Wow, oh wow, do we hear a lot about customer
experience these days.
But, when we hear the buzzword "customer experience"
and people talk about placing the customer
in the center using the customer as a reference
point, what does it mean in practice?
How do you do it?
When you move away from the buzzwords and
you actually start executing real plans for
real customers, things become much harder.
Today, on Episode #290 of CxOTalk, we are
speaking with somebody who knows.
We're speaking with Dutta Satadip, who is
the global head of customer operations at
Pinterest and, previously, he was at Google.
I'm Michael Krigsman.
I'm an industry analyst and the host of CxOTalk.
And, before I forget, be sure to subscribe
on YouTube.
There's a tweet chat taking place right now
using the hashtag #CxOTalk.
Dutta Satadip, how are you?
Thanks so much for being here.
Thank you, Michael, for having me.
Dutta, tell us about your role at Pinterest.
I joined Pinterest recently.
Just to give a quick background, Pinterest
is in the business of helping users discover
what they would like to do and then do them
in real life.
And, I joined Pinterest from Google because
I think there's a big opportunity combine
customer experience, bringing the customers
at the center of how we operate as a business.
How do you define customer experience?
Let me ask you that way.
The customer experience is effectively the
sum of all the interactions that any customer
or user has with the business.
And, the reason I say it's all the touchpoint
is because it's just not when a customer calls
for help, but it starts from the first time
anybody engages with your business and the
lifetime relationship they build with your
business.
It's not just transactional; it's a combination
of transactions, summing all of them up, and
the overall relationship that it builds together.
It's interesting to talk about transactions
because, when we talk about customer experience,
people tend to talk not about transactions;
they talk about empathy and feeling.
And so, what do you mean by transactions?
Maybe draw those distinctions for us.
Absolutely.
I think, traditionally, a lot of customer
experience work has been around understanding
customer satisfaction, understanding net promoter
scores, and these are great, I would say,
backward-looking indicators, scores, and measures
of how customers engage with the business.
What has also been part of this journey is,
for the most part, these have been mostly
centered around the customer service type
of organizations, which is mostly: I ask a
question and I get a response, and how well
I do in that.
I think, when we talk about empathy, a lot
of this is around, specifically, are we listening
to customers well?
Are we answering their questions well?
Are we empathizing and putting ourselves in
the shoes of the customer so that they can
get a very good outcome for an individual
transaction?
I think, as businesses have grown and evolved,
most customers now have to focus on not just
acquiring and answering questions, but a lot
of it is around customer retention.
And, thereby, now the shift has become, okay,
what is a great customer experience?
What is it that we are doing to create customer
outcomes?
When you combine experience and outcomes,
that's kind of customer success and what makes
it really customer operations.
Those are the great external things for customers,
but I think the big shift that we're all trying
to work towards is, how do we, as businesses,
understand these goals and put the customers
in the middle of all of our business operations
and how we execute internally?
I think everybody talks about doing that.
First off, then, why does everybody talk about
that?
Then, maybe we should discuss the challenges
of actually doing it in practice.
I think this is one of those things, right?
It's like motherhood and apple pie.
It sounds amazing.
It sounds good.
But, it's extremely hard to do.
Traditionally, the way most organizations
have evolved/grown is, there are executives,
there are functions, and each of the functions
are incentivized to perform one or two things
and do them really well.
Those types of incentives typically drive
some version of either siloed organizations
or siloed incentives.
That is why it is super hard to bring in the
concept of customer in because, when we talk
from the perspective of a customer, they honestly
don't care whether marketing's goal is X and
customer goal is Y.
They want a seamless experience no matter
who they touch, right from the start when
somebody reached out to them to do something
and ask them to invest in their company, in
our case in the form of an advertising campaign,
all the way down to when there is a billing
issue, and somebody needs to resolve that.
I think it's that connecting of the dots across
the different organization silos and the measurement
and the metrics that go around that have to
now evolve to being more cross-functional.
That's the pivot that we are working towards.
These are organizational issues then that
interfere with having the right kind of customer
experience.
I think that is probably one of the biggest
drivers and then the biggest challenges in
making this vision real.
It is a lot about organizations.
It is definitely a lot about understanding
the challenges in a data-driven way, in an
objective way and, ultimately, it is also
about the overall culture of the organization.
When you talk about it in a data-driven way,
that seems like a very key point, especially
today.
Absolutely.
I think one of the things is, back at Google
when I started there, I was there for seven
years.
When I started initially, Google was growing
really well and, one of the things I observed
was, we celebrated a lot.
But, what we celebrated were stories: the
time that we saved the customer, the time
that we got the great deal.
The challenge with stories is, we don't know
whether it is a one-off great instance and
an example or an actual pattern that we need
to suss out, scale, and do more of.
Where data comes in is to be able to help
us, to graduate us from these amazing stories
to some sort of stats, right?
This evolution from stories to stats is what
data provides us.
It helps us identify, is it truly a problem;
is it a meaningful problem; and is it something
that is going to be repeatable across wide
ranges of customers, wide ranges of internal
business processes, and then be able to solve
that?
That's really fascinating.
Data helps marketers move from stories to
stats.
Then what?
What's the next part of the chain?
I think most of the data in this journey that
we start with is often backward-looking, and
that's how the journey is.
It's nothing bad or good.
We have data, we start to analyze the data,
but it's typically backward looking.
The true opportunity is to take all of this
data and convert it into a science.
The amazing thing is there's a lot of technology
available that wasn't available before to
actually convert this data into forward-looking
insights.
On the high end of this, you could apply machine
learning algorithms, et cetera.
But, on the lower side of it, even getting
to basic stats that look at making predictions,
making recommendations, is a very, very good
start.
The journey that I would summarize is, if
you're at the story stage, how do we get to
stats?
And, if you are at stats, how do we convert
the stats into a science?
Okay.
How do you convert the stats into a science?
[Laughter]
It sounds like a great story but, unfortunately,
it involves a lot of nitty-gritty, crazy amount
of detailed work.
So, let me give an example of how we are going
about it.
Ultimately, I believe these journeys are successful
when we understand what the purpose is we
are solving and what is the problem we want
to solve.
Trying to boil the ocean usually never ends
up in the right position.
Trying to select a technology platform before
understanding the business problem typically
does not end up in the right direction.
One of the challenges that we are working
off at Pinterest is, how do we ensure we're
a growing business?
We have over 200 million active users on Pinterest.
We want to grow these numbers.
We want to make sure our advertisers and our
pinners find value in the system.
As a newer platform, we've been a company
only since 2010.
We're going and acquiring a lot of new customers.
Now, as we all know, to acquire new customers,
our sales teams should be spending more and
more time with customers.
When we look internally, we see a lot of time
being spent on internal activities versus
on external activities.
So, our goal is to have the best customer
experience.
But, if our sales teams are not really talking
to customers, understanding their requirements,
we are probably missing the mark somewhere.
That's the basic, core problem we are trying
to solve here.
What does that mean?
For us, it means truly unpacking what is driving
inefficiency I our system?
At this point in time, we're in the process
of making sure that we are building that one
view of the customer, all our customers' information,
not only what they have spent with us and
all the financial stuff, but also connecting
that with all the issues, et cetera, that
they have seen so far.
How have we tried to work with them?
How are our product adoption metrics looking?
Give a 360-degree view of that customer in
one place because, if we've tried to solve
that problem, now we have the foundation to
go back and understand very different types
of questions, specifically questions like:
What kinds of services; what kinds of help
can we provide to customers to maximize their
outcomes?
For us, outcomes is campaigns.
Is it, we need to give them a better understanding
of measurement?
Is it, we need to give them a better understanding
of their creatives, which is the image that
shows up in the ad?
Those are the types of conversations we want
to build towards, but our first and the foundational
step is having a view of the customer in one
place.
Is this the difference between customer experience
and customer operations, or is this how customer
operations support creating the right kind
of customer experience?
I think this is the beginning of the difference.
This whole data-driven approach is, I think,
one of the key hallmarks.
You're absolutely right, it's being done in
service of making sure that we are able to
deliver not only a great customer experience
but also have great customer outcomes.
To make it real, when we talk about experience,
it's probably not the best experience if somebody
walks into a meeting with an executive not
knowing that there was a trouble running their
campaigns and three of their campaigns had
some sort of an issue right before the meeting.
Chances are they executive who was going to
have a conversation knows about that and is
going to bring that up.
But, that's sort of in service of experience.
In terms of operations, if you have done something
really well, having that snapshot tidbit right
at the hands to make that conversation happen
is in the service of customer outcomes.
I want to remind everybody that we're speaking
with Dutta Satadip, who is global head of
customer operations at Pinterest.
Right now, there is a tweet chat taking place
using the hashtag #CxOTalk.
Please, join in, and you can ask your questions.
Dutta, as you are relatively new to Pinterest,
and as you think about the investment decisions,
investment of time and money, that you need
to make in terms of furthering customer operations
and the support of customer experience, how
do you build the framework for making those
kinds of investment decisions?
That's a great question.
When I joined Pinterest, one of the things
I knew was, even though I came from Google
and I was I a similar business around online
advertising, one of the things I did was actually
to go and talk to everybody inside and outside
the organization.
I talked with about 40 to 50 sales team members
to understand, what is it they did with customers;
why were customers buying from us; what were
the friction points?
What that allowed me to do is to get a very
bottoms-up understanding of the challenges
that are pertinent to Pinterest at this point
in time for the business circumstances, conditions
that we are in.
Understanding that allowed me to actually
stitch together what I talked about before
a little bit, [which] is, what are the touchpoints
that matter?
Specifically, how are we working with our
customers from start to finish?
The way we articulated it out is, we have
effectively a few phases that we go through
in our engagement interaction with our advertisers,
number one.
We are doing some version of planning with
our customers, right?
Our customers know about us.
They have expressed some interest.
We're raising awareness, but we're doing some
planning.
We want to understand what they want to do.
Then, we want to go and do some version of
a pitch, right?
We construct a pitch.
We go to a sell, do one, and so forth.
Our business is advertising.
We get commitments on how much they would
like to spend with us.
Once we get that commitment, we actually go
back and implement.
Implement is our next phase.
Most advertising campaigns need some kind
of optimization, tweaking, to make sure we
are meeting the customer objectives.
Optimization is the next phase.
We do some level of up-selling.
We tell them, if they put some more money,
they can get better outcomes.
Maybe they can get more reach, or maybe they
can get better conversions, which is people
going to their website, right?
Last, but not the least, we want to package
all of this stuff in the form of a nice measurement
and give them synthesized outcomes that we
can go back and have a discussion again, which
is back to the plan phase and say, "Hey, we
delivered this for you.
What can we do more for you?
How can we help your business grow?"
This process of going from plan, pitch, implement,
optimize, upsell, measure, is the lifecycle
that we came together after having these conversations
internally and externally.
For you, investment decisions follow the customer
and the advertiser lifecycle.
Absolutely.
We look at, how do we want to operate, what
does good look like, and what are the biggest
friction points in each of them?
Then, we can make a decision, "Hey, this is
the place that has the biggest friction and,
if you invest something here, this is going
to give us the biggest bang for the buck."
Obviously, we are running numbers.
Obviously, we are running models that are
not always perfect, but that's the overall
approach we are taking to prioritize our investment
decisions.
Now, the customer lifecycle at a company like
Pinterest would be quite different from that
at Google.
Although, I guess, for both Pinterest and
Google, you have multiple customers and segments
and so, therefore, different customer lifecycles.
But, when you were at Google, was this similar?
Did investment again follow the customer lifecycle?
Yes.
We actually went through this process, a version
of this process.
It was obviously different because Google
has many different products in the portfolio.
From the customer perspective, I had the full
portfolio of advertising products, which is
over 100 products.
Yes, we had to do a little bit more segmentation.
Also, the growth trajectory of each of these
products determined where and how much you
would prioritize.
But, the effective framework remained the
same of how we went about finding what matters
to customers, what will drive the best outcomes
for customers, and what were the biggest frictions
points for us to enable those outcomes.
How do you make even those decisions because,
as you're going through this process, you'll
have people who raise their hand or raise
their voice the loudest?
How do you decide?
How do you figure it out?
I think that is the reality of these roles
and this journey.
People feel very, very passionate about their
segment of the business or their product or
how investments should go into a particular
product line because it's growing faster than
others, right?
This is why I say starting to establish facts
from the get-go and using data to drive the
decisions is a way to sort of bring this together.
Now, data is a very technical way of doing
this.
One of the things that I have always believed
is really trying to understand what is it
that we want to optimize for.
A lot of this is understanding what my stakeholders
want.
Because this is an extremely cross-functional
role, one of the things I would say is I spend
a lot of time not only at the ground level,
but truly understanding what my peers, the
different executives for the different functional
roles, are looking to accomplish.
Is there a better happy medium?
It's not always if the data says X, let's
just do X.
Sometimes there is qualitative information
that needs to be funneled in.
The second thing that I always say is, none
of this is locked and loaded, and this is
the strategy.
We're just going to take six months and execute.
I think the best way to look at this is launch
and iterate.
Try something.
If there are two competing points of view
and we really don't have a good way to understand,
do experiments on both of them with a smaller
set of customers and see which one is yielding
the best outcomes.
Then make the pivots and the investment decisions
accordingly.
What I've observed is typically it's not a
choice amongst 40 things.
We're really debating about two or three things.
Being able to pilot something with two or
three choices is far more manageable than
trying to boil the ocean with, like, a list
of 30 or 40 objectives.
Dutta, how do you reconcile the data and transactional
view of customers with the marketing view,
which is about touchy-feely and empathy?
Can data capture that empathy in any way;
measure it in any way?
I think, while there is technology to look
at things like comments, scale, and do sentiment
analysis, et cetera, this is a little bit
of an art and a science.
Right?
I think the data is a good way to shorten
the problem or refine or segment the problem.
But, ultimately, there is obviously an element
of understanding the business, having business
intuition, leveraging our own experiences
to layer on top of it.
Even for things like marketing outcomes, certain
things we know to be true.
We do know there is a certain level of awareness
that will drive product adoption.
We do need to do announcements and things
like that for people to be aware of what we
provide; what our value proposition is.
They are not necessarily a transaction.
You can't take these events and say, "Did
I immediately get X, Y, or Z?"
They're not necessarily performance marketing
objectives.
They're probably more longer-term relational
metrics that we are looking at.
We do try to balance what is a more long-term
needle we're trying to move versus what's
a short-term thing we're trying to do.
As an example, if you want to acquire more
customers in a more performance marketing-oriented
way, then the metrics are a lot more tied
in and it's more metrics first versus if you're
trying to build brand awareness, visibility,
relationship, et cetera.
It's a little bit more long-term.
Do you use proxies?
Do you look at data and say, "Okay, this data,
if a customer is doing this, if they're leaving
more comments or whatever it might be, that
this means we're somehow touching them in
an emotional way"?
Absolutely.
We definitely look at both qualitative and
quantitative, the frequency of interactions,
et cetera.
We were born in the world of the Web and beyond,
Web and mobile, so we also have a lot of metrics
we look at from the product perspective.
Our products are instrumented to understand
how much engagement we have with our platforms
and who is spending it.
The beauty about Pinterest is it's just not
a passive platform.
When people come here to discover stuff, they're
looking at images, they're reading stuff,
and they're actively organizing them by clicking
on save and pinning them onto their boards.
That's a very good signal for us to understand
what's working and what's not working because
we can, overall, not only see how much time
they're spending on the platform, but we have
a clearer intent that we are seeing on the
platform through some of the actions that
they are taking within the platform.
You're trying to discern customer intent from
what, from the actions they're taking?
Is that the right way to describe it or put
it?
That is right.
That is absolutely right.
On the platform, actions like pinning it onto
a board, how many boards they have, all of
those things are directional indicators of
engagement for us from the pinner perspective.
Then, from the advertiser perspective, I always
like to say money talks.
If people love our outcomes of our campaigns,
they spend more.
If they don't like the outcomes, they shift
budgets and they go elsewhere, right?
We have a very clear sort of "voting" through
a dollar system that kind of works on the
advertising side.
When we look at what's happening on the pinner's
side, when we look at what's happening on
the advertising side, we can combine all of
these perspectives.
Like I said, I really wish it was as easy
as putting everything onto a spreadsheet and
just kind of sorting and picking the top two.
The best we can do with data is we come to
the top three, four, or five recommendations,
and then we have discussions around what is
the best outcome that we are trying to optimize
for.
We have an interesting question from Arsalan
Khan on Twitter who asks, "How does or how
should internal customer experiences affect
external customer experience?"
I think that's an amazing question.
I think there's been so much research in the
recent years that happy employees, engaged
employees, the overall culture of empowerment
within an organization leads to amazing experiences.
If I take a little bit of a longitudinal view,
for the longest time the focus of customer
experience had unfortunately transitioned
to how to optimize, do more calls, follow
a script, and so on and so forth.
Unfortunately, we all have been on the other
side of that call or the other side of that
engagement or interaction where we've been
asked to reboot our computer even though we
know that is not the issue, right?
I think the evolution is, if we empower our
team members, they're often seeing the issues
at the ground level.
If we ask them, "What is it that is going
to help us give the best customer experience?"
95% of the time they have a better, more practical
idea than what I can come up with.
Part of our mission was, at least at Pinterest,
and it was the same at Google, how do we engage
and create that forum to suss out these ideas,
engage our team members, and make sure that
they are part of the solution?
Creating that forum, we tend to get more,
happier, engaged internal team members.
Then, the discussion is not about, "I need
to follow a script," or, "I just need to close
the transaction," or, "I just need to answer
this and move on forward."
The question is, "How do I facilitate the
best outcomes?"
And, if I need to go out and do something
extra, I go and do it.
It's the process of pure empowerment, creating
forums as a leadership team to get those ideas,
activate those ideas, and creating the culture
where people can go above and beyond.
I think those are the real dimensions of that
internal mobilization that needs to happen
to deliver amazing, not just experiences;
experiences and outcomes.
Yes, the focus on both experiences and the
outcomes.
We tend to get lost in the experiences without
connecting the outcomes to that, and very
often it seems.
When you think about customer experience,
clearly the product is a core part of that.
How do you break down the silos in order to
ensure that the feedback from the customers
makes its way back into the product in order
to, again, improve that experience?
That's a great question.
I've been very blessed, I feel.
Both of my recent companies, we've always
had the overall values, mission of truly keeping
our pinners first, or our users first.
As a company, we not only believe it; we act
on it.
So, this makes my job so much easier because
taking the feedback from what we are hearing,
whether it is during a sales cycle, whether
it's during objection handling, whether it's
during actual running of a campaign, we have
a team called Product Operations that is responsible
for actually looking at all of this data,
synthesizing across new opportunities and
improvements in our products, truly partnering
with engineering, and making sure that the
biggest issues get solved.
I think it's always a balance around innovation
on one side and improvement on the other side.
As a growing platform, that balance is sometimes
hard to accomplish because we want to obviously
showcase the great innovation and sometimes
improvement lags behind.
But, by having the data from customers, having
the data about what the market is asking for,
I believe we are able to arrive at a better
balance of innovation versus improvement,
as we engage with our product teams.
Speaking of innovation versus improvement,
let me bring in efficiency here.
How do you think about the balance between
the efficiency of customer operations, which
is to say saving money, versus the additional
investment that's required in order to create
a better customer experience?
A lot of times those two are diametrically
opposed when it comes to a customer service,
especially.
That's a great question.
Having been in different roles in my past
and having done a lot of customer-centric
and operation centric work before I came to
Pinterest, I think has evolved my personal
thinking along the way.
I think the traditional approach to a lot
of problems around customer experiences, people
can solve them.
But, we are at a very interesting junction,
I think.
I think customers are expecting more and more
personalization at scale.
Think about our experiences when we engage
with a company like Amazon?
We're not only getting a very personalized
website; we're getting very fast transactions
in terms of delivery of service.
But also, when we go and reach out, we get
extremely personalized outcomes in terms of
how they're treating us.
This concept of personalization at scale is
something that we would see more and more
of a trend moving forward.
The great thing about being at a company like
Pinterest is we're still building everything,
so we have an opportunity to look at what's
coming in front of us and rethink.
I think my philosophy has significantly evolved,
and at least my first way of approaching this
is, can we use technology to solve problems
first?
Let's take a simple thing, something that
we are working on at this point in time.
A lot of our questions tend to be very basic
questions.
Now, choice one could be to say, "Hey, we
shift costs and look at an option like outsourcing."
Cheaper but, effectively, we are shifting
costs, right?
The second option could be to invest in chatbots
that are powered through machine learning
that are building intent models using our
help center and our previous questions answered
and give a very different experience.
I think the approach that I am looking at
in my evolution in this journey is, how do
we solve the technology first?
If there's no option with technology, how
are we building the technology and the data
to get there?
And, if all else fails, then we truly look
at adding people, adding processes, et cetera,
and solving that.
That's the big pivot that I think I have made
through my career and journey, not just in
customer operations, but looking at all the
functions that I have been a part of.
Where are we in the evolution of being able
to use technology in order to provide those
kinds of better customer experiences without
having to add more people?
Like with chatbots, sometimes it seems like
it's still fairly primitive days.
I think it is very primitive days.
We are just getting started.
But, the good thing is the underlying, the
underpinning technology is there.
And, like anything, there is a transition
period and then it gets better.
I almost like to compare it to, like, not
that long ago, before Siri was acquired by
Apple, Siri was like an independent app and
it did some very basic things.
The evolution from that to the way Siri is
in an iPhone or the Google assistant, Alexa,
or Cortana, the evolution that we have seen
in the past three, four years, I think, has
been significantly augmented and different.
I think we're going to see the same sort of
uptick.
A little bit of my role is to look into the
future and make investments in a way that
we are not loading it today in a way that
we need to make completely drastic decisions
tomorrow.
What kind of investments are you making?
I'm not trying to dig down into the deep secrets
of Pinterest.
Although, if you want to share the deep secrets
of Pinterest, that's good too.
But, what kind of investments are you making
today?
Again, how do you draw the balance?
Absolutely.
We are definitely evaluating.
At Google, we were able to do a lot of this
stuff.
At Pinterest, we are also looking at really
making sure that we have the right basis of
data so that, as we evaluate and introduce
technologies based on machine learning, et
cetera, we are able to leverage and get the
outcomes quickly.
One of my biggest learnings from my past is
it's not the algorithms; it's the data that
matters.
Having a good foundation of data is what will
ultimately power this.
Just taking some sort of a chatbot solution
and implementing right away will have, I think,
some benefits, but not really significant
benefits because, effectively, what we're
trying to do is copying some version of the
phone tree into a chat system.
What makes it more powerful is if we have
all the related data and then we're building
a true intend model based on how we have actually
answered questions, how we have actually solved
questions, what kind of artifacts we give
to customers to get them started, onboard
very easily, and then build that into the
real-time interaction like a chatbot at this
point in time.
That's one of the areas that we are exploring.
But, what we're really focused on is actually
building that foundation of data at this point
in time so that, when we start to introduce
these technologies, it's not an incremental
benefit but a significant step up.
Can you be more specific about the kinds of
data that you're trying to aggregate right
now?
I think that's really interesting.
Absolutely.
I think, when we look at data, I sort of see
three big buckets.
One, there is an operational view of the data.
Think about all the transactions, interactions,
et cetera.
At the end of the day, things need to be tied
in.
Everybody is collecting them in their own
silos.
It's very hard to join the space of data.
That's the foundational element of what we
want to do, and that's what we're doing right
now.
The second, I think, is the customer view
of the data.
Okay, we are doing these activities.
Is it leading to better outcomes?
I always say, "Just because I showed up, is
that actually good or this was actually going
to happen regardless?"
Right?
We need to understand, through some statistical
modeling, what correlation, what kind of services,
what kind of interactions can be attributed
to better outcomes?
That's the second stage that we are working
on.
The third stage is really sort of looking
at a more business-centric view, which is
more of a business decision-making.
But, the combination of understanding what's
happening in the operations to how our customers
are spending, what our win rates are, what
our churn rates are, that's the big connection
that we are working on building at this point
in time.
That type of view enables you to connect the
data that previously was isolated in silos.
That seems like the magic bullet, ultimately.
Yeah.
I really wish it was a magic bullet, but that
is, I think, the step in the right direction.
[Laughter]
But, it seems like, from what you're saying,
that being able to connect data from these
multiple perspectives ultimately is the way
to really understand the customer and, therefore,
build the kind of predictive models that you
need in order to have machine learning enabled
chatbots that become more than just fancy
phone trees.
Exactly.
If you think about it, if we can give somebody
a very helpful set of tips on what kind of
creatives work at Pinterest, knowing that
they are somebody who is a specialty retailer,
it's a great insight.
It's something that we can surface and give
it to them right at the beginning versus having
lots of conversations and then giving it to
them downstream because that's going to have
a meaningful impact.
It's going to probably stop them from spinning
their wheels, doing things that are not going
to work.
But, we need to unpack and understand what
is it that specifically will drive those great
outcomes for our customers.
In a case like that where you're talking about
content and aesthetic decisions, as well as
financial outcomes and correlations or causations
relating to, "Okay, yeah, do this type of
ad and you get this number of clicks or results,
click-throughs," how do you determine the
outcomes, especially again on content and
aesthetic decisions?
How do you even approach that?
The good thing about content is, especially
with a platform like Pinterest, you have two
very good measures/proxies.
One is you click on it and you go somewhere.
If it's working, we know somebody will click
on it.
The second one is, if somebody likes it, hopefully,
they will pin it.
Those are two very concrete actions in the
system that we know, over a period of time,
if we collect the data and correlate that,
we will be able to give more concrete recommendations.
It's not like we don't have these recommendations.
It's making these recommendations and making
them extremely relevant when the customer
needs it.
It's combining; give them the right information,
understanding where they are so that we can
establish the right time to get that information.
Those are the three pillars that we're trying
to connect together at this point in time.
You're always looking at the data in relation
to the customer, the desired customer outcome:
pinning it, higher ad spend, or clickthrough
rates.
Would it be safe to say that when you're talking
about these aesthetic decisions, the machine
doesn't understand aesthetics; the machine
understands that if you do a thing a particular
way that there's a higher likelihood that
that item will get pinned, for example?
Absolutely.
Absolutely.
I think you summarized it very well because
machines can't really tell you if something
is good or not good - not yet.
I'm assuming that a technology is going to
come at some point in time, but not yet.
It can recognize patterns.
It can tell you it's a banana or an apple,
but it really can't talk about aesthetic attributes
yet.
Then, part of it is, what is beautiful to
me is going to be very different than what
is beautiful to someone else.
Beauty is in the eyes of the beholder, and
that's what we are trying to suss out through
data.
Okay, if it is beautiful, what are you doing
with it?
That's either a click, a pin, or some kind
of an engagement in the platform.
I guess it raises the very philosophical question.
Ultimately, can we isolate the qualities of
aesthetics into a set of rules if we have
enough data?
But then, even if you do that, those rules
ultimately are always outcome-based in terms
of decisions and you leave out the unique,
innovative, brilliant combination or invention
that somebody might come up with aesthetically
that is really great, but nobody ever thought
of it before.
I think, when people talk about artificial
intelligence, they get this view of Arnold
Schwarzenegger coming out and killing people
in Terminator.
Then the reality is, these tools and technologies
are really more assistive.
They can help us point in the right direction.
They can help us suss out noise, patterns,
et cetera.
But, at least for now and what I know, it's
definitely not at the stage where it can truly
create rule-based systems and just automate
everything out because aesthetics is so personal,
and it is so context-driven.
If you're an advertiser like Axe, you are
probably okay with something a little bit
edgier than probably a brand that's a little
bit more conservative.
Again, it's like all this dimensionality that
is very hard to suss out and just put in an
algorithm.
But, regardless of the kind of brand you are
or your aesthetics, if you're a brand on Pinterest,
you want people to be pinning your materials.
Absolutely.
And that we know.
[Laughter]
That we know for sure.
Exactly.
Okay.
Well, what a very fast little more than 45
minutes this has been.
We have been speaking with Dutta Satadip,
who is the global head of customer operations
at Pinterest.
Dutta, thank you so much for taking your valuable
time in being with us here today.
Thank you so much, Michael.
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