
kind of culture of data within
the organization, people
comfortable using data to help
make decisions and that
definitely helps HR
organizations that want to wrap
up their people analytics
functions.
Another sector there that's come
out quite high is oil and energy
and that makes sense as well so
if the oil and energy companies
I worked with our particularly
research and academic led
initiative to how they work.
They are some of the larger HR
organizations as well.
>> And as you mentioned, this
data comes from the LinkedIn
platform but it's inclusive of
all sizes of companies.
If you were to look only at some
companies you would probably see
adoption rates significantly
higher than what showing here as
large organizations tend to have
more scale and resourcing to
dedicate HR analytics.
>> Yeah, and I can certainly
give an example of that so the
corporate research firm in the
UK, at the end of 2017 they did
a survey and they found 69% of
large organizations, those with
10,000 or more employees now
have a people in a lytic semen
although I think some people on
those teams are probably during
reporting, it shows that the
appetite is there and this is
something that's really moving
forward.
But I was quite interesting
there, I see a really big
opportunities in the retail
space, insert area where I'm
seeing quite a lot of growth in
people in a lytic's.
There's a lot of emphasis on
data from the consumer side and
that is starting to translate
across the as well a lot of
people are interfacing directly
with customers I think there's a
big opportunity therefore that
sector to use data more within
the people space.
You know, and that is an
important thing to really
optimize you gotta tie this to
business performance, however,
much data you've got on people
data you've got you really gotta
focus on some of those key
business questions for your
organizations.
>> So let's dive into that now
that we've talked a little bit
about adoption rates.
>> Yeah, so let's look at what
some of the leaders in early
adopters are using people and
old export.
Let's look at some of the top
use cases and some of the key
med true Paulison areas in the
US.
>> Perfect.
So in this research we look
deeper into the data to unearth
adoption patterns by use case
for a few of the biggest metro
areas in North America.
You can see here the top 10 use
cases that popped for New York,
San Francisco, Washington DC and
Chicago.
1st thing that jumped out at me,
and each Sydney compensation and
benefits is the number one use
case.
It's not particularly surprising
given the inherently
quantitative nature of benefits
and the long-standing practices
of compensation benchmarking.
I think this field is changing
though and we actually saw it in
our talent trends report which
was released yesterday where pay
transparency is becoming a much
bigger trend and so we start
seeing compensation and benefits
shifting from being a more
inwardly facing discipline for
HR analytics functions to being
more of a market driven and
something that is more candidate
driven in the way that
compensation and benefits
analytics is playing out.
Productivity and performance
rounds out the second most
frequent use case across the
board.
Another newer use case we're
seeing growth is the analytics
for culture and diversity
strategy.
This is a really interesting one
because historically it's been
hard to put numbers on this but
there's a ton of momentum.
You will see here particularly
in San Francisco were culture
and diversity is now the number
three top use case for
analytics.
Why?
I think there are a few things
driving it.
1st, building inclusive teams is
top of mind for companies and
their HR leadership in a way
that wasn't the case five years
ago.
It isn't just about having
diverse talents for the sake of
having diverse talent.
Diversity and talent is critical
towards actually being able to
build products that companies
can then have global reach with
because the tech companies are
billing platforms that have this
global reach and need to reach
more diverse audiences.
By having products that aren't
just built by folks that look
and think the same, they will be
much more effective in their
overall business strategy.
Combine that with the skills gap
for tech talent that's happening
right now and by expanding into
more diverse talent pools you
can open up new avenues and
potentially find new candidates
that would not have been
possible otherwise but you can't
fully impact what you can't
measure and so there's been this
huge momentum that has prompted
a new wave of innovation and
measure for culture and
diversity.
From a gender diversity lens,
companies are using our talent
insights product to benchmark
their gender representation
across various types of talent
against their local market or
against of their industry to be
able to understand whether their
starting point is one that
strong or weak.
Companies are also doing really
innovative stuff with their own
data whether that be analyzing
keywords in job posts and
application requirements to see
how various types of applicants
respond, whether that be
companies analyzing employee
behavior for key knows that
matter such as parental leave to
help inform the policies they
create.
Talent acquisition continues to
be a strong use cases well and
actually a lot of the examples I
gave with respect to culture and
diversity are different subsets
of talent acquisition.
Another one I'd like to point
out is workforce planning.
This is another area where
predictive modeling is becoming
increasingly important.
As the shelflife of skills is
decreasing, companies are
looking to new types of data to
ensure that their workforce are
equipped to develop and grow the
skills that they will need in
the future, not just those that
they have worked for them sick
historically so being able to
rely on data W to understand
skills that are emerging in
their company or in their
industry that they will need to
get ahead of will help companies
with their workforce strategy.
As I look at the list, one that
I'm a bit surprised isn't higher
is employer branding but I
suspect this will change going
forward.
The employer brand movement is
still quite new and initially a
lot of it was around, how do I
identify the message for my
company that we need to be
rallying around?
And what should be our employee
Valley value proposition and how
should that translate to
candidates and that is
inherently more qualitative at
the outset but then as data
becomes more ingrained process
and as you start to see more
marketeers enter into the
landscape of employer brand,
then we start to see companies
asking questions like how was my
brand resonating with internal
employees, external candidates,
how does that differ by talent
pool?
How do I think about targeting
my brain investment and across
this entire spectrum there's a
huge opportunity to leverage
data so David, as you look at
the use cases, anything else pop
out at you?
>> Will before I talk about
retention, I think one thing
that's really quite good about
this, this is I think I believe
it opportunity for people
analytics, some of those things
on the list.
HR we've been talking about the
important importance of employee
engagement and cultural
diversity for a long time and
some business leaders get it,
other business leaders don't end
with analytics and data we can
actually start to show the
business benefits of this.
Not only is it the right thing
to do, but it's the right thing
for the business to do as well
as I think that HR professionals
help make our workplaces better
and more humane.
Yes, clearly people analytics is
there to help drive productivity
and performance as well and
business outcomes and I think
these people outcomes are really
important.
One thing you were surprised
about employee brand, I was
quite surprised about
retention.
Large organizations that I work
with typically a use case that
they will tend to get started
with his retention.
It's relatively easy to quantify
what the ROI is on it, their
great use cases out there I
think there's one in the report
actually run Nielsen, they
determined that a 1% increase in
attrition was worth $1.5 million
seek out that business case to
see what is causing attrition
and solve problems to software
but my hypothesis is that
retention isn't necessarily the
most sexy thing to talk about
and that's possibly why people
don't highlighted as much in
their LinkedIn profiles and I
think that's what makes this
such an interesting piece of
research actually is because
it's a unique data set.
It's nuke more quantitative
weather than qualitative which
is how many of these studies are
put together and actually just
looking up information that's
there and people profile so
yeah, I was quite interested at
that.
>> Welcome I think perhaps
another explanation could be
with productivity and
performance first or as the
number two here, productivity
and performance may actually be
the leading indicator ever
tension and so by focusing on
getting the lead indicator
right, then hopefully you end up
moving the ultimate result of
keeping people longer or keeping
them happier.
>> Exactly and I think you can
also use the in elixir try to
identify employees at risk of
burnout so then you're actually
trying to solve the problems of
retention rather than just
talking about retention itself.
Think we could probably talk
about this all day but we
probably need to move on so the
next section really is in terms
of how to have a data-driven
culture.
Now we talked about some of the
top ways, Sarah you particular
talked about some of the top
ways HR leaders are using data,
business problems they're trying
to solve with that.
What we want to think about the
section a little bit more is how
companies and HR leaders are
structuring these people
analytics teams, what skill sets
are needed for the teams and the
really critical thing, how do
you build that data-driven
culture?
>> Absolutely.
So with that, will share a
little bit with what it takes to
structure and HR data analytics
team and all actually use
LinkedIn's internal people
analytics team as an example.
So if you look at LinkedIn's
internal analytics team, they
need to do three things really
well.
1st, deeply understand the
business and I consulted to ve
Madden
manner.
The team reports directly to the
HRO and has a deep foundational
understanding of the talents
questions they are challenged
with solving.
2nd, the team needs to be
equipped to handle complex
research questions which draw an
increasingly wide range of
technical skills.
Many long-standing HR analytics
team started with a backdrop of
organizational psychology but in
the era of big data, their
extending their skill portfolio
to be able to tackle new data
sets, analyze new types of data
and take on new research
questions.
This is continually expanded the
portfolio of what it takes on
the research side.
3rd, teams need to be able to
continually scale accessibility
to analyses so that the ones
that have already been solved by
the small people analytics teams
can then be self served and
accessible to the entire
business and that way the people
analytics team can move on to
tackle the next wave of
questions.
This data capability requires
capabilities and data
structuring and scalable
visualizations.
Balancing these three skills is
particularly critical because
applying talent analytics
effectively requires multiple
disciplines working toward a
single goal and often where we
see companies fall down is
companies that overfocus on one
of the areas.
For example, over focusing on
research and then the benefits
of that research don't
necessarily lend to the business
or over focusing on the
consulted tip side and then not
developing enough of the
scalability to be able to make
it work.
>> Or over focusing on the data.
>> That's the most common.
>> That is the most common, not
actually answering a question
that the business is about, come
up with great insights but no
one really cares about it.
So yeah, as you said, you have
to have that nice balance
between those three
areas.
>> So this gives the high level
but we also drilled into the
data on our platform to
understand the specific skills
that are analytics teams are
building.
So here you see the three key
skill sets that you need while
trying to build up your
team.
As we mentioned this is based on
data that is actually resonant
across the LinkedIn platform so
it is a composite of all the
skill sets people are building
today.
In the case of LinkedIn, we have
a larger team for our internal
people analytics team and as a
result, we have more resources
to put behind them but even
smaller organizations with
leaner teams can start creating
a more data-driven culture by
focusing on core skill sets in
these three areas.
>> Yeah, and I think as you
highlighted Sarah, big
organizations have that luxury
as it were to try and build all
this within the team and not
necessarily leverage skills from
elsewhere in the organization
partners from outside.
When you're working with a
smaller team, and some ways that
increases the need to have some
level of basic data across all
your non-data experts, we really
need to predict literacy across
HR anything that's a challenge
for many organizations, and that
means that the data needs to be
more accessible for HR leaders
to get their questions answered
and business leaders as well.
Maybe with the smaller team
that's key here is to focus on
the few decisions that really
matter, the real business
questions that matter and
leaning into those and as you
are going to show later, for a
lot of companies big decisions
can be around geolocation
decisions as well.
For example, you put your
engineering hub in San Francisco
or do you distribute your
workforce across other locations
within the country or across the
world and that is a focus for
many analytics teams.
These are decisions you do, you
have to figure out the most
important pieces of data and I
can be people data or business
data, that can be data that is
outside of the organization,
that can be new data that you
need to create.
You need to make that readily
available to the team.
A lot of this is thinking about
business challenges as we both
touched upon, the first measure
of people analytics start with
the business problem and then
asking what is the data that we
could potentially get to give me
insights to help me solve that
problem.
And this is one of the
misnomer's I think around people
in the X, you know, not everyone
within HR needs to learn how to
bring data to manage it, guess
similar the attorneys to be
data-driven and be prepared to
answer questions, that's
cleaning and managing the data,
that's a specialist job and we
are seeing some of that, and
influx of that talent into HR
but if you have someone on the
team that goes and you can focus
on some of the bigger the
questions.
>> Yeah, one last thing I'll
mention here even though we've
spoken about the skill set of an
HR in the lytic steam is
definitely possible to dive into
HR in the lid X without even
having a dedicated analytics
team.
If you recall the data that we
shared earlier, across every
industry only about half of the
companies with HR analytic
capabilities actually have HR
analytic teams.
Without an HR analytic team you
are more likely to lean on
off-the-shelf analytics products
rather than build proprietary
models, as you mentioned, more
likely to pick and choose the
questions that really matter and
that alone will get you a long
way.
After all, we're headed to the
New World of clicks, not code.
>> So we're going to look at how
to build that data-driven
culture within HR.
Now there's probably Lawton a
lot we could put here.
I think the really important
thing is communicate the purpose
and the value and the benefit of
analytics and HR.
I think it's almost a mission
statement within the team I
think it's great for the team
itself because it helps
immobilizer in a common goal but
it's really good for the
organization as well because
then they understand what the
team does and what it doesn't do
and what the challenge is our.
A lot of the challenges everyone
thinks they are there to do
reporting and provide bits of
data within the organization.
The second point I think you
want to talk about was learning
side.
>> Yeah, so creating a culture
of learning.
Analytics will probably get some
HR teams thrilled and some
members of the team throughout
and some will probably be quite
scared because it is a vast
difference between how H are
teams of operators in the past
but the reality is at the crux
of being successful in a
data-driven HR organization is
having a strong handle on what
questions to ask and that really
starts with the mindset of
curiosity and that mindset of
curiosity can help instill the
right culture even in the
top-performing HR analytics
organizations, no single
individual encompasses all of
the skills and listen on the
prior page so the culture of
learning is largely one of
creating the right environment
to ask questions, having
specialists in the right areas
to be able to draw from and then
providing platforms to shore up
knowledge gaps on a more
personalized basis were folks
needed.
>> Yeah, I think if you find
someone that's got all of those
skills, lock them
down.
They are unicorn or one of the
only people in the world that
has all of the skills and I
think it's really important that
we do improve the data literacy
in HR and we make training and
learning available clearly for
people within people in the
lytic steam but also people in
HR who want to learn more about
us they can be more comfortable
and competent with it and I
think the other area were
organizations can get to us when
they're hiring people and HR,
make analytical capability early
data literacy one of the key
components of it, bringing in
people who have that comfort
with that I think is a good way
of doing that.
So I'm sure we could talk about
how to build a data-driven
culture a bit more, but we'll
talk about the fourth area of
this webcast which is how we
apply analytics to answer
questions.
Introducing and analytics motion
into HR, goes beyond simply
investing in talent with the
right skills and identifying the
key business
areas.
You also need to understand
where you can apply in a
practical sense of it's all
great getting a bunch of nice
interesting insights, we need to
be able to take actions on them
and measure the outcomes on them
as well, so the key to success
in utilizing data is
understanding how to apply to
your business, your leads and
your organizational goals.
There's lots of great case
studies out there from what
various organizations have done
with analytics and scrape to
learn from but you must apply
what's relevant within your own
organization.
Whether that's the recruiter,
the HR business partner, VP of
HR, the learning development
consultant, the head of,
whoever's using it to make a
business case, highly gaps, have
strategic information or maybe
use that data to increase their
performance as well or behavior
around their teams, access to
analytics from HR professionals
more efficient in their job,
they cannot more impactful
conversations with people within
the business, with each other
frankly and if it elevates their
position within the
organization.
The vision really should be to
make analytics and that's what I
really like what you're doing,
make analytics available in a
format that is simple to access
so general HR practitioner the
recruiter can easily reference
and use it.
As far as I'm concerned, what I
believe a central goal should
always be how can we answer our
toughest most impactful
questions with data?
>> Perfect so with that, let's
talk about some of the questions
that we find HR professionals
asking.
We have had the benefit of
working with our customers
across the whole host of talent
questions and these are some of
the ones that we tend to see
coming off of most often from
talent leaders like you.
Some of the biggest questions
are first, which companies are
regaining talent from and losing
talent to? What skills were our
company need in the future? What
are we seeing emerging and other
companies that we look at as
best practice?
Where should we open our next
office or if were not actually
open looking to open an office,
where should we open the next
rack or set of reqs to be able
to take it manage of local power
dynamics.
These are questions are US
talent professionals are really
helping to shape the future of
your organizations.
We've talked a lot about how one
of the biggest challenges for
adopting talent analytics was
the accessibility of data
historically.
Getting accurate data in a way
that is actionable and helps you
build data-driven talent
strategies is one of the primary
reasons why we undertook the
effort to build LinkedIn talent
insights.
I'm very excited to share with
you a little bit more about
LinkedIn talent
insights.
LinkedIn talent insights is a
new self-service talent
analytics product that gives you
and your team access to
real-time data and insights on
talent pools and companies
across the world.
LinkedIn has half a billion
members and LinkedIn talent
insights is powered by the
millions of real-time actions
are members take every day so in
effect, it gives a full view
into the professional workforce
across the world.
The talent pools report lets you
see instantly trends and
movement across the talent
marketplace with access to
real-time supply and demanded of
talent.
You can see, for example, the
total number of software
engineers in New York or where
there might be a hidden gem city
where demand is low and supply
is high for the talent you might
need more or where people are
migrating to and from.
The company insights report
gives you a real-time look at
who you are gaining talent from
and losing talent to and who
your competitors are gaining
talent from and losing talent
to.
This helps you build a deeper
knowledge on your priority
markets and helps you understand
how your workforce compares to
your competitors so that you can
plan for the talent you need
today in the talent you need for
the future.
With LinkedIn talent insights,
you can bring actionable
insights to the table and
conversations with your
leadership team that CEO that
the CHR oh is increasingly
getting influence with and
advise on key business decisions
with reliable, validated talent
data.
But the product is really better
understood by being able to see
it so here's a screenshot of the
talent pool report in the
LinkedIn talent insights
product.
This allows you to dive into
everything from where talent is
going to the level of hiring
demand at really any level of
granularity that makes sense for
your business.
You can also make your focus
even deeper by adding specific
skills to the job titles you're
looking for in the left-hand
column and therefore dynamically
be able to see how the talent
characteristics are changing.
So how are people using the data
from LinkedIn Talent Insights?
Five key use cases that we're
sing so far from customers,
realistically the range for tell
insights go well beyond these 5.
Based on our customers today,
these top use cases are first,
hiring strategy, which is using
talent insights to inform your
recruiting strategy and set
expectations with hiring
managers based on dynamics the
talent pool.
The second is employer branding
which is using talent insights
to identify target audiences,
create campaigns, and allocate
branding budget in a way that's
tailored to the talent pools
that you most need to attract.
3rd is competitive intelligence.
This is something I'm really
excited about because talent
insights gives unprecedented
visibility into pure companies
and industry leaders and how
talent is shifting across
different companies.
4th is geolocation decisions.
This means using talent insights
to understand how the talent
market and competition vary by
region to support location
decisions.
Anything from that next new
headquarters all the way down to
how to target particular
requisition.
And the fix is workforce
planning, using talent insights
to create data-driven plans for
talent acquisition, development
and retention because often we
find when the talent pool is
incredibly hard to acquire, that
mainly mean that you need to
change what you're hiring for
and that actually has
implications on the development,
retention, and other
expectations of the talent pool
so being able to understand all
of that in concert can help the
workforce plan, but as I
mentioned before, the talent
insights product is best shown
by being able to see a live, so
I will now attempt to do a live
demo of the product.
>> And while you're firing up,
Sarah, I had the good fortune of
being in Sydney and Paris last
year for the LinkedIn talent
intelligent experience and I
spoke to some of the pilot
customers and it's a powerful
thing, talking to some of the
heads of talent acquisition,
their companies have made
different decisions about where
they were going to locate a new
team based on the inside of this
product.
>> Completely agree and I'll
actually show you two of those,
so first use case we'll shares
hiring strategy.
Auto was an early customer of
talent insights.
Jennifer, as senior engineering
recruiter was tasked with
finding candidates that had the
11 must-have skills the hiring
manager
needed.
She knew this would be tough and
she wanted to set expectations
with the hiring manager so she
started out with our talent pool
report to understand the talent
pool for senior software
engineers in San Francisco, so
it started out by saying she
wanted senior software engineers
in the San Francisco Bay Area
and you can see appear the
number of professional updates
dynamically so she can see
there's 27,000 senior software
engineers in San Francisco but
as I mentioned before, her
hiring manager had very specific
skill requirements so Jennifer
went and started including the
different skills that needed to
be part of this wreck, Java,
C++, and there are many more
here.
>> And I think again, it's so
easy to use, even I could use
it.
This quite a lot to be honest
with you.
>> Yeah.
You know, I think back to the
clicks not code.
So Jennifer typed in the skills
that she required for this
particular search, and she
wanted someone who had all of
these skills, click apply, and
you see the talent pool that had
been 27,000 professionals is now
only 413 which immediately gave
Jennifer pause and made her
think, wow, this may be a really
hard requisition to field bill.
Not only that she noticed that
the hiring demand for this
talent pool which is based on a
lot of signals from her platform
is very high so she realized
that this would also be not only
there were not that many of
them, they are quite difficult
to gauge and they have a fairly
short tenure which means they
may not stay very long once they
are there.
But she wanted to understand the
landscape to get a feel for how
successful she might be in
attacking this talent pool, so
if I click on the company tab,
you can see which employers are
hiring this talent pool in which
employers have pools of these
individuals already so she
looked at the top employers.
If you look here, Apple actually
has a reasonably high attrition
rate for this particular talent
pool, so she decided to look at
her competitiveness against some
of these employers so if I go
back to the talent insights
product, dig into the company
report this time and look a
Autodesk, you can see the number
of professionals at Autodesk,
generate the report and you can
see right here the distribution
of Autodesk's workforce and more
importantly, where they are
winning talent from and who they
are losing talent to so if I
click into that to drill down a
little bit more you can see that
Autodesk loses 2.3 times as many
professionals to Apple as they
gain so this gave Jennifer some
quantitative information to be
able to go back to the hiring
manager and have a conversation
about the hiring manager about
how to relax some of the
criteria from the required
skills in order to get to a
talent pool that felt a little
bit more reasonable to
hire.
This approach has been so
successful that Autodesk has
actually rolled this out across
a much wider set of their
recruiters and it's become a
common part of their intake
process with their recruiters.
So let me also give an example
of an employer branding loot use
case because as we talked about
earlier, that's an area that we
fill is emerging for data.
This case I'm going to speak a
little bit about Intel.
Intel was also an early customer
of talent insights.
They were struggling to hire
software engineers in Poland
where they had a big facility in
Europe and they wanted to run an
employer branding campaign but
didn't want this bread their
budget too thin and wanted to
get the maximum return on
investment for their employer
branding campaign so they
started out by trying to
understand the market.
So they look for software
engineers in Gdansk, it turns
out that my spelling may not be
perfect out
>> you can see here there's 2000
professionals, software
engineers and Gdansk and if you
look at the companies that are
employing, they are already the
largest employer of software
engineers and Gdansk and largest
by far so realistically their
brand is probably already
reasonably strong in Gdansk so
this prompted if they may be
better off by putting their
employer branding campaign
elsewhere in Poland so if you
widen the criteria to go all of
Poland rather than just Gdansk
and look at the locations you
can see here that while there
are 2000 professionals and
Gdansk with Intel is the largest
employer, there are almost over
twice as many in both Kraków and
Warsaw so this enabled Intel to
then drill down on both of these
locations, so they started with
Warsaw, include Warsaw, exclude
all of Poland and when they
drill down into Warsaw, they saw
there were a couple of employers
with over 100 software engineers
and then after that, then the
pool becomes pretty small and
spread out.
They also looked at Kraków and
did a comparison and found in
Kraków, there were actually
about 10 employers who had the
bulk of the talent was
concentrated NEC here to know
employers that have over 100
software engineers and so what
Intel actually did with this
information is they put physical
billboards outside the locations
of a few of these employers
where they felt like they would
have a strong chance of being
able to hire software engineers
and after they did that, they
saw a 20% increase in visits to
Intel's career career pages so
what I love about this is it's
taking access to talent data to
then use for an analog campaign
that then sought digital
conversion and I love the
creativity of this and I love
the results story and able to
get real hard quantitative ROI
out of this.
>> It's a powerful story, very
powerful and both great use
cases that demonstrate how data
and insights can be used across
a variety of HR areas.
They know that you you showed
that her talent acquisitions
one, obviously after thing
placed ninth but I don't know if
you want to say anything about
some of the use cases they've
got.
>> Yeah, the use cases that I
shared were hiring strategy and
employer branding but you can
see that we drew on competitive
intelligence for both of those
decisions and really it's that
deep knowledge of which
companies are hiring and that
competitive talent load that
underpin everything and you can
see that Intel's path to make
the decision on employer brand,
it informed their strategy of
the Gdansk location and so if
you think about the larger world
in which Intel is thinking about
its geolocation strategy that
the possibilities are truly
endless and then finally on the
workforce planning, the Autodesk
example just shared the narrow
slice of setting expectations
with the hiring manager but if
you take that a few steps
forward, if Jennifer then were
to completely change and relax
the criteria for hiring that
would have implications on which
skills her, the employees would
need to learn on the job versus
which ones they would be
expected to come in with and
that might actually impact the
workforce plan of what types of
skills you bring folks in with,
what you develop as you go see
you can really see even with
these two use cases which were
hiring strategy an employee
branding, the full range and I
guess one more thing I'll
mention it even though the two
examples I shared were both
different flavors of tech and
software engineering, in
reality, we are seeing companies
of every industry, every
teletype start to use talent
insights to drive talent
decisions.
>> I certainly wish I had it
when I was a recruiter back in
the late 1990s.
>> Five clicks to an answer.
>> That would be fantastic.
>> So thank you very much
everyone.
I'm looking at the screen
because I'm looking at some of
the questions that you've been
asking as Sarah and I have been
talking so firstly, thank you
very much for the questions that
we're going to spend some time
and try to answer some of those
now.
Sarah, I'm going to address this
one to you and I can probably
add a little bit on as well.
In terms of when we talked about
how you can check the team, how
do you separate the reporting
from analytics within a people
analytics, HR and lytic steam?
>> I mean, I think honestly it
comes back to the scale of the
team that you have, and I think
because these skill sets tend to
be somewhat different for
managing reporting and the tools
and programs elements versus the
research and analytics, in the
larger teams we do tend to see
these as separate disciplines,
you know, sometimes managed by
different individuals, but in
those cases then we tend to see
a continual feedback loop
betweena continual feedback loop
between the 2.
I think the challenge that
companies have is as they start
to get more specialized skill
sets across each of those they
use the connectivity of making
sure that the research is being
done in a way that can
ultimately scale and so I think
having the folks that are
running their reporting and
scalable tools as part of the
conversation as soon as some of
the research starts to hit the
tipping point of potential being
ready for scale that will help
help maximize the chance that
research that's being done can
then translate into scalable
tools down the road.
>> Yeah, and have certainly seen
companies actually take
reporting out of the analytics
team and put on another team or
others have it as a distinct
function within the team as well
thought I don't think there's
any right or wrong answer but I
think part of the education
process to go out to the
business into go out to the
wider HR community is reporting
and analytics are difference,
yes, data is a key component of
book but they are different, so
you just need to be very clear
in terms of what the team does
and the difference between the
two I think, when you are
communicating.
So lots of great questions
coming in.
This is an interesting one.
So what advice do you have for a
recruiter trying to get more
resourcing, solicit every
product like this and they've
other things, fewer head of
talent acquisition, how do you
get more resources around
analytics?
>> So this may sound like a
tongue-in-cheek answer, but I
would actually use talent
insights, we actually have seen
it, a use case can be looking at
using the data in the platform
to understand the magnitude of
the growth, the skill sets that
are required and then being able
to use that to then compare
against what your team has today
so I think that's 1.
I think second is focusing on
one or two really critical
business needs that aren't able
to be fully answered without a
investment in analytics and so
rallying around the couple of
business needs that will truly
make the case can help executive
leadership make the decision.
>> I think that's the key thing,
actually focusing on the
business questions, finding some
insights on those to get actual
insights and then having an
impact and then get analytics,
but I've seen organizations as a
famous case study for emerging
media around employer brand,
they felt they were having such
a bad candidate experience that
it was actually impacting all
their customer loyalty it would
actually move to a competitor
organization because they had
such about expense and they
actually went to the consumer
analytics team and leveraged
their data in the recruiting
data and found that they did
have a problem and it was
costing them somewhere around $6
million a year because so many
people were going to the
recruitment process and having
such a bad experience they were
leaving within four weeks and
moving to one of the competitor
organization so that insight
help them refined their
experience and allowed them to
provide such a great process
that actually noncustomers
wanted to sign up to become
customers as well and as you
said, I focused on the rate
business question in the reports
of the business and had impact.
I suppose back to the typical
size of people in lytic steam in
a company, how big is LinkedIn
steam?
>> LinkedIn steam is actually
fairly lean, by 15 or 20 folks,
and that's intentional because
the goal is to tackle a couple
of business challenges and then
scale throughout the business,
but you know we see teams of all
shapes and sizes depending on
the complexity of the workforce,
how globally distributed it is
in the nature of what that team
takes on versus the rest of the
organization.
LinkedIn as a whole is quite
data-driven so I think the
people analytics team benefits
from having an organization that
is able to self serve a lot as
well.
>> It also depends on what's in
it because some of the bigger
organizations have abortive
hundred people, some teams are
doubling in size, tripling in
size in one example, capital one
I think is tripled their team
over two years, 25 to 75 people.
We're just spotted time, we
could probably continue with
these questions all day so we
just want to close with three
key takeaways that can help you
be a people analytics champion
within your own organization.
If you have not heard already,
really focus on if you are
getting started out, just
identify what are the key
business challenges your
organization faces and actually
what are the data behind that's
answer that.
The second one really is
analytics is a team sport.
When you are getting started if
you can leverage resources, data
scientists perhaps, data
specialist from elsewhere in
their organization like
marketing and data, like
emerging media example that I
gave there from outside HR, that
can really help you have an
impact as well and then I think
the third one, and I would say
this is important for even when
you're quite well developed,
have a business sponsor for what
you are working on, a business
sponsor that actually wants the
insights from this particular
project and is prepared to
actually action those insights
as well.
Sponsorship is very important as
well.
So Sarah, it's been a pleasure
to spend time with you.
Any final words from you?
>> You know, I think we are just
at the outset of a new era of
being able to understand all of
use cases that companies will
come up with and so while we
shared some tidbits today about
what it takes to develop the
capability and use cases, I
think we come back and do this
five years from now, we will be
ended completely different
spacing companies will innovate
in ways that we cannot even
imagine today, so I'm excited to
see what you all do with this
information and where you take
your businesses going forward.
>> And thank you very much for
joining us today.
