[MUSIC PLAYING]
SJEF VAN STIPHOUT: So
I brought some friends.
I'm here joined by my colleague
Sona, who is a Solutions
Architect in Google Cloud.
James, who is a data scientist
at creative agency Jellyfish,
and Tim who is CISO,
Chief Information Security
Officer and Global
Lead Analytics
and Infrastructure at Colgate.
Big data is big business.
I put up two very generic data
statistics to just prove out
the size of the opportunity,
but this is not new.
The reason I put these
two is to show how
this opportunity is twofold.
If you want to increase
return on investment
you can do two things.
You can create more return or
you can lower the investment.
So a lot of the things and the
applications that you'll see
can work in both ways.
So either you can
untap new opportunities
or you can do the
things you're already
doing in a more efficient way.
A lot of the data that we
talk about in the day-to-day
is structured data.
It's your advertising data.
It's your CRM data.
It's your structured
databases that you can either
access with a SQL
query or a dashboard,
but today we talk about
unstructured data.
And what is unstructured data?
Unstructured data
is the type of data
that doesn't come with a
predefined or an intuitive
schema that is easy
understood by machines.
So machines can display
it, like photos,
and you can browse through it.
You can easily change
them, but the machine
will have a hard time telling
you what's in the photo.
The same with recorded audio,
video, and email, for instance.
It's all digital but
it's hard for the machine
to actually understand
what is in the email.
So this is where the
big opportunity is.
So if we know that half, 50%, of
organization's structured data
is actively used in
decision making--
this is a survey.
This is probably on
the high end even
because if you ask people did
they look at data before making
a decision, a lot of people
are going to say yes.
But how many people
actually did it?
That number goes
down to less than 1%
if it's about unstructured data.
For instance, you can think of
older recordings of your call
center.
Those are usually recorded
for training purposes.
What if you could actually
tap into that information?
Even in real time, can you pick
up on things people are saying
and trends?
That's the type of data
we will talk about today.
This wouldn't be a Google
talk if we wouldn't talk
about AI and machine learning.
And in this case, it really
signifies that bridge
between your unstructured
data and your structured data.
So you probably already
have in place ways
to deal with your
structured data,
but machine learning really is
the bridge between those data
sources.
And Google Cloud then is
really the opportunity for you
to leverage and
to use, but Google
has learned about data
in the last decade or so,
developing their own algorithms,
and you can apply this
to your own dataset.
So what is machine learning?
Well, everything is
illustrated by cat and the dog.
Deep neural networks is a
form of machine learning,
and it basically trains to
recognize certain patterns
in unstructured data.
In this case, it's a photo
and it tries to figure out
it's a laundry basket.
Is that a cat or is that a dog?
So the animation on
the right signifies
different neural
[AUDIO OUT] different models
that the ML
application is trying
to figure out what it is.
And at the end it says,
OK, it's not a cat.
It's a dog.
This sounds simple,
but what if you
have to decide whether this
is a chihuahua or a muffin.
If your model can
differentiate between these two
you are on the right way.
Is it a labradoodle or
is it fried chicken?
Sheepdog or mop?
This really goes to
show how delicate some
of the differences are
and how difficult it
is if you want to build
something that you can rely on
in your decision making.
Because sometimes
90% is not enough.
You need to be sure.
Luckily, Google
Cloud has a suite
of products that can
address these concerns,
and we're well aware of
the fact that ML and AI--
the opportunities
present themselves
at different stages
and different locations
in the company.
You might have actual
machine learning scientists
who write their own code, who
build their own models, who
have the TensorFlow network.
Sorry, the package.
It's an SDK that really allows
you to apply the cutting edge
machine learning.
But not everyone is
equipped to do that.
There's a whole bunch
of data scientists
that also might
not want to do it.
They want to, on the one
hand, build something specific
to your business
and the other hand
use the knowledge that Google
already has about images.
If you think of
Google Images, those
are already all
digitalized by Google,
so there are ways to
leverage that insights.
One way is Auto ML, and
it's basically a hybrid.
You use a little bit of Google.
You use a bit of your own.
And then in the left
is the app developers,
and those are the APIs.
Those are really out-of-the-box,
simple solutions where you make
an API call, you send it a bit
of text, you send an image,
and Google will tell
you what's in it.
A sentiment score
goes pretty deep,
and you will see into use cases
what that actually looks like.
But what does that all mean,
and what do we care if we cannot
make money out of it?
So how do you go from
unstructured data
to business insights?
So the last five minutes I've
been talking about the role
that machine learning plays.
So you have your
unstructured data,
you run it through these
APIs or through your models,
and that brings you
to structured data.
So Google Cloud also
has a great suite
of products that allows
you to work with that data.
For instance, you can
think of BigQuery.
That could be a
great destination
for your structured data.
And from there you can
apply your standard
more common analysis, and we
start driving those business
insights.
I work for Google.
So you're probably like, well,
what else is he going to say?
That's why we brought two
customers on stage who
will talk about how they
apply this type of logic
in their business.
And we'll start
off with Jellyfish,
and I'll hand it
over to my colleague
Sona will introduce
the use case.
SONA OAKLEY: Perfect.
Thank you so much Sjef
for that introduction.
So hi, everyone.
My name is Sona Oakley.
I am a Senior Solutions
Architect with Google Cloud,
focusing on marketing
analytics and machine learning.
So today I'm going to
partner with Jellyfish
to talk a little bit about
the Google technology
that we use to help
implement creative insights,
and then the Jellyfish team
will talk about the business
value that unlocks for them.
So firstly, when we think
about unstructured data,
every single
company in the world
has an advertising arm
and a marketing arm.
And we realize that we're
sitting on a lot of images
that we could then
use to help generate
these valuable
business insights.
So I'll start with a
very basic definition
of what is a creative because
I understand we may not all
have a marketing background.
So creatives are
the visual materials
that are used to generate
leads and sell advertising
or marketing.
We are all familiar with these.
We're here today
at Next, and I know
I've seen a bunch of
Google Cloud creatives
as I've been walking through.
But I just wanted to
level set that this is
what we're talking about today.
And more specifically,
we're talking
about creative analysis.
So that's the comparison between
two or more of these images
to determine changes
in performance.
So when we're thinking about
a marketing organization,
we think about things
like click through rate,
impressions, clicks as
metrics of performance.
And we want to understand
how these two images compare
and contrast in performance,
as they're both here showcasing
the same lip balm.
So here we have the question.
Do lifestyle creatives perform
better than product creatives?
An example of a
product creative is
this one here in
the middle, where
we're showing just the product.
And a lifestyle
example is the one
here on the right,
where we actually
have a model who's holding and
interacting with the product.
Now, as humans, this is
really easy for us to look at.
We can look and we can
see that on the right
there's a model, on the
left there isn't, therefore
we very easily
classify this problem.
But to Sjef's point earlier, how
can we have a machine do this
at scale?
And that's what the power
of Google Machine Learning
can really bring to
the picture here.
It'll help us to be able
to identify these two.
The other question you
might be asking yourselves
is why should we
focus on creatives?
There are so many other aspects
of an advertising or marketing
campaign, like
targeting, like recency,
like reach for example,
that we've been hyper
focused on as an industry.
However, according to
a recent Nielsen study,
we saw that creatives still
contribute to about 45%
of overall sales.
So making sure that we
have high creative quality
is imperative to our business.
And it's almost
a no brainer when
we're running these
ads to make sure
that the content of that
creative is of high quality
and resonating with our
users and our customers.
So that being said,
if it's a no brainer
and we feel like everybody
should be doing it
and 50% of the business
relies on it, why aren't we?
So the main challenges that
we face today are threefold.
It's expensive, it's
manual, and it's rigid.
So it's expensive
and manual because it
takes a lot of human effort.
If we think about our two
creatives that we saw earlier,
today we would need
humans to actually go
in and tag that the
creative on the right
was a lifestyle creative.
It had a model in it, et cetera.
And so that really
limits us to only looking
at a subset of our creatives.
The other thing is that
it's a very rigid process
in that we have to think half of
these use cases ahead of time.
Because it's so
expensive and so manual,
we want to make sure that
we're getting the most
bang for our buck as we're going
through this analysis process.
So with Google and
Jellyfish, we thought
that there has to be
a better way of doing
this creative analysis piece,
and we used Google Cloud
technology to help do that.
So the first thing is
that we reduced cost
using the Vision API.
And I'll go through each
of these pieces in detail,
but I just wanted to
generally introduce them.
So we reduced costs
by using the Vision
API, which Sjef introduced, to
do automatic creative analysis.
Then we helped to automate that
analysis by implementing a data
pipeline so that way we're not
just looking at one creative.
We can look at
multiple creatives.
And then lastly,
we used App Engine
to allow for flexible
scaling here.
So now instead of just
looking at a set of 100,
we could scale up to 1,000 or
multi thousands or even more
using the same process.
So that's how we
solved for this.
So the first thing that we did
was evaluating the Cloud Vision
APIs.
While it might be fun to upload
pictures of fried chicken
and labradoodles, does
this actually apply
to advertising
creatives, and does it
have any business value for
our marketing organizations?
So that was the first thing
that we wanted to try.
And here, if we look
at the sample creative,
we had uploaded this
to the Vision API
and we saw that it
pulled out three labels
that we thought were
really important in doing
this kind of analysis.
The first is that it was able to
identify very accurately faces
and emotions.
So not only can we
now see that there's
a model in those creatives,
but we could actually
see the emotions that they're
expressing, whether it's
joy, sorrow, anger, et cetera.
The next thing was that we
could do textual analysis.
So in the bottom right,
where it says learn more,
it was able to identify
the text piece.
And then, lastly, we
were able to identify
the dominant colors.
So this helps to ensure
the quality of the creative
by ensuring that our
creatives are on brand
and using the color scheme
that's been recommended.
So that first part
was a success,
and we were able to prove that
it worked for one creative.
But I don't know about you
guys, for me I don't really
appreciate uploading
one creative at a time
and downloading them.
So to that end, we
went ahead and created
a data pipeline that took our
creatives out of campaign.
manager, which is a Google
Marketing platform product that
houses all of an advertiser's
campaign information
and creative information
in one place.
So this was the data
source for those creatives.
We then took them
and used App Engine
for our pipeline orchestration.
So this was where it kicked
off the flow for the rest
of the data pipeline.
And what it did is
that it published jobs
to Cloud Pub/Sub, which is
an asynchronous messaging
queue, that then kicked off
two separate work streams.
The first is storing these
raw assets in cloud storage
so we could do analysis
on them, and the second
was running them
through the Vision API
to do image analysis,
and then storing
all of those results
back into BigQuery
to run our results off of.
So this is the architecture,
and we'll actually
go through a live demo of this.
So let's see this in reality.
If we're looking at
this example here,
one of the first
customers that Jellyfish
went to market with
for this project
was with Walden University.
So here we have an example of
two creatives, creative number
one and creative
number two, and we
want to understand which
creatives show happy faces.
And if you guys have
been to the keynotes
like many of our leaders have
said, seeing is believing,
so let's jump in to a live
demo of how this process works
and we'll switch
over to the demo now.
Could we please
switch to the demo?
Great.
So the very first
thing that we'll do
is we'll actually try
out the Vision API.
So here, this is
an online website.
Anybody can go to
cloud.google.com/vision
and upload it with
your own creatives,
so I would recommend
that you do that.
Here I have downloaded
the creatives already,
so I'm just going to go
ahead and drag and drop.
Verify that I am
not a robot in fact.
And we'll see the analysis here.
So in a matter of
seconds we were
able to get back the API calls.
Like I mentioned earlier,
we wanted to look at faces,
so here we've
correctly identified
that there is a
face in the picture
and that joy is very likely.
So automatically, we're now
identifying the creatives that
have happy faces versus
the ones that don't.
The other thing
that I had mentioned
was the text analysis.
So here we're able
to pull out the text
from each and every
single creative
and understand where they are.
So let's go ahead and
reset this and try it
with our second creative.
So let's make sure it
works on both creatives
before we move forward
with a data pipeline piece.
And again, we're able to see
that the faces were identified
and that this face
is very neutral.
It's not showing any
likelihood of any emotion.
The other thing that
I wanted to highlight
is that this, of
course, is a UI view
so it's a very small view
of what we'll actually
be able to see when
we call the API.
Here if we look
at the JSON we can
see that these are all
the information that's
being recorded as the response.
So we can actually see, for
example, the bounding polygon
and the vertices
of the face itself.
So it really enables
you to do whatever kinds
of creative insights it is that
you want to as an organization.
So now that we've proven that
the Vision API piece works,
let's go ahead and go into
the actual data pipeline.
We're going to go
into Campaign Manager,
like I mentioned earlier, which
is the ad server tool that's
a part of Google
Marketing platform.
Here we're in Walton
University's creatives,
and we can actually
see the number
of creatives that have ran.
And I can scroll down
for eons on this.
And if I just zoom into
one of these creatives,
we can see some of the issues.
The creative names
here are really
made for campaign performance.
It helps us identify which
campaign this creative was
a part of, but it
really doesn't give us
any information about the
content of the creative itself.
So what we'll do is--
right before I got on stage
I kicked of the data pipeline
already, so we'll watch the
data pipeline as it progresses.
And what it's doing is it's
taking this list of creatives
and putting it
through that process
that I had mentioned earlier.
So I'll go ahead and go into
my Google Cloud Platform,
and here we can see
that I've kicked off
the job in App Engine.
We can also follow the
logs in Stackdriver.
So here, if I zoom
into our logs,
we can see that it's split
out into two sections.
The first is the
visionary, so that's
the Vision API actually
doing the analysis
on each individual creative.
And then the second piece
here is the gcs_uploader.
So again, that is how we're
uploading this information back
into Google Cloud Storage.
And then lastly, I
wanted to show the data
once it lands in BigQuery.
Because while I really enjoy
seeing all the details of how
this pipeline works, I'm
sure that for the business
folks in the room
you really want
to see what is the value
of this data once it lands.
So here I've gone ahead and
pulled together a query.
And before I go into it, I
just wanted to highlight here
on the left hand side.
We have the various
tables that came out
as a result of
our data pipeline.
So we have the face detection.
We have additional
image properties.
We have label detection,
logos, et cetera.
And then lastly,
we're able to join
this with performance data.
So now we're able to really
implement creative analysis
by seeing that performance data
side by side with our images.
So if I go ahead
and run this query,
here what we're going
to see is the results.
So these are the two creatives
that we looked at earlier,
and we see that in
the first creative
the joy likelihood is
five, which is very high.
And the second creative
the joy likelihood is one.
So here we have our very happy
face and are not happy face.
We were asked to redact
the number of clicks
and impressions due
to privacy reasons.
However, here you would be
able to see that performance
information, and we'd be
able to compare and contrast
the performance itself.
Jellyfish will actually
go into the details
of how they were able to
accomplish this test and more
of the value.
But I just wanted
to show how, if we
lay that foundation
of data you're
able to see all of
this in one place.
The other thing to note
here on the right hand side
is that we've also been able
to link out the Google Cloud
Storage URL, so
if anybody wanted
to go through and do a manual
check of these creatives
it's really easy to do that.
Before we leave our demo,
the last thing I wanted to do
was uncomment this
one line in our query.
So what if we had
decided instead
of looking at happy faces I
want to understand if people who
are wearing graduation
caps in my creatives
were able to perform
better or worse?
If I uncomment out this face
annotation headwear likelihood
and rerun the query, I'm
able to edit my hypothesis
in just a matter of seconds
and see the results here.
So here, if you remember
in our creatives,
the headwear-- neither of them
were wearing graduation caps
so we're able to see that here.
But if you imagine that
we're running this off
of hundreds of creatives,
not just one or two, then
you can see exactly
how impactful
being able to pull this
data pipeline together is.
So do you mind please now
going back to our presentation?
Great.
So thank you so
much for listening
to the explanation of the Google
tools that help to power this.
I will now hand it over
to James from Jellyfish
to go into the business
value that this tool was
able to provide.
JAMES RAPPAZZO: My name's James.
I'm a data scientist
at Jellyfish.
For those of you who
don't know, Jellyfish-- we
are a global digital partner,
as well as a Google Marketing
Platform and Google Cloud
Platform certified partner.
We offer a wide
variety of services
from creative
development and analysis
to media strategy, data analysis
training, and much more.
As part of the
data team, we build
solutions that help our clients
understand their big data
and then take these insights
to inform their business
processes.
So how are we using
this solution?
Well, it augments our
comprehensive data strategy
by informing our data driven
creative development strategy.
It helps us increase
performance.
As Sona mentioned,
creatives account
for a really significant
amount of sales.
And if we can just be a
little bit better at creative
we can deliver significant
performance lift.
And to that end, it helps
us quickly and efficiently
discover what works
and what doesn't
with creative development.
So the first thing
we wanted to do
was see if there was any
meaningful information
that we could pull out
from the Vision API.
And so we set up
a series of tests
using the adjusted
viewable click through rate
as our performance
metric, which is
essentially clicks over
total viewable impressions.
We then split our
creatives into two groups--
having and not having
a specific feature.
And then finally, used
a two sample t-test
to analyze significance.
So our first hypothesis
was that creatives
with happy facial
expressions have historically
performed better than creatives
with faces that did not
have a happy expression.
There were 184 creatives with
happy faces and 16 without,
and we found that
creatives with happy faces
performed two times
better than creatives
without happy
facial expressions.
For our second
hypothesis, we were
really interested in seeing what
makes call to action buttons
effective.
In order to do
that, we had to do
a bit of feature
engineering, which
is one of the really cool things
with the Google partnership
is that we're able to
build on each other's work.
So I used OpenCV, the
computer vision library,
to extract the location,
color, contrast, size,
et cetera of the buttons.
And then once we had the
location of the button,
we could cross-reference
that with the Vision API data
to find the text that
appears inside the button.
And then from there
we can find out
what impacts performance,
positively or negatively,
in terms of the text.
So our second hypothesis is that
creatives with other text aside
from "Follow your
Why" in the CTA button
have higher historical
performance than creatives
with "Follow your Why"
in the CTA button.
And there were 172 creatives
with other text, 16
with "Follow your
Why," and we found
that creatives with other
text have historically
performed two times
better than creators
with "Follow your Why."
So we're really excited
about this tool.
It enables us to do much
deeper creative analysis,
and we've started working
on a Self-Serve Analytics
Dashboard, which will basically
allow our clients to track
the general performance
of the different features
of their creatives.
And then we'll use
this information
to inform our
hypothesis-driven analysis
on the historical data.
And then, finally, we're going
to start exploring and building
a machine learning model
to predict performance
based on the feature mix.
Thank you so much, and I'm
going to give it back to Sjef
to talk about marketing
insights from text.
SJEF VAN STIPHOUT:
Thanks, James.
It was awesome.
So I realized I forgot
to introduce myself,
but I got a second chance.
My name is Sjef.
I'm a Marketing Technology
Specialist in Google Cloud.
It means that I'm
basically a custom engineer
but I work with our
customers when they either
bring in their own
advertising data
or they have their on
premise marketing solution,
how to leverage Google Cloud
and the advertising side
of the house in our products.
I have the privilege
here to be with Tim.
Tim joined us from
Colgate-Palmolive,
and we worked on a project
that I will tell you about,
where we did something
similar as James showed you
and Sona but then with text.
And it's hard to
underestimate the value of
and the role of online reviews.
So written text these days--
whatever is out there.
If it's on product review
sites or it's on Reddit,
if it's on blogs,
consumers are offering up
a lot of information
about what they
think of your products, what
they think of your brands, what
went well, what went wrong.
There's a lot of
reviews out there.
I looked up some numbers,
and I believe, for instance,
Yelp in 2018 had a number of 171
million reviews on their site.
There's no way you're
going to read those.
Maybe you have hired people
to read those reviews
and to pick up on problems or
to think about new features
that you can add to your
product or your hotel
or whatever business you're in.
But that's a lot
of work, and you
might miss very important
signals, especially if you're
talking about thousands of SKUs
that you would have to monitor.
This is where natural language
processing really has its
benefit, and Google Cloud
has two out-of-the-box ways
to really leverage
that technology.
One is pre-trained
machine learning models.
They come in the form of an
API, and that's just an API call
like we saw with Sona and
James with the Vision API.
There was a version of it
that does natural text.
So it just sends the
body of text to the API
and it will return the fields
with the different dimensions.
You can also train
your custom models.
This is what I was talking
about earlier, where
you have this hybrid model where
you leverage what Google knows
and what you know.
Especially if you're in
a very specific sector,
like if you're in law or
it's a medical journal.
It might be jargon
and certain subtleties
that you as a company really
are aware of but Google didn't
optimize their models for.
That's really where
Cloud's AutoML comes in.
So you train it to look
for those specificities
that you know that
are out there,
and then we'll see solutions
that start with an API
graduating in AutoML.
As soon as you start to
see where that value is,
that is really your next
step to really leverage
those subtleties.
So the Cloud Natural
Language API.
This is what comes out.
So you've got a general score
of the whole body of text.
That comes with a magnitude.
And I think we often talk
about mag sentiment analysis,
but I think
magnitude, or salience
as we see with entities,
are very important features.
Because something
can be very negative,
but if it's not
central to your text
or if it's not very
pronounced, then you
might want to rank it lower.
So it's a great way to
start weighing your results.
So even if you use the API
and not an AutoML model,
you're still able to really
pick up on some subtleties
because you start to see where
the API might overgeneralize
things.
For entities, this is
where you really leverage
Google's Knowledge Graph.
Google has, for
the last decades,
really tried to figure out what
are two people talking about
and how can we
identify-- for instance,
in Google search if
you type something in,
what are they
actually referring to?
So there might be five
ways to talk about a car.
You can now apply that
Knowledge Graph to your text.
So we'll say you talked
about the Eiffel Tower.
You talked about a cinema or
you talked about a conference.
For all those entities,
you get a sentiment score
and a magnitude and
salience, so you
know how important
that element was
in the total body of the text.
The same for sentences.
It will break out the sentences
and it does language detection.
So if you have an
international market
and you get reviews,
for instance,
in different languages, then you
can use the API to detect it.
And it's now available in--
I'm going to read
it from there--
monitor was English, Spanish,
Japanese, Chinese simplified
and traditional, French, German,
Italian, Korean, Portuguese,
and Russian, and
this list is growing.
But you see that, in a
multilingual environment,
this is really a tool that
also works out of the box,
so you won't have
to translate. you.
Won't have to hire people
in different countries
to do those analytics.
This is just the first review I
ran into on Colgate toothpaste.
It's this person's
favorite toothpaste.
It's good cavity fighting
toothpaste with added whitener.
The whitening
features really works.
Why wouldn't you
want a bigger smile?
So you see, I'm not going
to be the whole vibe
because it's actually
a lot of text,
but that really goes
to show that, if you
want to do this at scale, you
need an automated solution.
So, similar to what
Sona and James showed,
we built a pipeline where we can
analyze these reviews at scale.
And it starts with
a storage bucket.
So as you might know, Google,
BigQuery, and other data
analytics really
excel in situations
where you have combinations
of streaming and batching
pipelines.
So this is an example of
how you add batching type
pipeline to your architecture.
So let's imagine this review
comes into the storage bucket.
There is a feature
for Google Storage.
It's called object
notification, and what
it does is, as soon as a file
arrives, or when it's updated,
it sends a pops up notification
to a topic of your choice.
For people who are not aware
or don't know what pops up is,
pops up is a messaging service.
It's basically a Slack
group for applications.
So it says, OK, I've
found a new file.
That triggers a Cloud Function.
What the Cloud Function does
is it picks up the review
and it makes the API call.
So it just reads the file and it
sends the plaintext to the API.
The API runs it through
all the Google magic
and it sends back the fields
that we just walked through.
The Cloud Function waits
for the API to finish.
It adds that data.
It augments the
file with that data
and puts it back
into cloud storage.
You might wonder why you do the
round trip in cloud storage.
It's because you want to
preserve the initial state so
that, if you decide to structure
differently in BigQuery
or you want to build
another pipeline,
you have that data available.
And with the storage
options available,
it's very competitive.
It's not expensive, but
you don't have to do that.
You can also
directly [INAUDIBLE]
into BigQuery if you want if
that suits your needs better.
So, from that, it
arrives in BigQuery
and, from there,
it's structured data
and it becomes available
for your analytics.
So I'm going to switch
to the demo right now.
I'm going to show you
what that looks like.
So I copy pasted--
well, actually,
Sona did, thank you.
But she copy pasted the review
that you saw into the website.
This is a similar online
demo that you can just
use on the Google Cloud
website, and it shows you
how the NLP API works.
So if we press Analyze,
it does really nice things
with pretty colors.
This is the Entities
tab, and it really
shows the different
entities that it
has identified in this review.
So it knows its
talking about cavities.
It knows it's about toothpaste.
It gives a type so it
knows it's consumer goods.
It gives you sentiments for
these different elements.
It thinks whitener
is a person, so that
is an interesting observation.
Colgate [INAUDIBLE] Google
Total is an organization,
and this really opens the
door to also brand tracking.
So you can start
to see at scale how
people are talking
about your brand,
not just about your product.
Then you can start to see which
products might invite people
to talk about your brand.
So where is your brand
exposure the biggest?
That could inform investment.
That's not all.
So we have sentiments.
So we get a score for the
whole entire document,
so that's the 0.7 with a
pretty hefty magnitude.
And then you see what I
promised per sentence.
What is the sentiment and
what is the magnitude.
Then there is some very fancy--
if you know languages and
you really want to dig in,
Google also gives
you syntax analysis.
So it starts to designate
certain words and parts
of sentences-- what
they mean, the role they
play in the sentence.
And this can be
really informative
if you want to start building
models on top of this
if you want to see more
linguistic analysis.
Thank you.
I'm going to switch back
to the presentation.
All right.
So I mentioned him three times
now but he's finally coming.
Tim, thank you for joining us.
TIM BOOHER: So I'm
a little surprised.
Whitening is a person.
He runs around in
our technology center
and puts the secret whitening,
and Google just discovered him.
So that's a trade secret
out there for the world.
Happy to be here
and have a chance
to really speak to what we're
doing with Google, which
is super exciting
for me, as I connect
Colgate's data to
decision makers
to drive growth and value
in our analytics program.
Key to that is a
flexible set of tools
that empowers us to ride the
wave of technology that's
there.
It takes an investment of skill.
It takes some really
good partnerships
to be able to do that.
But once the flywheel
starts moving,
the applications
proliferate really quickly.
For the demo you
just saw, once we
built that capability we found
around eight different use
cases inside the company
for that corpus of data.
Much more important
than the data set
is the flexible ability
to go out and gather
the data that we need.
For example, if we
started on reviews
on an individual
consumer retailer site--
understood what products people
might be asking for or sizes
[INAUDIBLE] that we
may not be doing--
that we might not be
presenting to the world.
We might then pivot
from that to looking
at consumer affairs
and our ability
to respond with the limited
people we have to what people
are saying about our products.
To translate them into different
applications so that we can
empower the teams
that we already have.
But data sites like that tend to
be very useful in applications
like e-commerce optimization.
What's great about that is we
don't end up with a Balkanized
set of tools, but we're
empowered to take a set of data
and a flexible
acquisition capability,
analysis capability, and
then action capability--
chain those together in a
number of business cases.
And that matters a lot.
As a company, we have more than
3 and 1/2 billion customers
that use our products
around the world.
By and large, the relationships
with those customers
are inter mediated
by the distributors,
by the retailers that we
sell those products through.
So understanding
what consumers want,
what they think
about our products--
it's really important to be
able to use the power tools
that you're shown here.
And it's important to put it in
the context that Sjef showed,
where we're able to move
around blue hexagons
and quickly rearrange and then
agilely focus our business
where the value is going to be.
The three points made here
are first driven by the fact
that the complex ecosystem
that we're at in the world
is discernible by the tools
that you've seen here.
That takes engineering
and writing code.
It takes an
investment to partner
and build that internally.
That's a major effort of ours.
The second part is we
need to have partnerships
to prime the pump to show
what's possible in this type
of context.
Once you do that you're
past a barrier of entry
and you can start to
present to business users,
whether it's in marketing,
supply chain, product
development, and start to
proliferate the use cases.
And then, you enter
into a virtuous cycle
where demand for this type
of capability goes up,
and you're able to increase
your resource investment
and capability in this area.
And I think it's pretty
clear from everyone
that using GCP for
us has been really
helpful to start with
a low barrier of entry
and quickly move into
doing complex things
that we're able to expand
across a diverse base of users.
I would love to talk to
you afterwards about this,
and thanks to Sjef for
setting this up for us
and showing us the way.
SONA OAKLEY: Awesome.
So we will go ahead and
do a really quick wrap up
of what we've seen here today.
So essentially, you saw
two different examples
of how we can start
to get insights
from unstructured data at scale.
The problems that both Colgate
and Jellyfish articulated
are not new problems.
They are things that we've
been dealing with and finding
workarounds for.
What's really new here
is the innovations
that Google Cloud has been
able to bring from a tools
and infrastructure
perspective to really help
solve these problems at scale.
And that is, to Tim's point
and to James' point, the power
of the partnership between
Cloud and the customers
that we work with.
[MUSIC PLAYING]
