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BRUNO AZIZA: Welcome to another episode of Data Journeys.
I'm Bruno Aziza.
And today, we're going to talk to Scott.
He's the VP of Data Analytics at Wunderkind,
and he has a lot to teach us about their deployment.
He was at the company where they had eight employees,
and now they have over 400, an amazing data journey
with data and analytics.
Scott, thank you so much for taking the time
to talk to us today.
Let's start with the basics.
What is Wunderkind?
SCOTT SCHAEN: Yeah, absolutely.
Thank you, Bruno.
So Wunderkind is a digital marketing SaaS company.
We're a leading performance marketing channel
that delivers one-to-one messages at scale.
We realized that our clients have a lot of unique end users,
and they deserve to be treated like people
and based on their user behavior and who
they are and their interests and their disinterests.
And so each month, we're sending over 5 billion
one-to-one messages to users on our clients' behalf.
And our clients are some of the largest digital brands
that exist, both e-commerce and publisher.
BRUNO AZIZA: So this is really about on-site experience,
creating more value for visitors and, of course, more values
for people providing services/products
to those visitors, and data understanding intent
is a big part of that.
And so tell us a little bit about the use cases
where you are using data analytics today.
SCOTT SCHAEN: Yeah, absolutely.
It's really about understanding end users,
understanding what they're interested in,
understanding what they're not interested in,
appreciating that they are unique,
and they have unique interests.
An example on site might be we see you've been to the site
before, whether on that device or another device,
and we might make a recommendation
based on previous purchases.
We might also use your intent and realize
you've been looking at this pair of socks for a while.
Maybe we should ask if you're interested in choosing a size
or choosing a color or adding to cart,
and then we'll extend that off site to both email and text
and try to give our end user something of value
and a positive experience.
And so that might be, hey, here's a list of all the things
you looked at, or, here's a list of all the things
that you've looked at that have since dropped in price
or are back in stock or even low in stock.
And so at the end of the day, we're using data to understand,
who are these people?
What are their behaviors?
What are they interested in?
Do they like receiving emails?
Do they not like receiving emails?
And trying to tailor to them as if I'm working in a local shop,
and you came to visit, and you come to visit all the time.
I know what you're interested in,
and I know what you're not interested in.
And you appreciate doing business with me
because we know each other.
BRUNO AZIZA: Yeah, so you do this at a large volume--
5 billion one-to-one messages, a few hundred million people
you interact with.
You do this in real time, so you have
a user of Google technology--
BigQuery, Pub/Sub, Bigtable, Dataflow, of course.
And so you've got a few things going on.
One, you're empowering your own products with data analytics.
But what are some of the other areas where you're
using data analytics-- client reporting, internal enablement
or empowerment?
Tell me more about the other areas
where you are using analytics today.
SCOTT SCHAEN: Absolutely.
So I touched on the fact that there's
a lot of interaction between us and end users.
So data plays a critical component there
or is a critical component there.
And the next would be client reporting.
So we are running a lot of different strategies
and different campaigns for our clients.
There's a lot of things that are both rules based and machine
learning based or AI based.
And so we help our clients understand
what's really adding value, what's really
moving the needle, what are the strategies that
are most effective.
Helping them understand things like incrementality,
giving them insights and opportunities
for their own sites--
that might be things like we see a lot of friction
in this area of the funnel, and you could potentially
improve that by doing X, Y, and Z.
And maybe it's something with their checkout flow.
So that's a fun one.
And one of the challenges is our clients are very different.
Even though there's a lot of e-commerce and publishers,
even among e-commerce, they vary so dramatically
in terms of KPIs that they care about,
in terms of data that they're interested in.
And so we do get a lot of custom asks, as our clients think
of us as thought leaders and know
that we have such a rich understanding of on-site user
behavior and off-site user behavior.
And so that is a real challenge, but something
that we're able to keep up with really nicely,
thanks to BigQuery and thanks to having Looker set up
in a very optimal way.
The next area in which we're using data--
I would call it internal empowerment.
Everything we do is driven by data for the most part.
We do not run campaigns or run strategies
for the sake of running campaigns and strategies.
We're running it because we have reason
to believe that it's adding value,
and then we will confirm it's adding value.
It's doing what it's supposed to do.
It's offering our end user something,
and it's making our clients happy.
And so we innovate at a pretty rapid rate.
Bruno, you and I talked about a few of our products.
And really, that was just a few because there are so many.
And our analytics team tries to keep up with our innovation
and make sure that every single new feature,
every single new product, we understand
how it's really working, and where it's working,
and where it's not working.
And if it's AI based, we want to know
what are the machines doing?
What are they thinking?
And again, we're able to do this, thank you to BigQuery,
thank you to Looker, and thanks to some really intelligent
design and great people.
BRUNO AZIZA: So I want to talk a little bit about business
intelligence, and then we'll talk
about artificial intelligence and machine learning.
How are you using BigQuery and Looker together
internally and externally?
SCOTT SCHAEN: Yeah, absolutely.
So internally, everyone's using Looker, almost,
I would say, the entire company.
Thankfully, they don't have to really think
about where the data is coming from, even if it is BigQuery.
And the way that we've been really successful here
is understanding who our end users are-- which
are our employees, but there's a number of them--
and what is the data that they're really interested in.
So when it comes to Looker, there's
a lot of ways to share data.
And what we haven't done a ton of
is the ability to really explore data from scratch
and build something out.
What we rely really heavily on is purpose-specific dashboards.
And the reason this has been effective
is because we can make sure it's really answering our end users'
questions.
It's really getting at the heart of what they care about.
So, for instance, we send a lot of emails
on our clients' behalf.
And so our clients have been asking,
how many people receive more than one email
in a given amount of time?
Or more than two emails, or more than three emails?
And that's something that's pretty easy to do in BigQuery.
And so we figured, OK, then it should
be pretty easy to do in Looker.
And we gave it some thought, and we figured out,
what is the best way to display it?
What is the best narrative to add to it?
What is the best way to show it back to our clients?
And so that's what we ended up with is
a pretty beautiful dashboard.
But forget about the fact that it's beautiful.
It's actionable, and it's meaningful,
and it gets to the heart of the problem.
BRUNO AZIZA: That's excellent.
And, of course, you are using BigQuery machine learning,
and so tell me a little bit about what
you're trying to do there around anomaly detection.
Why does this matter for your employees
and also your clients?
SCOTT SCHAEN: So we're using machine learning
in a few different areas, both to empower our product,
and more recently we're using it internally
with analytics for anomaly detection
and catching off-looking data.
So, for instance, we may notice that one of our client's
websites has a dramatic spike in visits
or a complete dropoff in visits, and that's something
that we want to look at and make sure, is this legitimate?
In the last year or so, a lot of newsworthy events
have happened, and so our ARIMA alerts had went off.
And immediately, when I see that, I go,
oh no, there's something in the news.
And sure enough, there's something big in the news.
And ARIMA has been pretty amazing for anomaly detection.
Again, it's working in conjunction with Looker.
We've gotten some really nice charting
to make our alerts very actionable.
I think the challenge now for us with machine learning
is that BigQuery has so much of it with BQ ML
that we want to play with it all,
and we want to explore it all, and we just
don't quite have the time.
BRUNO AZIZA: Well, that's a good problem to have.
So we've talked about the sophistication of your use
cases.
We've talked about how, really, you're
a data-driven organization.
Data is the differentiator, and your ability
to interpret the data to understand intent
is a great value proposition for your company.
And given you've had eight years of experience in building this
amazing platform, let's get into the dos and don'ts now.
Let's get into what you would tell yourself
at the beginning of this journey you absolutely must do.
So let's start with some positive dos.
SCOTT SCHAEN: So for the dos, when it comes to modeling data,
the analytics team should be involved, especially
if it's analytical data.
And you want to give it thought.
You absolutely want to give it thought and think
through most of the use cases.
But you're never going to nail it.
It's never going to be perfect.
So at some point, you've got to say, all right, it's time.
Let's do it.
That's not the do.
The do is plan to continuously improve.
Plan to refine your data model.
Live with it for a few months, however long
it takes to learn from it, but then improve it.
And the way that I think about it
is if I had to teach a new employee how to use the data
set, how many weird nuances and caveats
would I have to explain?
And if there's too many, if it's not intuitive enough, then
probably there's some work to do.
So that's do number 1.
Do number 2 is an obvious one-- garbage in and garbage out.
So set aside time for data integrity and validation.
I believe greatly in redundancy.
As an analytics leader, I want to have
my own alerts and my own test setup,
regardless what the broader engineering
team has because I notice when I need the data most,
I don't want to be wondering how accurate it is.
I want to have confidence that the data is
as accurate as it can be, and it is what I believe it is.
And then finally, do number 3 is data enablement.
And data enablement comes from really
understanding your stakeholders and their needs.
It comes from an actual conversation, if not
many conversations.
And it doesn't happen just by giving out raw access or access
to large quantities of data.
It is very easy to misinterpret raw data,
as I'm sure any data analyst can vouch for.
And so I wouldn't want to put my stakeholders in a situation
where they can run with inaccurate data.
So talk to your stakeholders, understand their needs,
and focus on intelligent design.
BRUNO AZIZA: And so that gets us to your favorite expression,
which is, there are many ways to eat a kiwi.
And I think your don't is don't give everyone access to SQL.
SCOTT SCHAEN: Yeah.
BRUNO AZIZA: Tell me more about that.
Like, how do you parse through the various personas
and the type of access you're giving to your users
to the data?
SCOTT SCHAEN: So I think, internally, I've
built a reputation as being a little bit
of a stickler with regards to giving people SQL access.
I love SQL.
I want other people to love SQL too.
However, it's not the right solution for everything.
So there's a lot of very basic or very repetitive
types of asks that we get, and we should be answering those
consistently the same way every single time.
Back to many ways to eat a kiwi, in this case,
we should be eating the kiwi the same way
every time because a lack of consistency
will get us in trouble.
So if every client that we have had a different understanding
of how many sends they had or how much incremental revenue
we drove, or every time they asked,
they got a slightly different answer, things would be bad.
So the reason that I don't like giving out
SQL access to everyone is not because I
don't want them running SQL but because I
want them to approach problems similarly
in a way that's easy for us to manage
as an organization and works with our narrative.
BRUNO AZIZA: And so do you then give access
to particular personas?
So you're picking maybe a data scientist has access
to this type of data in this interface,
and the business analyst has access
to that type of data with that interface?
How do you even start deciding who gets access to what?
SCOTT SCHAEN: Yeah, it's a great question.
It's very much team oriented.
So, for instance, the analytics team
is-- outside of, let's say, engineering,
the analytics team is really the only team
that has access to SQL and the ability to run ad-hoc SQL
queries to answer questions.
And then around us, we have a few teams that I would say
are power users of Looker.
And they have more open access to Looker,
the ability to explore, in Looker terminology,
to explore and build out their own reports.
And that's based on a lot of trust and a lot of education
and also because they do--
not so physically, but they do sit very closely to us
and work very closely.
On the outskirts, we have our accounts team
and our sales team, and that's where we just
can't afford to run with incorrect data or data that
does not get at the heart of the problem.
And so for our account managers and our sales team,
they have access to dashboards, where the dashboard
is purpose-specific and answers one type of problem,
but it can do so in a very flexible way.
BRUNO AZIZA: Thank you so much, Scott, for the insights.
We learned that the art and science of modeling-- it
changes, and that you have to build at the beginning
to make sure you have the ability to modify these models.
We also learned how to provide access
if you're a data scientist, the data analyst and type
of interface that you need.
I feel like I've learned a lot from you in this short segment.
And I hope people will reach out to you to learn more from you
directly.
If you're interested in more stories just like this one,
click on the link down below.
Until next time, I'm Bruno Aziza.
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