Good afternoon,
everybody, and welcome
to the session on fueling
agentic engagement
with Formula One.
My name is Jacob
Hickick, and I lead
Agent Force 360 platform
and Data 360 product
marketing for
Asia Pacific out of
my hometown of Sydney,
Australia. And I'm
thrilled to be here
with you all from
the land down under.
Now, we have a lot of
content to get through.
So before we get into
it, I need to show
you this love letter
from our lawyers.
Please be sure to make
all purchasing decisions
based off publicly
available products and
generally available
information as well.
Now, I want to start
by saying thank you,
because there are
literally hundreds and
thousands of sessions
that you could be at
right now, and you've
chosen to be here
with us. And the
Formula One story is an
incredibly powerful
one, and we have a lot
to share with you. So,
I want to make sure
this is as valuable
as we possibly can
make it. So, who's
ready for this. Give me
a little bit of energy,
guys. The more we
get, the better it
is for me. Thank you.
And for those
viewing on Salesforce
Plus, we're
really excited to
have you here
with us as well.
Now, if we take
a step back,
every customer touchpoint
is expected to
be personal.
Now, this is something
that we've seen with
the consumer experiences
today when we
think about all the
transformation that's
happened there. But
when we consider what it
means for a business
context, this is the
customer expectations
that is required of
all the organizations
that we're a part of.
But personalization at
scale remains out of
reach for many of the
businesses that we work
with. And if we have
a look at where we
are today and where we
want to be, these are
the challenges that we
have in front of us.
Over 70% of
organizations have tools
that are disconnected
and not working
together, which means
that they're having a
swivel chair between
all these different
applications. They've
got trapped data,
both in structured
and unstructured.
Poor governance means
that you're having
data that's flowing
out to systems that
otherwise shouldn't
be. And you've got
integrations that
aren't working to the
best efficiency of
the organization and
the outcomes that
you're trying to drive.
And like I said,
this is the
reality for the
businesses today.
We have disconnected
applications
across every single
experience that you
and your organization
are trying to
work through, but
also your customers
as they try to work
with you as well.
And so when we think
about what that
means for digital
labor and human labor,
let's actually walk
through this now.
Your digital labor
is going to become
that brand relationship
extension. So
it's going to be
that proactive
assistant that is going
to be able to help
be powered by real
-time insights to
help your business
achieve its goals,
which then frees up
your human teams to
focus on developing
the relationships.
And so at the end
of the day, this all
comes down to how
they can build trust.
And together,
this is how we're
going to be able to drive
that personalization
at scale.
Now, a brand that is
doing this incredibly
well and they
are well on their
journey is Formula One.
Now, we're incredibly
lucky to have Claire
Sparks, the head
of technology
initiatives at Formula
One, here with us
today. So can you all
please help me in
welcoming her to the
stage to tell us a
bit about this story?
So, Claire, thanks so
much for being with
you. I'm going to hand
it over to you and I'll
be back a little bit
later. Thank you,
Jacob. Good afternoon,
everyone. on. My role at
Formula One is oversight
across all of our
technology initiatives.
So whether that's
our traditional
corporate IT projects, or
whether that's innovation
and R&D projects that
we do with our technology
partners, I bring
to the table all of
our F1 skills and
expertise and make sure
that we have the right
resources committed to
our projects to make
them a success for us
and for our partners.
So before I get into
the detail of the
how and the what of
what we've done at
Formula One, I think
it's important to share
with you some context
around our data
landscape. Data you'll
hear is the foundation
of everything that
we've done so far.
So our fan database
is phenomenally huge.
We have 827 million
fans and 107 million
social media
followers globally. A
huge amount of data
in our fan database.
When we started our
journey in this space,
we had less than we
have now, but we knew
that the potential
was just going to grow
and grow and grow for
us. And so when we
think about Formula
One, Formula One is
75 years old, but our
data journey is still
relatively new. We
really started our
focus on data in 2018.
So in the overall
lifespan of Formula
One, it's a fairly short
space of time over
the past seven years.
With that volume of
fans comes a much
more diverse fan
base. And what they're
interested in knowing
about our sport is
incredibly different
for each demographic,
each grouping within
our fan audience.
So we now have 42%
of our fans who are
female. This is a huge
difference from where
we were five years
ago. The past five
years have been an
incredible time of not
just growth, but also
change and diversity
in our fan base. And
43% of our fans are
under 35. So we have
a fan base that's
younger. It's a different
gender compared with
our traditional male
over 45 cohort from
traditional motorsport
and from our
history. So we're growing
and we're changing.
And with that size
of fan base comes a
huge amount of
responsibility to interact
with them in a way
that is right for them.
So as I mentioned,
our journey with
Salesforce began,
again, relatively
recently in our
history. In 2022, we
began working with
Salesforce and we
needed a solution
that was scalable,
that could meet
the multi-channel
ambitions that we had
for talking to our
fans across those
diverse interests, but
also diverse platforms.
Where are our
fans hanging out?
And we knew they
weren't just in one
place anymore. We
needed to talk to
them where they were.
So meet with what we
needed now, manage
all of our data,
help us structure and
segment our data,
but also be able to
help us enable the
personalization that
we were looking to
achieve and to be able
to drive that multi
-channel approach.
We started relatively
small. So we have
a membership service,
which is called
F1 Unlocked. It was
relatively new at
the time. So we
started with our known
fan base in our F1
Unlocked audience.
And we took the
journeys that we were
working on for them. We
wanted to personalize.
We wanted to have
a multi-channel
journey, and we
wanted to do that in a
way that was using their
inferred preferences,
but also their
stated preferences.
And we took that from
different sources.
Their stated preferences,
we asked them that
when they signed up. Their
inferred preferences,
we used the behavioral
data that we
were getting from our
previous interactions.
And using Salesforce,
we determined the
personalized content.
We changed up the
channel mix. If
engagement was low on
a particular channel,
we would shift to
a different channel.
And we would also
look at replacing
content with look-at
-like content, again,
based on those
preferences that our
fans were telling us.
The results
were incredible.
We started off
with that test
and learn, started small.
But that first
campaign that we did,
we increased our
click-to-open rates
by 17%. So incredibly
positive for
us, and we knew
that that was really
a stepping stone
to take us forward.
Fast forward to more
recently, we shifted
our focus to our F1 TV
subscription audience.
So our F1 TV
subscribers, our customer
service center for
our F1 TV subscribers,
handles many, many
inbound queries on a
daily basis. As you
can imagine, over race
weekends when we're
broadcasting, so F1 TV
is our broadcast
channel, when we're
broadcasting, we have
numerous queries that
could come in. So if
you think about as
you're watching something
through a streaming
platform, you might
need to reset your
password. You might
need to update your
account details,
payment details, so on.
So our customer services
department could
handle any nature of
those queries along
with technical support
or product support.
We started with an
agent that could
deal with the very
simple queries. So
where perhaps a fan
wanted their contact
details updated or
an address updated,
very, very simple
queries that could
be done very easily
by a digital agent.
And then that moved
the demand from our
human workforce so
that they could really
work on the higher
value conversations
that they were
having with our fans.
And the results there
really speak for
themselves. So, just by
starting small with that
one use case, we reduced
our chat handling
time by 50%. Our total
call center agent time
went down by 25%.
And when we looked at
that collectively
across our whole contact
center, we were reducing
around 15%, 16% on our
call center costs.
So, again, incredibly
positive experience from
our first toe in the
water when it comes
to agentic engagement.
What have we learned?
you probably heard
this a lot, but I talk
about this a lot as
well. Data quality is
key. It takes time,
effort, hard work,
blood, sweat, and
tears, but it honestly
is worth it. The more
that you put into
getting your data
right, robust, ready,
the better the agentic
experience your
customers will have.
The other thing that
we learned very quickly
was to keep, in our
case, the fan, your
customer as your focus.
It can be really tempting
to approach this from
a how could we save
time as an organization
or how could we what's
what's more important
to us. But there's
nothing more important
to us than our fans.
And as I mentioned,
that responsibility to
give them the best
possible service and the
best possible experience
with Formula One.
So we kept the fan as
our focus. We looked
through the lens of
the fan and we made
sure that we were always
approaching what we
were trying to do with
their view in mind.
The third key thing
that we learned was
to really trust
the feedback. So I
mentioned earlier that
behavioral data is
absolutely critical
in what you do next.
So sometimes we can
enter these test and
learn scenarios with an
assumption around what
the outcome is going
to be, that internal
bias, and then we ignore
the behavioral data
that we get back from
that test and learn.
It almost negates
the reason for doing
the test and learn
in the first place.
So really important to
trust the behavioral
data, even if
it's not what you
thought it was going
to be. It is what your
fans and customers
are telling you.
So really important
to trust that and then
take that as your
next step forward.
you know, having
been here now for a
couple of days, I'm
sure you'll agree
the possibilities
here are endless.
We are wanting to get
the right balance,
the right mix
between our digital
workforce and our
human workforce. We
want our humans to
be working to have
those real valuable
conversations with
our fans and being
that real white glove
treatment for our
fan touch points.
And to do that, we're
looking at how we can
use autonomous agents
to support that. So
using the autonomous
agents for, again, those,
maybe the technical
tasks or the product
-based tasks. So our human
workforce can really
focus on those value
-added conversations.
The future is so
exciting, and I
can't wait to see
where we go next.
So I think we can
all agree that that
was just packed with
insights. and thank
you for sharing
your journey with us
and thanks again for
being here today.
So I've got a couple of
questions that I know
everyone in the room
is probably curious
for us to dig a little
bit deeper into your
story. So to start off
is, how did you know
the Salesforce's solutions
was right for both
those short-term and
long-term goals? Yeah.
with the volume of
data that we had,
we knew that it
was only going
to get bigger.
We had plans
for new products
at the time,
Strive to Survive was
in its infancy, F1
movie coming up.
we knew that we had
incredibly ambitious
growth plans. So on
that basis, that big
data set was only going
to get bigger. And we
knew that Salesforce
had the solution
to scale with us.
Yeah. Fantastic.
Thank you. And just on
the data piece. So
you mentioned how
important Data360 was
for F1. So how did
you set up your data
infrastructure for
success? Because you
pulled it out there
a couple of times.
Yeah, absolutely.
I would say in the
main, we took two
core approaches. This
was our foundations.
Again, we'll talk a
lot about foundations.
They're so important
not to skip.
Across our data landscape,
we have a number
of owned and operated
products where we
are responsible for the
incoming fan data. We
also have a number
of third-party data
sources where we get
our data from, where we
are ingesting other
people's data. So for
us, we started with
our owned and operated
estate. We made sure
the data entry points
were standardized in
terms of validation for
any fan data that
was coming in so that
there wasn't discrepancies
or differences in
formatting. And that
was at the point of
entry where our fans
were giving us their
information and sharing
their details with us.
The other thing that
we did across the
third-party data sets
was around looking at
the data and where we
could standardizing
as much as possible.
So whether that was in
formatting, whether
that was in country,
you know, different
names for different
countries, whether that
was in date formats,
we standardized across
that. And so that
combination of entry
for our own estate
and standardization
for key fields across
our third party data
were the two key things
that we did to set
those foundations really
well. I love that.
And now that you've
done that, it's going
to stay clean forever
and you never have
to touch it again,
right? Absolutely.
That's the magic, right?
Now, you mentioned
before about all the
possibilities and how
they really are endless.
And so how did you
actually go through and
pick that first use
case for agent force
360 like what was that
first thing that you
were doing like did
you use a framework or
a workshop to be able
to get there yeah so
for us it was about
the responsibility of
our brand to our fans
so f1 global name huge
brand huge responsibilities
and we wanted
to make sure we were
giving our fans the
best possible experience
of their interaction
with us so when you
think across that 827
million fans only one
percent of our fans
are ever likely to
attend a race so the
potential touch points
for other fans is
relatively it's completely
different they're not
at track and so we
wanted to make sure that
within their
interactions with us that
were away from the
track that they just had
the best possible
experience and that's that
fan-centric approach
that i talked about
so we approached it
through that we focused
on our owned and operated
product which was
f1 tv it was fully
within our control so
we put up the guard
rails, we set our
priorities, and we moved
forwards from there. I
love that. Thanks for
sharing. Now, this is
your first Dreamforce.
It is. And we are
now nearing the end of
day two. So what are
some of the innovations
that you've been
excited to take back
to the business so far?
Gosh, there's almost
too many to talk
about. And one of the
things that I love
to hear is how other
organizations have
done it. You get
sort of a bit of an
insight into how other
organizations work.
So I spent a couple
of sessions at the
Salesforce on Salesforce
theater, which is
great, by the way,
this is how Salesforce
have done it for
their organization.
And so looking
across that, that's
something, everything
that's been
shared in those
sessions has just been
really interesting
and insightful.
The other thing for me
that I think is we're
very much at the start
of our journey with
agentic AI, as a lot
of organizations are.
So it feels like the
path ahead of us has many
paths spanning off it.
The inspiration for
me, though, is around
that agentic enterprise.
We're an incredibly
lean organization.
People are often
surprised when I tell them
we have around 650, 700
employees that do what
we do on a global
scale, 24 races a year,
21 countries, and we
move around the globe.
So with that
lean workforce,
using agents to really
elevate our teams,
to enable them to do
the real skilled work
rather than the more
mundane, repetitive
work is a really great
way to just drive
growth and efficiencies
within our organization.
Yeah, absolutely.
And I know I was
surprised when I found
out how many people we
had. It's probably
any, right? Yeah. So we
are getting to the end
of our session. And
I really want to ask
you one thing here.
So you mentioned some
of the key takeaways
earlier as the data
quality is key. Keep
the fan as your focus
and trust the feedback,
which these are all
takeaways that I think
everyone here should
be taking back to
their business. But
what I would love for
you to share before we
close out is what was
one thing that you
would like to change
about your Agent Force 360
platform implementation
journey? What would
that be? Yeah.
The thing for me that
I would take away from
this is that when
we work with our non
-technical teams, so my
role is a technical one.
When we work with our
non-technical teams,
there is often a
level of skepticism or
concern around AI. Is
it going to replace my
job? What is it going
to do differently? How
am I going to be impacted
by this? Is it just
a shiny tech project
over there and I don't
really get it? And I
think as organizations
grow and as organizations
move forward at
this time in history
with technology as it
is, it's really
important to remember the
human impact. We are
all looking for human
connections. We are all
looking to how can we
work together better,
more collaboratively.
And I think one of the
things I would absolutely
change if I could
go back to zero and
start again would be to
really build the trust
and credibility with
our non-technical
teams from day one so
that they see what's
happening in the tech
world, in the AI world as
a real positive way to
support them, to help
make their day-to-day
easier, to help them
do the better tasks
and not the ones that
they find incredibly
mundane or repetitive.
but to really let them
focus on where they
can add value because
when they add value,
they're happier in
their work day to day.
So for me, it would
really be that, Jacob,
bringing the non
-techie teams or just
bringing the workforce
along on the journey.
Start small, build
credibility, build
trust in the tech and
prove to them how it
can really help them,
what's in it for
them. I love that.
And so really it comes
down to that empathy
and also what we talked
about right at the
start, which is
together with digital
labor and human labor,
how they can work
together to deliver
personalization at
scale. Absolutely.
Claire, thank you
so much. Can we all
give a hand for Claire
for coming out and
sharing all this with
us? Thank you. Thank
you, everyone. Thanks
so much, Claire.
Now, I know that
this race is over and
you're about to go
race or somewhere
else, but to make sure
you have the resources
to continue this
learning journey,
if you want to go
deeper on the Formula
One journey, you
can see them in
another session
happening tomorrow
at 9.45 a.m. in the
Metroton Theatre.
And you can also view the
Now, if you want to
get these slides and
all the slides across
the Agent Force
track, Please scan
the QR code, join the
Agent Blazer community
there, or visit us
on the Community
Cove. Thank you,
everybody, and enjoy
the rest of Dreamforce.