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When it comes
to customers,
there can be
nothing artificial
about the experience.
It's absolutely
critical for us
to be able to connect the
dots from a marketing CRM
perspective.
Data allows you to
understand the needs
of your customers.
Our customers
are saying, wow.
They actually understand
who I am and what I want.
So with CRM, AI,
data, and trust,
we can create an
exceptionally personalized
experience for
our customers.
We had a lot of
our customer data
and information in
different places.
Bringing all of
this together
will take the power
of Data Cloud.
We had over 30
different systems
that had customer
info and order info.
Using AI and Einstein
has given us the ability
to really take on
that single source
of the truth.
I needed a trusted
source to secure my data.
And Data Cloud was
the perfect product
to take that
journey with me.
Data Cloud becomes
that central hub
where all of that
data flows in.
And it helps create
this dimensional view
of our donors.
With AI, with data, with
the latest innovations,
Marketing Cloud is
really the engine
that powers the
customer experiences
we want to deliver.
So this is
Marketing Cloud,
where we're able to
look at customers
who perhaps have got an
interest within fashion.
And we're able to then
tailor the push messages
all to do with fashion.
The insights
enables us to build
specific, targeted
campaigns.
Thanks to
Salesforce commerce,
we're able to deliver
real-time information,
automation capability to
make our customer happy.
Salesforce
Einstein 1 commerce
is the backbone of our
purchase functionality.
It is all about
having really this one
landscape of
data to derive
a customer-first
experience.
CRM with AI and data
powered by trust.
That's the way
forward for us.
Please welcome president
and CMO, Salesforce, Ariel
Kelman.
[APPLAUSE]
Hello.
Hello, Chicago.
Welcome, everyone,
to Connections 2024.
This is our
annual conference
for marketing and
commerce professionals.
I am thrilled to
be here today.
We've got a lot
of information
to cover, a
lot of content.
Just to kind of ground
you on everything
that's subject to
today's presentation,
welcome to the
AI enterprise.
We're going to talk
about how AI is changing
the way that we work
and how you can harness
this technology.
But first, I want
to say thank you.
Thank you to our
customers, our partners,
our trailblazers.
Thank you to our MVPs.
Do we have MVPs here?
[CHEERING]
These are the secret
weapons of Salesforce
deployments.
If you don't have
one, I encourage you
find one or produce one.
We're producing new
MVPs all the time.
OK.
Since we started
the company in 1999,
we've been focused on
becoming the number one
CRM provider.
And we've grown
over the years.
And this year,
we're projected
to reach $38 billion.
And we're proud of that,
but we're also proud
of the innovation
that we've
delivered to customers
and for being recognized
as one of the
leading philanthropic
and ethical companies
in the world.
And a lot of this is
because we're really
grounded in a set
of core values.
This is a
value-driven company
and always has been.
At the heart of
that is trust.
You trust us to
make you successful.
You trust us to help
you make your customers
successful.
And now more
than ever, you
trust us with your data.
And we're going to
talk a lot about that
because in the
world of AI,
data is absolutely
important.
It's the key to
success with AI,
but it needs to be
done in a trusted way.
You also trust
us to innovate,
and to enhance
our products,
and to build our
products in a way that
is both equitable
and sustainable.
So philanthropy
has really been
in the heart of
this company's DNA
for a long time.
As many of you know, we
created this 111 model
that's been replicated
by thousands of companies
around the world,
especially startups, where
when we start the company,
we put 1% of our equity,
1% of our employees' time,
and 1% of our product
into a foundation.
And the results have
really been amazing.
And I thank all of you
who've participated
in these programs.
We've donated
over $700 million.
Our employees have
volunteered over
nine million hours
of their time.
And we have almost 60,000
nonprofits and educational
institutions running
for free on Salesforce.
And one nonprofit
I like to call out
is Big Brothers
and Big Sisters,
one of my favorite
nonprofits.
[APPLAUSE]
Thank you for
everything you do.
Now, they are actually
in our campground today,
so I encourage you to
visit them and learn more
about their
amazing programs.
So when we started
the company,
the vision for the
product was really
to help companies
connect with customers
in a whole new way.
And I thought we'd bring
up a customer early
in the presentation
to just ground us
in how customers are
really using our products.
And so the
company that we're
going to bring up
today is a company
called Turtle Bay Resort.
They have a resort in
the North shore of Oahu,
a wonderful place.
And they, like many of us,
struggle with challenges
like disconnected
data, trying
to bridge different
teams to be
able to work together and
deliver a great customer
experience.
And of course, in not
very surprising news,
they've turned to
Salesforce to help them.
So I'm really honored to
have with us today Robert
Marusi the chief
commercial officer
from Turtle Bay.
Robert, why don't
you come up here?
[APPLAUSE]
Thank you, Robert.
Aloha.
Aloha.
So why don't you
tell us about--
maybe we start with--
tell us a little bit
about some of the problems
and challenges you've
had with your goal
of delivering a
really great customer
experience to everyone
who visits your resort.
Yeah.
I mean, early on, we
did quite a renovation
and a re-imagination
in this resort.
And I had the
luxury of being
able to change
my distribution
channel, my messaging, my
digital brand experience.
And I knew early
on that I had
to change my tech stack.
My tech stack was
closed loop at best.
It was a lot of point
to point solutions.
We took off the shelf
to accomplish these very
siloed engagements.
And it didn't
speak to anything.
There was no end
to end connection.
There was no view
of the customer.
And I knew early
on that I needed
to take the journey
to get a 360
view of the customer.
Great.
And you've been one of the
early adopters of our Data
Cloud product.
And we're going to
talk a little bit
about Data Cloud today.
So can you tell
us a little bit
about how that plays into
achieving this improved
customer experience?
Yeah.
I mean, most of you know
we're in a choice economy.
And it's about the
client, the consumer,
being in the middle of
this omnichannel spoke.
And it's our
responsibility,
as brand people, to
be delivering choices
to that consumer.
And that's what they want.
That's the way they want
to consume their data
across the omnichannel.
And I knew that
I had to get
into a whole advanced
personalization,
hyperpersonalization,
tactic, and strategy.
And I knew that
I had to be
able to segment my
data based on mosaics
and personas,
speaking to the client
and what their interests
and affinities were.
And Data Cloud became that
very apparent platform
for me to segment my data,
put it in different data
sets, data
spaces, and then
be able to talk
to the consumer
in that hyper
personalized fashion.
Excellent.
Any advice for our friends
here in the audience
on how they can achieve
some of the same results?
How much time do we have.
A couple of hours.
Really, at the
end of the day,
it's, how do you keep
your use case simple?
Because it's a daunting
thing to think about AI.
And what is AI in terms of
how you're digesting it?
Is it GenAI?
Is it AI that lives on
a trusted data model?
What are you trying
to use it for?
And in the MarTech
MarCom space,
it was very easy
for me to get there.
So I kept it simple.
I kept the use cases
very tight and simple
to be able to
really talk across
the crossplatform of
marketing and all that.
So the advice is keep it
simple, really understand.
Get the reps in the game.
Spend the time on
the insight sites,
McKinsey insights,
Gartner insights,
Accenture insights.
And learn about
data and AI
because the conversation
with the c-suite level
leaders today is long,
and it takes a while.
So you're going to be
in an education process.
You're going to be in a
change and transformation
engagement with them.
And really try and
understand it and learn it
every day was really the
biggest takeaway for me
to be able to talk to
that level of leader
and be able to digest
it down in chunks,
so they understood the ROI
and what was happening.
Makes sense.
Start off small.
Maybe pick a couple of
data sources and a couple
ways you want to activate
and implement that first
and learn and grow.
Absolutely.
And get the wins
along the way.
You get immediate
wins along the way.
And that's the key.
How are we going
to make money?
That's the question you
always get as operators.
But if you can prove
that success quickly,
then you're off
to the races.
Excellent.
Great.
Well, thank you for
spending time with us.
Thank you.
Appreciate it.
[APPLAUSE]
OK.
Well, let's talk about AI.
So AI really has
an opportunity
to fundamentally
transform how we work.
And we've been going
through various waves
of AI.
Really, predictive AI
was the first wave.
And we launched
Einstein in 2016.
And I think it's become
fairly commonplace
now to do things
with predictive AI,
like scoring leads,
like coming up
with next best action
for our sales people,
like routing cases.
This is something that's
become very commonplace
and I'd say mainstream.
But now we're
in another wave.
We're in a
generative AI wave,
where the AI is actually
talking to our employees
and where we have
the opportunity
to be able to augment
the capabilities
of our employees, so they
can be more productive
and more effective.
And then after
that, the next wave
that we're just starting
to enter is autonomous.
And this is where the
AI can take action
on its own.
The AI can do work for
us, talk to customers.
And as we head towards
the fourth wave
of artificial
general intelligence,
we got to remember that
at the heart of this,
it's all about effective,
lasting customer
relationships.
So let's talk about
how to get there.
So I want to go
to consumer AI
first because I think we
can learn a lot from what
it can offer, but also
what it can't when
it comes to how we would
use it in the enterprise.
So I'm sure all of us
here have used consumer AI
tools like ChatGPT.
And they feature
three parts.
There's the
user experience
that you interact with.
There's the model,
the large language
model, the LLM, and
there's the data.
And there's a lot of data.
And what's amazing now is
we have tons of choice.
There's many options
at the UI level.
There's many models.
And all of these
have been trained
on a massive
amount of data,
blog posts, videos,
open source databases.
And this is great.
So for example,
if you might
be having a minor
kitchen pantry
disaster of needing
to make chocolate chip
cookies and not having
any eggs or baking powder,
generative AI and
these consumer models,
they'll do a fantastic job
of helping you figure out
how to go make
these cookies
without those
key ingredients.
But when we go to
the enterprise world,
all that data really
isn't that useful.
it doesn't help
the AI give you
the right answers, give
you the right advice,
produce the right
results when
you want to do things like
deliver a better customer
experience.
When you want to have
your employees be more
effective at solving
customer problems,
you need your own data.
You need data about
your customers
and what's
unique about them
to ground the AI to
get the right results.
But you've got to do
that in the right way
and we're going to be
talking about that.
So in addition to being
able to trust the data,
we also have to get
it all in one place.
And all of us, I
think, are struggling
with islands
of trapped data
across the enterprise.
If we think about
the core, a lot of us
have our core customer
data in Salesforce,
but a lot of us
also have tons
of customer data in
different places,
in our data warehouses in
Snowflake, and in our data
lakes in places like
Databricks, and BigQuery,
in our back end
operational systems.
There's data everywhere.
And so this is
a challenge.
So if we're going to be
able to have the ability
to ground AI in
trusted data,
we need to be able to
have access to all of it.
So in addition to being
able to get all the data
together, to unify
it, and to deal
with the siloed
data problem,
there's also a lot
of other questions
that we need to answer
to have AI be effective
for the enterprise.
We have to make sure it
has the right security
and privacy.
We've all spent
a ton of time
setting up the right
security, and data access
rules, and Salesforce so
the right information goes
to the right people.
We don't want to undo
all that by implementing
in the wrong way.
We also don't want
toxic results.
The worst way to make
a customer relationship
is to offend the customer.
We do not want to do
that, so no toxicity
and no hallucinations.
We've seen examples
where AI has made offers
to customers on websites
that don't even exist.
And the courts
have shown we're
really liable
for fulfilling
on those offers.
So you've got to
have the right data.
It's got to be
connected to CRM.
So all of these
concerns need
to be taken into
account in order
to deliver AI for
the enterprise that's
trusted, and really
works, and really delivers
business results.
So this is why we
created Einstein 1.
So Einstein 1 is
the trusted platform
for the AI enterprise.
And what it is
is we've taken
all of our
applications, all
of our AI capabilities.
And with Data Cloud,
we're bringing together
all of the data
that you have
in the enterprise,
the data in Salesforce
and also reaching
out to data outside
of Salesforce.
If you've done the work
to organize your data
in a data warehouse like
Snowflake or Redshift,
great.
We can reach out
to that data.
And it can reside
in those systems.
But you can use
it in Salesforce
just like it was
the standard object
and do all the things
that we'll show you
a little bit more.
And then most
importantly, this
is all built on a
metadata framework
because the AI
needs to understand
about your data.
They need to know
things like accounts
or companies, contacts
or people that
work in those companies,
the cases or problems
that those humans that
our contacts have.
And that kind
of intelligence
needs to be built
into the platform.
And we've built that in.
So not only are we giving
you intelligent, truly
useful AI, because it's
grounded in your data,
it's automated, so
it can take action.
You can implement quickly
because we have low code
and no code for
customization
and deployment and
making changes fast.
But also, it's open.
So we've built
this architecture
to work with any
large language model.
If you've trained your
own custom models,
we can plug it into those.
If you have a third
party provider
you want to work with
when you plug in those.
And we have large language
models of our own.
But being open is an
absolutely key part
of this because we
know that you have
existing investments.
So let's talk a little
bit more about Data Cloud.
So Data Cloud
really is the heart
of modern marketing.
And as a marketer, I
have a MarTech team.
And they use Data Cloud.
And it's really
become the foundation
of how we do marketing
at Salesforce.
So we think
about having all
of these sources of data.
Some of it's
in Salesforce.
Some of it's outside
of Salesforce
in different system,
purchase data, CRM data,
inventory data, data
from our website,
data from our Snowflake
data warehouses.
And we pull all
this together.
And once it's unified
with Data Cloud,
you can do everything
with that data
that you can do with
any standard object.
And so the main three
things that we're doing
is providing visibility,
activating on that data,
and grounding
AI in that data.
And I want to walk you
through each of them,
but they're all
really important.
So let's take
visibility first.
So we have a wide
variety of data.
We have over 80
different data
sources that my
MarTech team has
unified into Data Cloud.
And then in terms
of visibility,
well, we want
to have access
to that data for our
sales and service people.
So for example, if you
click on the account
screen, wouldn't it be
useful to our sales people
to see a summary of
all the engagement
that this customer has
done with our marketing
programs?
Wouldn't it be
great if they
could see the
purchase history,
if they could see the
recommended next best
action from a custom
deep learning model
that we've built
that offers up
to our sales people
some guidance, so we
can bring that
all together
in a very easy way?
So next is taking action.
So one of the
things-- and this
is sort of the classic
customer data platform use
case.
And Data Cloud
is great at it.
So if, for example,
I want to market
one of my favorite
products, Sandbox,
to our customers,
I can start
with a machine
learning built
segment in Data Cloud.
So Data Cloud will help
you create the segments.
And then I can activate it
in a wide variety of ways.
We have a lot
of connectors.
One of my favorites is
LinkedIn because LinkedIn
gives you a platform
where you can target
specific companies
and the people
you know and don't know
in those companies that
have certain roles.
And so with this
Data Cloud connector,
we're able to very
seamlessly run ads
on LinkedIn to the
specific humans
in that campaign--
sorry, in that
segment that
are a great fit
for this product,
offer them up Data Cloud.
And then they
can go and make
a purchase in your account
area of our product.
So we talked
about visibility.
We talked about
activation.
But there's also AI.
And to give you
a sense of why
grounding AI and trusted
data is so important,
I thought I'd kind give
an example, again, go back
to the consumer versus
business use case.
So let's say
that we wanted
to use generative AI to
produce a marketing email.
Now, if we took a consumer
large language model
and we just asked it a
question of, write me
an email inviting
our client,
Lauren to attend an
upcoming conference,
it's going to give us
a pretty basic, pretty
boring, pretty generic
email that's just not
that compelling.
But if instead, we put
in the prompt information
about the customer
that's useful for writing
a good email, just like
what we'd think about
if we were to
write the email
ourselves-- so what type
of company are they at?
What is their role?
What products have
they bought before?
What products have
they been looking
at on our website?
And what do we want
to invite them to?
If we take all
of that, we can
get a much more useful--
I mean, [INAUDIBLE] kind
go to plain language
sometimes-- a much more
useful email that's
likely to be compelling
and get results.
So not only have we been
delivering a lot of AI
functionality,
but we've also
been innovating
across our product
with thousands of
new capabilities.
And I'll call out some of
the marketing and commerce
capabilities that we're
going to be spending time
on in this conference.
So for Data Cloud,
we've delivered
over 200 new connectors to
make it easier than ever
for you to integrate Data
Cloud with all the data
sources that you have.
We've launched today
our Einstein copilot
for our commerce
products, giving
a copilot for merchants,
for buyers, for shoppers.
And then we've
also delivered
several new enhancements
for our composable
storefronts functionality
in our commerce products.
So how do you build
an AI enterprise?
Well, we've broken it
down to five easy steps.
Build a customer 360 to
give you a unified view
of your customers.
Unlock and activate
all of your data,
so you can have unified
data for your activations
and for grounding AI.
And to use that AI, you
need a copilot, but not
just a generic copilot
that doesn't know anything
about your business,
a copilot that's
deeply integrated
to your data,
to your metadata that's
right in the flow of work
to augment the
capabilities
of your employees, so they
can be more productive.
You need an AI
that's built into
and helpful for
your analytics
to help you find the
right insights, what
questions to ask.
And then lastly, AI should
be part of collaboration.
One of the most exciting
things we're going to show
is Slack AI.
And the capabilities
are really outstanding.
I use it every day.
I think you're
going to enjoy that.
But let's start with
the customer 360.
So at its heart,
the customer 360
is really about giving
you the capability
to interact effectively
with your customers
as they touch all of
your different channels
and all of the
different employees.
And we've been innovating
at each of those layers.
So really, it starts
with sales cloud.
And with our
AI sales cloud,
we give you a copilot
right in the flow of work
that understands
what you're
doing that can help you
build and close pipeline
faster than ever and
boost productivity.
So one of the companies
that boosted productivity
was Spotify.
And by integrating their
workflows for sales
directly into
Slack, they were
able to increase
productivity by 40%.
And on this
service side, I
think one of the most
obvious and fastest
adopting areas
for generative AI
is customer service
because in a customer
service context,
we can again
have that copilot
supercharging
the capabilities of
call center agents
to be able to
help customers
solve their problems.
And then also, we can have
the AI talk to customers
directly through our
Einstein chat bots
and solve their cases
quickly and deflect
those cases, so the
agents can spend more time
on higher touch
customer engagements.
And an example
of that is Sonos,
where they were able
to use Einstein chat
bots to deflect
40% of their cases
to self service,
which was a big win
for that company.
And in AI marketing
cloud, our generative AI
capabilities are helping
marketers create segments
and activate on that data,
build campaigns, customize
email templates,
a lot of content
oriented and
segmentation automation.
And one of the
customers that
took advantage of that
is St. Jude's Research
Hospital.
And so by creating highly
personalized campaigns,
they were able to
raise $2 billion
in the past year,
which is truly amazing.
I actually wasn't
sure about that.
I looked it up.
And that is true.
It's truly a phenomenal
fundraising effort.
[APPLAUSE]
And then for
commerce, as we're
going to show
you later, we've
added Einstein Copilot to
our commerce capabilities.
And what it's going to do
is it's helping merchants,
helping commerce
professionals build
their stores, build
their detail pages,
and also copilots to
interact with customers,
so we can help convert
those shopping carts
to revenue
faster than ever.
So we're going to go
into each of these parts.
The next part of
this is Data Cloud.
We're going to show
you more about how
Data Cloud can be the
heart of modern marketing
and commerce.
And with that, I'd
like to turn it over
to Kelly Eliyahu from our
marketing cloud product
marketing team.
[APPLAUSE]
Thanks, Ariel.
So businesses have
been trying to connect
data for over 30 years.
Back in the '90s, data
was managed in on prem
databases, but the
problem was there was no
interoperability.
Then came DMPs.
And these were
great for activating
anonymous customer
profiles for ads.
But the problem was
they couldn't handle
any type of known data.
Then came CDPs.
And these were a
real breakthrough
because they
helped us connect
both known and
unknown customer data
for marketing use cases.
But in today's world,
that isn't enough.
We're missing
opportunities
by not connecting
CRM and CDP.
We're following
customers around with ads
for something they
already bought.
Or we're making them
repeat the conversation
with every single handoff.
Now, I'm a marketer.
And I know that in order
to create those truly
personalized and
connected experiences,
we need more than
just marketing
to have that unified
view of the customer
to be able to
action on that data.
We need marketing, but
also sales, service,
and commerce
as well to have
that data and this
is exactly why we
built Data Cloud.
Data Cloud is more
than just a customer
data platform.
It's a company
data platform.
It's a hyperscale data
engine built directly
into Salesforce that
makes every cloud better.
It helps you unlock
data from any source
and activate that
across any application.
You can do things like
pull in inventory data
to help inform
your campaigns
or bring in web data into
CRM, so that sales knows
exactly when a prospect is
looking at a pricing page
and can take action.
And we're so excited
to announce that we now
have hundreds of
new out of the box
connectors available
for Data Cloud.
Now, these are built
directly natively
into the platform,
so there's
no need for any
custom integrations.
OK.
So how does all this work?
Data Cloud connects
data through connectors.
And these cover
everything from Salesforce
apps to third party data
lakes and warehouses,
all without any
data duping.
And this includes
unstructured data as well.
So think an email or a
PDF knowledge article.
And then all of that
data is harmonized
into one metadata model
and unified customer
profiles.
This can then be
activated in CRM
to power both
actions and insights.
And this is something
Salesforce does better
than anyone in
the industry.
And that's
because Data Cloud
is built on Salesforce's
Einstein 1 platform
and integrated with
our metadata model.
Now, metadata may sound
like an intimidating word,
but it's really not.
It's quite simple.
Metadata is just
context about data.
It's what gives
it meaning.
If I were to show you
five random numbers,
you wouldn't know
without the right context
if I was talking about a
price or something else.
Computers are the same.
They don't know
unless we tell them.
We need metadata
to tell us
that those five random
numbers represent a zip
code.
Metadata translates
that raw data
into contextual
information
that can be used anywhere
inside of Salesforce.
So that can be a lightning
component for displaying
our data within apps.
Or that can be flow
for powering workflows
and automations.
And all of this trusted
data and metadata
is what makes AI effective
in the business context.
So if I was to ask ChatGPT
about, I don't know,
my campaign
performance, I probably
wouldn't get a
very good answer.
And that's because
consumer data, or consumer
AI, we hope, at least,
doesn't know anything
about our campaign
performance.
It's learning from
data on the internet.
Data Cloud allows
you to safely insert
important business
data and context
into every prompt
you create.
And then once that prompt
has been augmented,
we send that
over to the LLMs.
But you don't
need to worry.
None of that data is
stolen or retained
by the model.
We ensure it
remains your data.
OK.
So we've talked about why
we built Data Cloud, how
it works, and
how it makes AI
more effective in
the business context.
Now I want to talk about
what you all actually came
here for, which
is announcements.
So if you haven't
been picking up
what I've been
dropping down,
it's that Data Cloud makes
marketing and commerce
better.
And we've got two
new innovations
to put our money
where our mouth is.
First, we've got Data
Cloud for commerce.
This power is every part
of the commerce experience
with trusted data.
It powers segments.
It powers insights.
It powers journeys.
And second, we've got
Einstein personalization.
And this is built
into Data Cloud
to help you build one to
one customer relationships
through AI decisioning and
real time recommendations.
And both of
these innovations
are available this summer.
And the innovation
doesn't stop there.
We also have our new zero
copy partner network.
Now, this is made
up of partnerships
from major data
platforms like Snowflake
and like Databricks,
but also implementation
ready partners, as
well as third party
partners on
our AppExchange
with AI ready data sets.
But we don't just want
you to take our word
for all of this.
We want you to get started
fast and see value fast.
And we want to bring this
to life through a customer
story.
So Aston Martin is an
iconic automotive brand
known for combining
precision, technology,
and craftsmanship.
And as you can imagine,
personalization
and quality really
matter to Aston Martin.
And that's why they
turned to the Einstein 1
platform, to help
them unify all of that
disconnected data
to power ultra
personalized
customer experiences.
So what I want to
do is bring this
to life through a
short demo, so can you
please help me give a warm
welcome to our amazing
demo team?
They are going
to be driving--
[APPLAUSE]
They'll be
driving live demos
throughout this
entire keynote.
OK.
So what are we
looking at here?
Here is Aston Martin's
view of a customer inside
of Salesforce.
And we're looking at
Abigail's profile here.
So if we look
in the top left,
we've got some of
the basic information
that you would expect.
We know she's
from Chicago.
We've got her email.
We've got her
phone number.
And below that, we
have a little bit
more information as well.
We've got her
past appointments.
And we know that she
purchased her last Aston
Martin in 2021.
But if you look across
the rest of the profile,
there's not really
that much there.
This definitely is
not a complete picture
of everything
Abigail is doing.
And it's not
enough to create
that ultra luxury
experience that Aston
Martin cares about.
To do that, we're going
to need more data.
But I don't want
to just like
ask our demo team to
go and manually pull
that for me.
I think we can do
better than that.
So let's go ahead and
hop into Data Cloud.
All right.
So what we're
looking at here
is data streams
that are pulling
in data into Data Cloud.
And this isn't
a very big list.
We've just got our
one Salesforce org.
So what I want to
do is add more data
to help us understand
things like,
how is our customer
using their car?
Do they need service?
How can we enhance
their experience?
So let's click New and
add more data sources.
So here, we've got a whole
bunch of data sources.
We've got our
Salesforce apps.
We've got third
party data lakes.
And if we keep
scrolling down,
you'll see we
have hundreds
of connectors available
through MuleSoft.
But what I want to
do is add Databricks.
Now, this is where Aston
Martin is collecting
all of their
telemetry data
coming from their
connected cars.
And by bringing this
data into Salesforce,
Aston Martin is able
to increase the ROI
on their investment
in Databricks.
That's because all of that
previously trapped data
will be available
to help them
create that ultra
luxury experience
we talked about.
So let's go ahead
and click Next
and add Databricks.
So remember that
idea of harmonization
I talked about earlier?
Well, this is
that process.
We're connecting the
data in Databricks
to the data model
inside of Salesforce.
Basically, this is
to help us make sure
that the right telemetry
data is associated
to the right customer.
All right.
So this mapping
looks great.
Let's go ahead
and press Save.
Now, while we were
busy harmonizing
all of that data,
our demo team
was rapidly adding
new data streams.
They're the fastest
in the business.
So in addition to
Databricks and that one
Salesforce org, we've
got a whole bunch
of other data here.
We've got our other
Salesforce orgs, as well
as APIs.
But what I want
to do is actually
go back into that customer
view and hit Refresh
because I've got
a lot more data
to work with here now.
Wow.
This is so much more
of a complete view
of our customer.
And this is totally
unique to Salesforce.
So let's check it out.
In addition to that
standard contact
information we have,
below that, we also
have calculated insights.
And this is
just a fancy way
of saying that we're using
the data we just brought
in to help us understand
things like CSAT score
or customer
lifetime value.
We've got our previous
service interactions.
And then in the
middle here, we've
got that telemetry data
that we just pulled in
from Databricks,
so we can see
current usage of the car.
And then we have our
engagement activity feed.
This allows us
to see things
that Abigail is doing.
And it looks
like she might
be showing some interest
in the new DB12.
So again, much more
complete view of Abigail.
But the real value of
data is the ability
to action on it.
And this is something
Salesforce does better
than anyone.
So let's go ahead
and take a look
at what that view
looks like in flow.
This is our workflow
automation tool.
And it helps us
do things like
transform interactions
into journeys.
And what we've got here is
a Data Cloud trigger flow.
This triggers any
time a customer
takes a specific action.
In this case, it's
Abigail showing
interest in the DB12.
And we're going to
show her a invite
to the local DB12
launch event.
So I want to jump over and
see what that experience
looks like for Abigail.
So here, Abigail
is receiving
a personalized
WhatsApp message.
This is her preferred
communication channel.
And it's sent
at the time she
is most likely to engage.
Because this is using
Einstein personalization,
we're able to
use all the data
that we know
about her to make
sure it's the perfect
offer, in this case,
an invite to
that DB12 launch
event at the
Chicago dealership.
And the personalization
continues
across the commerce
experience as well.
So when Abigail goes
to astonmartin.com,
she's immediately
shown a DB12.
Data Cloud for
commerce, again,
is using all of
that information
we know about her
to make sure this
is an ultra personalized
shopping experience.
The DB12 in her
favorite color.
We've got that beautiful
Chicago skyline.
And all of the
configuration
is around maximizing
performance.
And as Abigail configures
and browses more,
all of that data
is captured back
into Data Cloud.
And that's made available
to the dealership.
So when she walks into
that showroom floor,
it's a continued
personalized experience.
It's a connected journey.
All right.
So we want to get you all
started with Data Cloud
today.
That is our goal.
And we've made this
easy in a couple
of different ways.
So we've got our Data
Cloud starter bundle.
This is a bundle of
professional services
and licenses
that will help
you get started with
a common prototype use
case of Data Cloud.
And we've also
got Trailhead.
We've got 10-plus
hours, of courses,
of videos of hands-on
workshops all to help get
you started today.
So I want to see
all the phones out.
Get your phones out.
Take a picture of this
QR code, and get started.
With that, Ariel,
back to you.
Thank you, Kelly.
And one more call out
as we have hands-on labs
experiences here.
So if you want to have
someone walk you through
everything from a
five-minute overview
of getting started with
Data Cloud to a deeper
exercise, it's all
available here with space
for 4,000 people,
so we have a lot.
Well, next up, we're
going to talk deeper.
We gave you a deep dive
on unifying your data
and taking action
with Data Cloud.
We're now we're going
to talk about AI
and show you AI in action
with Einstein Copilot.
And it's my pleasure
to invite up
Michael Affronti, SVP and
GM of Salesforce commerce.
Thanks, man.
Appreciate it.
Good morning.
How are you guys doing?
All right.
This is my favorite
part of this keynote,
not because I'm talking,
but because we're going
to get really geeky.
Are you guys you
guys ready for that?
All right.
Let's do it.
OK.
So as commerce and
marketing leaders,
we know that our
goal is to deliver
deeply personal
relationships, right?
We want to make every
customer interaction feel
like it's the first
time that we've ever
spoken to them,
but we know
everything about them.
But we know that
delivering one
to one personalization
at scale
is really costly
and difficult.
But customers are
expecting us to meet them
where they want to be.
And that's the
real important part
of everything we're
talking about today.
What I'm really
excited to show
you is how at
Salesforce, we're
going to help you
deploy data and AI
to actually deliver
that one to one
personalized experience.
And the way that
we're going to do that
is with our new
copilot experiences.
So our Einstein Copilot
is one digital assistant,
a conversational
digital assistant,
that works across all of
your trusted company data.
The coolest part
about Copilot
is that you can literally
ask it questions,
and it will give
you answers.
It can translate content.
It can write summaries.
It can even
action Salesforce
records for you.
And the thing we've done
is built a single Copilot
experience that works
across every Salesforce
application, which means
you can get started today
and actually learn the
same behaviors across all
of our applications.
And what's
really exciting,
and this is perhaps
my favorite part
of what I get to
talk to you about,
is that we are delivering
new Copilot experiences
today for my two favorite
clouds, marketing
and commerce cloud.
OK.
So for our marketing
cloud customers,
our new copilot
for marketers
is like your digital
campaign assistant.
It helps you write
campaign briefs.
It'll generate content
for you, generate images.
It'll even write entire
emails to your customers.
Now, on the
commerce cloud side,
we decided to give you
two different copilots.
Now, has anyone ever
walked into a store
and had this
incredible experience
with an in-store associate
where they sort of know
everything that
you need, they
can help you find
specific products,
and they also
remember everything
about the last time
you were there?
Generally, yeah?
We've all had
those experiences?
Our copilot for shoppers
is exactly that.
It is a generative AI
assistant that sits on top
of your commerce cloud
storefront and actually
talks to your customers
like a 24/7 in-store
associate.
It's truly incredible.
We've been building
this in pilot
with a handful of
customers over the last 12
months.
And the results have
been phenomenal.
Now, on the
merchant side, we've
built a copilot
for merchants.
And this helps
you do everything
from create new
storefronts,
to write product
descriptions,
and even optimize
your SEO with AI.
And we'll show you
some of that today.
Now, the really great
part about these copilots
is that they fit
into a family
of copilot
experiences that
work across the whole
Salesforce platform.
And this includes our
service cloud, our sales
cloud, even Tableau and
our developer platform.
So as you learn
more about Copilot
and start to get more
familiar with it,
it's ready and
waiting across all
of our applications.
And it's a really special
part about the experience.
Now, there's another
important part
about how you talk
to Copilot, which is
using natural language.
So when you ask
Copilot a question,
it goes behind the
scenes and builds a plan.
Now, a plan consists of
your organizational data.
It consists of your
records, your workflows.
This is how it's going
to answer your question
or your request.
And that action that
it can take for you
are things like sending
an SMS to a customer.
It can update
Salesforce records.
It can even write
summaries and send out
emails.
It's really powerful.
But we also know that not
every company and line
of business is the same.
So you can
easily customize
this whole experience
without writing
a single line of code.
And we're going
to show you that
in just a few minutes.
But I want to talk to
you about something
really important to me.
And that's the safety
of all of your data.
At Salesforce, we've
been protecting your data
for over 25 years.
And that hasn't
changed a bit with AI.
So Ariel and Kelly
talked a bit about this.
And I want to open up our
trust layer a little bit
for you guys.
So we've built what we
call the Einstein Trust
Layer.
This is a single
platform that
sits underneath every
copilot experience you're
going to have.
And its main goal is to
protect your customer
data.
From the second
you enter a prompt,
our data masking and
secure data retrieval
ensure that none of your
raw customer information
is ever passed outside
your organizational
boundary.
And our dynamic
grounding actually
helps reduce the risk of
hallucinations because we
are grounding it on your
actual company data,
your existing CRM records,
existing merchandise data,
existing marketing data.
Now, this one is
really important.
Zero retention
policies with all
of our third
party LLMs mean
that once the response is
generated from the LLM,
they discard everything
that we sent to them,
so they never
retain or train
their models on your
obfuscated customer data.
And when that
response comes back,
it is all passed through
our built in toxicity
and bias detection.
And this part is
really important
because it gives
me the confidence
to help customers deploy
and use our new copilot
for shoppers, which
is literally talking
to their customers.
So you can be
safe and confident
that we have got
your back when
it comes to the entire
loop of safety around AI.
OK.
I think it would be
cool to do something
with Copilot together.
Like I said, we're
going to get geeky.
You guys ready?
Yeah?
OK.
Chris, Miles,
you guys ready?
OK, how about a hand
for Chris and Miles
at the demo booth?
[APPLAUSE]
OK.
We're going to pretend
that we are marketers
at Aston Martin.
And our goal is to
build a campaign today.
And I want this campaign
to help our customers be
really excited during
what Aston Martin calls
the lull.
And the lull is
the period of time
from when a customer
ordered a car while it's
getting built to when
it gets delivered.
OK.
So let's ask Copilot for
marketers to help us out.
Create me a campaign for
customers who recently
ordered the DB12 car.
What Copilot
is going to do
is using our customer
data grounded on our brand
and information.
It's going to go and
create a campaign brief.
I like to think
that this is
sort of the pitch
from an agency.
Here's what we think
we're going to do.
This seems like a
pretty good campaign.
I want to give
it a preview.
And what we see
underneath this
is a summary of what this
campaign is going to do.
It's going to create
this segment for me.
It's going to write
an email subject
line and even a
preview of a paragraph
that it'll send
to the customer.
I think this
looks pretty good.
Let's go ahead and
create the campaign.
Now, this is where it gets
really cool because we're
going to go in
and edit this
built in email template
that it generated for us.
And what we're
going to show
is that this email
template is, of course,
rich in the Aston
Martin brand and tone,
but it's also got
some really cool data
inside of it.
So you remember
Kelly talked
about the hundreds of data
connectors that we have.
One of the things
Aston Martin has done
is connected their vehicle
assembly status data
into Data Cloud.
So now when this
email goes out,
it has a real time update
on the actual status
of her car across
the assembly line.
That's really cool.
But I want to add
one more thing.
If you've ever done any
commerce merchandising
or promotions, you
know that during this
build time is a
great opportunity
to offer them some
upsell, some cross-sell.
So we're going to
add a recommended add
on section using Copilot.
I simply ask it to build
this segment in the email.
And it goes and creates
it automatically.
Now, this is
pulling information
from the commerce
cloud product database,
like the images,
the metadata,
and even the title.
I think this looks good,
a great opportunity
to upsell some sunroofs.
Let's go ahead and
save this email.
All right.
So now, if
you've ever done
ecommerce
merchandising before,
you know that one of
the things you're always
thinking about is, how can
I make these offers even
more enticing?
So one of the things
that Copilot does for you
on your behalf is it looks
at your commerce data.
It looks at your
Service Cloud data.
And it notices things.
And in this case,
it's actually
going to tell
me proactively
that it thinks
it found a way
to make this campaign
more efficient.
And that's because a large
percentage of customers
have been going online and
buying the carbon fiber
tailpipe package
in conjunction
with the interior package.
So that's a really great
bundling opportunity.
So instead of having to
go into commerce cloud
and actually set
up the bundle
and do all these
different things,
I'm simply going to
tell Copilot to go ahead
and activate
that promotion.
So now every
customer that gets
that promotion, 10%
off all the carbon
fiber that they want.
OK, this looks
really good.
I think we're going to
go ahead and launch that.
So now what I'd
like to show
you is what this looks
like on the shopper side.
So let's switch
over to Abigail.
And Abigail, as
Kelly mentioned,
has been experiencing the
Aston Martin interactions
over her favorite
channel, WhatsApp.
But what's
really important
is that this is running
our copilot for shoppers.
So not only is she
getting the build
status updates
over WhatsApp,
it's also an opportunity
for her to say, hey,
is it too late for me
to add the carbon fiber
interior package?
Copilot for shoppers looks
into the assembly system,
notices that this is
a great time to do it,
and then says, absolutely.
But more importantly,
we built that promotion.
And Copilot for
shoppers says,
why don't you add
both for that 10% off?
Abigail says,
yes, add both.
And just like that, using
her save payment methods,
thanks to our new
Salesforce checkout SDK,
she's able to securely
check out just by saying,
please give me both
of those products.
And that's it.
That's our Copilot
for shoppers
and our Copilot
for marketers.
We created a
marketing campaign,
pitched it to one
of our customers,
and actually cross
sold some products.
So thank you very much.
I hope you guys
appreciate that demo.
[APPLAUSE]
OK, part number two.
Now, this is
where it gets,
as I like to say,
really fun and geeky.
So buckle up.
We're going to do
some cool stuff here.
I'm going to show you
how we can customize
everything that
we just did
for your individual
line of business
and your company using our
Einstein 1 Studio, which
is a portfolio of tools
that lets you do things
like build your
own prompts,
create new
Copilot actions,
and even design
your own AI model.
So I like this stuff
so much, that--
Chris, is it cool
if I do this myself?
All right.
Cool.
All right, team.
All right.
So here we go.
What I want you
to imagine first
is that I am a dealer
at Aston Martin.
Aston Martin sells
cars exclusively
through their
150 dealerships
around the world.
And one of the things
their dealers do
every day is they start in
their Aston Martin portal.
Like any dealer,
the first question
that I'm going to go ask
Copilot is, who should I
sell a car to today?
Which customer
should I reach out
to that are most
likely to buy soon?
Now, Copilot is
going to think
about this for a second.
And it's going to give me
the best answer it can,
which is there are
three people that
have appointments today
to come in and check out
cars.
And if you've
ever bought a car,
this may not be
the greatest signal
that I'm ready to buy one.
It just may be that
I'm coming in to browse
or honestly wants
some free coffee.
Who knows?
But what we're
going to do is now
go teach Copilot a
new set of skills
to help make this answer
a lot more effective.
We're going to
come over here.
And we're actually going
to build a new AI model.
OK.
You guys ready?
Want to build a
model with me?
Yeah.
OK.
Here we go.
All right.
So we're going
to go and create
a model from scratch.
And the model
that we're going
to create underneath
the covers
is what's called a
predictive regression
model.
So what that
means is we're
going to take a bunch
of historical data
about Aston Martin's
customers, put it
at a model, and then
have the model try
to predict an outcome.
In this case,
it's going to be
how likely are people
to want to buy a car.
So I first have to
tell the model, what
data am I going to use?
I'm going to pick all
of the individual data
that we created in
that unified profile
in Data Cloud.
Then I'm going
to hit Next.
You have an
opportunity, depending
on how big your data
is to filter down.
In this case, 516 records.
We're going to go
with all of them.
Now, this is where
you tell the model,
what are we trying to do?
And here, we're
actually trying
to predict the
likelihood to buy.
So we pick that
field as the target.
And then if you've ever
built a regression model,
you know you need to
maximize or minimize.
And we're going
to maximize
to one, which simply
means optimize the outcome
to predict people
that are going to buy.
Value equals one.
So we hit Next.
OK.
Now, this is where
it gets even cooler
because the model can be
trained on data using Data
Cloud from all over
the Aston Martin
ecosystem, average
ownership length coming
from the CRM database,
car mileage coming
from the telematics
connected into Data
Cloud, our recent
life event,
if they filled out
a survey or a form
telling people
that they've moved
or that they've
maybe had children
and are looking at
an SUV, all of these
are interesting
variables, so I'm
going to add all of
them to my model.
Then I'm going
to hit Next.
Einstein can also pick
the best model for you.
So we'll let it
stick with the GLM.
We're going to check all
of the information here.
Great.
And then we're
going to hit Save.
So model training
can take anywhere
from a few minutes
to an hour or so,
so I'm going to take
one out of the oven
that we put in just
a few minutes earlier
and show you the outcome.
So model builder
tells me that--
great job, guys.
We built a highly
predictive model,
a 91% chance that
we were going
to predict a
high likelihood
to buy for an Aston
Martin customer.
This is great.
But let's go see why.
It actually shows you
the top predictors.
And these are the values
in the training model
that prove to
be the highest
propensity of influencing
that decision to buy.
And this makes
intuitive sense, right?
If someone's had a car
for a really long time,
has very few open
service cases,
and maybe had
children or moved,
those are all
things that I
think would make
someone potentially
want to buy a new car.
So it feels good to me.
The data shows that
it's high prediction,
so we're going to
go ahead and we're
going to activate.
OK.
So now that we've
built a new AI model,
we're actually going to do
another very cool and very
geeky thing, which
is actually build
a new prompt template.
So I like to describe
prompt templates
to our customers as
mail merge for AI.
It lets you build
a new prompt which
can be asked in
Copilot anywhere
across Aston Martin.
And what I want this
prompt template to do
is help people find
the best customer
to reach out to and
send an email to them.
So I'm going to start by
pasting in a command here,
generate an email
that a dealer can send
to a customer inviting
them to come test drive
our newest offerings.
This message should
be clear and concise.
But as Kelly said, it's
really important for Aston
Martin's brand and
tone to be aligned
across every
customer outreach,
so I'm literally going to
write out Aston Martin's
brand and tone and ask the
prompt to always use it.
Third piece, we
need to point
the prompt at a
type of customer
record to go and actually
send a communication to.
So we're going to go
pull a unified profile.
And we're going to pull
it out of Data Cloud.
So this is the Data Cloud
real time personalization
model.
It's actually a data
graph that shows you
where all the composite
information is
coming from.
The record obviously
has been pre-built
and looks really
good to me.
But I want to add
one more thing here,
which is the
engagement history.
So engagement history
is how frequently
and how recently has
that person touched
any outbound email or
SMS marketing we've
done to them.
This looks pretty
good, but let
me just preview it.
And when you preview
this type of model here,
you're actually going
to see the raw JSON
output of the
application object graph.
So this is just giving
me one more check
that it's going to
output great information
about the customer.
So we'll save
and deploy that.
OK.
So now we're
going to go back
to the prompt
template workspace?
So remember, we built
the prompt template.
We're grounding it in
Aston Martin's tone
and voice.
And now I'm going to ask
the prompt every time
that it runs to pull
the unified profile
of the record.
So now we're going to
pull in that customer
information.
Let's go preview
it, so you guys
see exactly what I mean.
When I hit Maria Kostas on
the left-hand side here--
zoom in for you guys--
you're going to see the
response, the raw response
from the prompt.
So this is the actual
JSON coming back
from Data Cloud that shows
all the unified customer
information
that will be put
into the generative
response.
And on the
right-hand side,
you actually see
the email that we
would be sending
to Maria if someone
was invoking this prompt.
It's actually pulling in
dynamic car information
from the marketing
campaign.
It's offering her to
come by for a test drive.
And it feels very
Aston Martin.
It's very on brand.
And it even includes my
personalized thank you.
So this looks really good.
So we're going to
go ahead and save
that prompt template.
OK.
So now Michael becomes
a car dealer again.
And I'm back in
my dealer portal.
We're going to go ask
Copilot the same question
we asked it just
a few minutes ago.
Check this out.
Which customers
should I reach out
to today that are most
likely to buy soon?
And Einstein Copilot
is a lot smarter.
It now tells me that
I should reach out
to Ashley Harrison.
She has a very high
likelihood to buy.
It's explaining
her lifetime value,
key kind of
thing in my mind
I want to have
at the ready,
and most importantly,
that she interacted
with one of our campaigns
about two hours ago.
So this feels like a
really rich opportunity
for me to go and
reach out to.
And I actually want
to do that right now.
So let's email Ashley.
Draft an email inviting
Ashley to come explore
our latest offerings.
So now that
prompt template
is getting invoked.
It's actually
generating that highly
personalized email.
And all the dealer
had to do was ask.
All the back
end processing
happens to make sure it's
grounded in Aston Martin's
brand and tone.
This looks really
personalized.
It's nice and casual,
the way like to write.
It includes my name
as a follow up.
So I think this
looks really good.
And we're going
to hit send.
And let's go see if Ashley
is going to buy a car.
Hopefully, she will.
All right.
Well, look, this is one
of my favorite experiences
because it shows
how without writing
a single line of code,
you can completely
transform our Copilot
experiences to make them
your own.
You can use our
prompt templates
to give every dealer
or every person
at your company
an easy way
to pull unified
information
and use it in
rich outreach.
And you can use our model
builder to build your own
or bring models from
outside your network
to do an incredible amount
of personalization that
is only possible
on our platform.
All right.
So with that, I'm going
to turn it back to Ariel
to take you guys to
the next chapter.
Thank you very much.
Thank you, Michael.
That was fantastic.
And just to remind
everyone, everything
that Michael showed
there is available today,
Prompt Builder, Einstein
Copilot Model Builder
is available for
you to use today.
Now, the next part
of the presentation,
we're going to
talk about how
AI can make your analytics
and your collaboration
even better.
And with that, I'd like
to bring up Kelly Thacker,
who is in our demo booth.
We're going to have
another live demo.
And Kelly is the CMO of
Salesforce Commerce Cloud,
so take it away, Kelly.
Thank you.
All right, you guys.
So let's talk about
the AI revolution.
Now, with all of the
incredible AI innovation
that's happening, there
is an increased demand
from leaders, from
really everyone
in the organization
to have better access
to real time, accurate,
and trusted data insights.
Now, you see there's
this infinite loop that
exists between
data and AI, right?
So the quality
of your data
is going to inform
the quality of your AI
insights.
And those very
same insights
are going to inform
how you manage
and how you use your
data moving forward.
And at the end of the
day, all we really
want is data
that's accessible
and data that's usable.
So when we're talking
about data insights,
there is no better
product on the planet
than Tableau.
Tableau truly
transforms how
everyone in the workforce
can solve problems
using data, truly
becoming data experts.
Now, the Tableau
team has been
working very hard on
two new incredible AI
innovations that I'm
so excited to tell you
about briefly and
then show you.
So the first is
Copilot for Tableau.
So you've heard a
lot about Copilot
today from Ariel, from
Michael, from Kelly.
So the very same
incredible AI assistant
is now available for
every single Tableau user.
This means you can
streamline all your data
prep to data
visualizations,
truly helping you
accelerate data insights.
So you can talk
to your Copilot.
You can talk to your
CRM the exact same way
that you talk
to your analyst.
OK, the next innovation
is my favorite.
Out of all the
Tableau innovations
they've ever deployed,
this one is it
and because
this allows you
to truly interact
with your data
in a completely
different way.
Now, I get Tableau Pulse
every single Monday
morning at 7:00 AM.
And it delivers
all of the insights
that are most important to
me as a marketing leader.
So I get to
look at my MQLs,
my pipeline, my revenue.
And it's going to tell
me what's trending up,
what's trending down,
and any anomalies
in my business.
So now, I don't need to
go into a separate data
visualization to try to
connect the dots myself.
Tableau Pulse is
doing that for me,
so I love it
so much that I
am going to walk you guys
through an incredible demo
of Tableau Pulse.
So again, this
is Aston Martin.
This is how they
use Tableau Pulse.
At the top,
you can see all
of the metrics that have
changed week over week.
So right here, you see
that the APAC segment
is really surging
on impressions.
If you scroll down,
you see all the metrics
that matter a
lot, that matter
the most to the marketing
leader at Aston Martin.
So I'm going to
want to click
into my total impressions
to see what's happening.
Now, right here, I
see that impressions
have increased week over
week over the last--
and this is
amazing because I
get to see that
it's increased
based on historicals and
based on the expected
range.
I can break it
down by region.
And even better, this
is the coolest part,
I can even ask questions.
So again, can you imagine?
Now you can actually ask
questions about your data.
So I can say,
all right, what
are some of the
questions I want to ask?
I want to learn more
about what's happening
with my impressions.
OK.
So it's going
to even give me
questions I should
ask to learn more
about my business.
Now, all of this would
take how many days?
One, two days
from your team
to be able to dig in
to what's happening.
Now I can get it
literally-- you
guys are watching-- with
just a matter of clicks.
And even better,
I can share this
with my marketing team.
So now the marketing team
can take action on this.
They're seeing
that impressions
are increasing in APAC.
Now they can adjust their
budgets accordingly.
So just like that, I'm
taking action on my data.
This is why I love
Tableau Pulse so much.
Again, it's giving
me the opportunity
to truly democratize
AI insights, so
that I can action
on my business.
Now, I just talked
about step four, right?
All about
analytics with AI.
Now I'm going to wrap up
this keynote by talking
about the fifth step.
And this is all around
how we get better
collaborate with AI.
Now, as a
marketing leader,
I'm sure a lot of
people in the room
can also feel the pain
of how many interactions
we have to juggle, right?
You have interactions
with your internal teams.
You have interactions with
vendors like agencies,
customers, right?
And a lot of the tools
that we use, like email
and text messaging really
isn't that efficient,
right?
So wouldn't it be nice
if there was a better way
that we can really
collaborate with all
of these people
that are so
important to our business?
Well, this is where the
power of Slack comes in.
So Slack is truly
the central hub.
It brings together
conversations.
It brings in
collaboration.
It brings in automation.
And now it brings
in the power of AI
all in one single
place, truly changing
how we work.
And the Slack team,
just like Tableau,
has been working
really, really hard
on lots of new,
incredible AI innovations.
So I'm going to quickly
tell you about them.
And then better, I'm
going to show you.
So the first
one is Copilot.
Again, all the
same exact copilot,
that incredible
intuitive AI assistant,
is now available in Slack.
So if you're in Slack, you
can talk to your CRM data
the exact same
way that you
talk to your colleagues,
allowing you to avoid
the swivel chairing and
allowing you to actually
move forward
with work right
in your conversations.
OK, record channels.
Now, this is incredible.
It's taking all of
the record data,
and it's putting it
right into Slack.
So now you can collaborate
crossfunctionally
on the most important
opportunities and deals.
As a marketing
leader, I don't really
get a lot of access
to my CRM data.
And so now I can sit
side by side with sales
to help close the
most important deals
to add more value right
in the moment it matters.
OK, Sales Elevate.
Any sales
leaders or anyone
that does sales
in the room?
OK, a few people.
Tell your sales
friends this.
For Sales Elevate,
it's amazing.
It's bringing
in all the Sales
Cloud data right
into Slack.
So again, you get
real time deal alerts.
You get to automate
workflows and actions.
And you can edit
all of your CRM
on the go from any device.
And then finally,
Slack AI.
This is incredible.
I can't wait to show you.
This is allowing you
to search and summarize
everything, all of the
workforce conversations.
It's truly tapping into
the collective knowledge
and history
right in Slack.
OK, so enough
of the telling.
Now I am so excited to
show you the final demo.
This is like the
demo of all demos.
It's bringing
together everything
you've seen all through
the power of Slack.
OK.
So this is how Aston
Martin uses Slack.
Now, question.
I can get a little
bit frustrated.
I know I talk to customers
every single day.
It's kind of amazing
how much time
it takes just to get going
in the morning, right?
You wake up,
kiss your kids,
have a cup of coffee.
And then you have to
dig into your email,
dig into your
text messages,
call sales leaders, like
Stephanie Glenn right here
just to catch up
on what happened
while you were sleeping.
That is insane.
But now with
Slack AI, that
is no longer because
you have recaps.
So check this out.
Recap is literally
sending you
a daily digest of
all the app mentions,
all the conversations, all
the activities that you
missed while
you were away.
So right here, you see
marketing strategy.
I had just sent that
Tableau Pulse insight
when I was up at
the demo desk.
And you see there's a
lot of positive reaction
from the team
on this insight.
And they want
more information.
Now, while Recap is
amazing at summarizing
what's happening, I want
to dig into the data.
Now, before I'd have to
leave the Slack channel.
I'd have to go into a
data set, find the answer,
go back into Slack
just to send it.
Now I don't
need to do that.
Why?
Copilot.
Copilot is right here with
me right in this channel.
It has all the
context to this recap.
I can literally
type in, what
is the most popular
custom customization
in this region, and
just like that, I
get the information.
This is incredible.
So I can share this right
back out to team Japan,
give them the question
they are asking.
And they can take
action on it, again,
right in the
place they work.
OK, let's go back
to the recap.
Let's scroll down.
OK, stop right here.
Do you guys see right in
the Aston Martin deal,
we just closed.
We got a deal win alert.
Now, this was an
incredible five-day nail
biter.
Has anyone ever been
in deals like that?
Maybe?
Yes?
OK, so I want to walk
you through exactly what
happened.
Let's click into the
opportunity channel.
OK.
Amazing.
Now, any Slack users?
Yes.
That is amazing.
I love Slack too.
OK.
But for the
Slack users, does
this look a little bit
different, this channel?
It does, right?
This is the record.
This is the record
channel, right?
So this is where you get
all of that rich CRM data
right here right
in conversation.
So with the record
channel, I can edit.
I can automate workflows.
And even cooler-- well,
not cooler than that,
but I can also bring in
amazing Slack features
like Slack Lists.
So Slack Lists is going to
let me collaborate, again,
crossfunctionally
to prioritize
what is most important
to the business.
How do I need to
close this deal?
OK.
Let's go back
to the messages
because I'm telling you,
it's a really good story.
OK.
So a customer-- a dealer
reached out and said,
I have this really
bespoke customer request.
I need help.
The customer,
Amy, she wanted
to have the interior
stitching of her car
match the Chicago blue.
That is very
bespoke, right?
And while that's
cool, I thought
Chicago would be
psyched about this.
That's pretty cool, right?
Now, bespoke
color, very rare.
And so if you
scroll up, you
can see that
incredible model
that Michael built, that
propensity to buy model,
well, real data
came in and said,
actually, we're seeing
that the propensity to buy
went down.
So Amy is
probably not going
to buy the car
for two reasons.
It's actually giving
you the reasons.
The first one is because
it's such a rare color,
the supplier needs a
little bit more time
to secure it.
So now, we're not going
to get the material
until 2025.
Number two, is it's
a rare color, so not
a lot of
suppliers have it.
That's OK because
Copilot, again,
is right here in
Slack taking action.
It is 10 steps ahead.
So it is
automatically going
to send an email to every
single available supplier
to see if there's
an alternative.
This is pretty incredible.
Now, I want you to
take a quick look
at all of those-- do you
see that conversation
history?
Look at all of
those emails.
Can you imagine if
that poor sales rep
had to do that repetitive
task of the emailing,
and then responding,
and then emailing?
Now that rep was able to
focus on the high value
sales, rather than
the repetitive tasks
that Copilot took care of.
OK, so going back
to the story.
15-- or not 15 days.
It felt like 15 days.
Three days later,
the deal was closed.
Amy bought the car,
and she was psyched.
Now, it sounds like
a lot of magic,
so I just really
want to reinforce
what just happened here.
Number one, Michael
built that model.
That model was not just
for his specific use case.
It's pervasive.
It's across the
entire enterprise.
That model was used.
Real time data came in.
And the data
dropped the score.
Copilot proactively
reached out on Slack
to let us know that action
was going to be taken.
Number two, when
the score dropped,
it triggered an
automated workflow.
So that workflow
sent Copilot
to send an email to all
the available suppliers.
That's incredible.
And when Copilot
secured a supplier,
the opportunity record
was automatically updated.
Can we get round
of applause?
Does that happen in your
workforce right now?
No, that's Copilot.
That's amazing.
So that is the power
of AI in Slack.
And that is the power
of AI in action.
All right.
Ariel, back over to you.
Thank you.
That was fantastic.
Well done.
OK.
So we learned how
to build a customer
360, how to unify
data with Data Cloud.
Michael showed us how
to use Einstein Copilot
to have AI in
the flow of work
directly in your favorite
Salesforce applications.
And we talked about having
AI make analytics better
to really let us
talk to our analytics
as if it was an analyst
and our amazing Slack AI
features.
Well, this is the
start of your journey
at connections 2024.
And we have an amazing
conference for you.
We have a ton of
great content,
270 sessions, 50 demos.
I'd especially
like to call out
the sessions with
my MarTech team
if you want to learn
more about how Salesforce
is deploying
marketing technology.
Thank you.
There are a lot
of them are here.
And great customer
stories as well.
But one last request
I have, as a marketer,
I would like some
data from you.
So we love feedback
from our customers.
If you could
scan that QR code
and give us some
feedback on our keynotes,
so we can help
make it better,
we would love to get that.
And you have a chance
to win a free pass
to Dreamforce.
So thank you, everyone.
Appreciate your time.
And I hope you
have a great time
at the conference.
Thank you.
[APPLAUSE]
[MUSIC PLAYING]
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Main Keynote: Welcome to the AI Enterprise
Join Salesforce leaders to learn how marketers and digital leaders can build an AI enterprise with CRM + AI + Data + Trust.
Starring
- Kelly EliyahuSenior Director, Product Marketing, Salesforce
- Ariel KelmanPresident & CMO, Salesforce
- Michael AffrontiSVP & GM, Commerce Cloud, Salesforce
- Kelly ThackerSVP, Commerce Cloud & CMO, Retail & CPG, Salesforce
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