When we compute on a person,
we can actually use AI to derive,
this is gonna be the most optimal set
of objects that we may want to recommend to them.
(upbeat music)
-Hi, this is another episode of "Powered by Snowflake,"
where I interview technology leaders
who are building businesses and applications on Snowflake.
Today we will be learning about how Zeta
has built an AI-driven marketing platform.
And with me to talk about this is Ryohei Okabe.
Ryohei, how are you today?
- Hey, Daniel, I'm doing well.
- Awesome, awesome.
So, you know, to kick this off,
tell me more about what exactly
is your background in this space and what is Zeta
and the problem that Zeta solves?
- Sure, so once again, my name's Ryohei Okabe.
I'm senior director of product over here
on the Zeta Marketing Platform.
I'm normally based out of our New York office in Manhattan,
but more recently out of New Jersey
either my living room or my daughter's bedroom sometimes.
From a product management perspective,
I really focus heavily
on the data connectivity and activation on the platform.
So we're gonna see a little bit of that
but I've more broadly worked
with data-driven marketing platforms for over a decade.
From a Zeta perspective, we were founded in 2007
by our current CEO, David Steinberg,
and John Scully, ex-CEO of many companies.
And from day one, what's carried all the way through
to today is the concept of data-driven marketing.
And along the way there,
we realize there's just so much data.
We need to bring in more layers of AI to make that easier.
And so as the amount of data increased, we put more effort
into making that data easier to use for marketers
so that they can reach their customers,
acquire new customers, grow their existing customers
and retain their existing customers as well.
- So that's a pretty powerful platform that you built.
How did you land and choose Snowflake
to build this platform on top of?
- Yeah, so that's actually a great question.
We originally started with a pretty modern
cloud-based infrastructure
without Snowflake, and we assessed Snowflake
and realized there were so many benefits
that we actually went through and transitioned
and moved a lot of our compute and storage,
basically all of it, onto Snowflake.
So the way data is structured on our platform,
we have a centralized data cloud
of Zeta data that allows us to drive interests
based on billions and billions of signals.
And on top, next to that, kind of parallel to that,
we also have basically our clients
bring their first-party data sets of customers.
And so all of that now exists
in Snowflake. So much processing
and so much compute happens on all aspects of this,
whether you're working on just your first-party data
or just within our own data cloud.
And then when we match against that data,
we use the interest arrive from that data cloud
to better serve our clients and their customers.
- That's awesome, that's awesome.
And so I saw that you guys
recently went IPO last year.
Tell me a little bit about that,
and kind of the journey that you've taken
from pre-IPO to now you're IPO.
How has that changed the company and the product?
- Yeah, that's another good one.
We really we're investing so heavily
in the product for really the last decade,
but that's continued to grow exponentially.
And about in 2017, we acquired a company called Boomtrain,
which was entirely an AI-driven marketing automation tool,
but was focused more on small business.
And we actually took the modern technology out of that.
A lot of use cases that we had
with our history and database management
and email marketing and marketing automation in general,
and combined those use cases
into a single, powerful platform, Zeta Marketing Platform.
And so pre-IPO
we actually were building this platform,
putting it through analysts reviews
and we actually ended up doing quite well
on a few analyst reviews overall with our CDP and our ESP.
And we felt it was a really good time.
We already had tons of enterprise clients
large-scale, pushing terabytes and terabytes
of data, computing terabytes data on Snowflake.
And we wanted to really get ourselves
into a position to continue to grow more by going public.
- That's exciting, so one of the things I always ask folks
is I want to get hands on.
Can we see a demo here today?
- Yeah, absolutely.
So what we see here is the Zeta Marketing Platform.
I'm going to start in the context of a single person.
This is a helpful storytelling tool.
This will give you a sense of the type of customer data
that a business may be wanting to keep
on their customer, so that they can communicate
with them cleanly,
whether it's for marketing or customer service.
You know, one of the most frustrating things
is if you call customer service
and they can't figure out who you are
and they need to know exactly what you bought.
And when they get that wrong or they say, "No,
you absolutely got this message," or they have no idea.
You know, all that discrepancy creates a lot
of confusion and a lot of pain
in that relationship from the client to the customer.
And so what you'll see here is the data.
All of this data here is sitting within Snowflake.
While it's one person, for many clients
it's tens of millions,
potentially hundreds of millions of these customers.
And we'll also see various Snowflake compute
and data that we use on all of this underlying raw data.
So here, we're taking a look at an example customer here.
We're pulling out some key properties.
But you can see a whole stream of events.
With our platform, we have a site integration,
where clients can basically
put a pretty lightweight JavaScript beacon
on their site, and you get your basic site metrics
but we also have APIs and API file-based ingestion
as well as Snowflake shares to receive events
and activities on a person.
All of the activity that we get
on a person basically also comes
with a pretty hefty amount of metadata.
So I can see here, even on this site visit,
you can see all the metadata that's tracked in that event.
And so we actually have a seamless data structure
that Snowflake allows us to set up
and really scalably allows clients to bring in any new data,
make changes on their site
and capture it all the way through.
You'll see other events in here as well
because our platform takes all that data,
processes it, computes it, makes decisions
and then allows you to even message the customer.
So there is a full email platform, SMS capabilities,
and push capabilities in this platform.
In addition, we have a DSP for programmatic,
and we're also releasing a brand new
site personalization tool
on top of existing site personalization capabilities
that we already have.
On top of that event data, we also have, for each person, the data that you might bring in.
Really basic data that you might have
Because you do need to communicate back and forth.
You may need to store that data.
You may base some targeting and messaging
based on location or flags, engagement,
any data that you want.
And all of this data can be passed through
and with additional metadata or as nested JSON objects.
So it can be super flexible
in terms of how we store it.
On top of that, all the events
and the existing properties allow us to also
derive Zeta properties.
So these are additional properties
that are kind of flat values
that are easy to target, easy to use.
And we always think about not just getting the data
in one place, but bringing it to a point
where somebody could just make a simple decision on it.
A flag. A yes/no flag.
A score that would say, "Hey, we have
a high propensity likeliness to score or churn."
All that data gets processed in here
or brought in and combined
with other data to be processed further.
Per person, we also have this whole setup of AI-driven recommendations.
So for different clients, for retail,
many cases it's products. For publishers it's articles.
And sometimes it could be really anything you want.
It could even be stores, agents
whoever you may want to work with.
If it was real estate, it could be properties.
This freeform structure allows us
to handle all these objects with all this metadata.
And then when we compute on a person,
we can actually use AI to derive,
this is going to be the most optimal set
of objects that we may want to recommend to them.
And then we do that regularly.
And when you go to use the recommendations,
you can say, "Hey, I want to grab some subsets."
So maybe I want to grab similar products
or accessories or something within a certain category.
And you can use all of this,
really drive some really personalized messaging.
- That's pretty powerful.
So one of the things I saw
on your website, for example,
is how the data flows, right?
So you're ingesting a lot of data to be able
to make these types of insights.
Tell me a little bit more on what are
those data points that you're ingesting
and how are you actually getting that data into Snowflake?
- Yeah. So, really, all the data that ends up
in the platform is on Snowflake.
Because that's where we're actually storing all the points
of the customer data.
So we do get that data in a number of ways.
We have a full set of real-time APIs,
and that's if you need to get the message in immediately
and also react to it immediately. In many cases,
that message is going to come directly from an API
from some other system or from the site integration.
And so we'll trigger maybe a direct confirmation.
For batch data, we have our data conductor
where we can actually grab data
from various sources and pull them down into the ZMP [Zeta Marketing Platform].
But we can also use the data connector to actually pull data
out of the ZMP or various Snowflake sources
and make it easier to connect with different systems.
And so with our clients,
we have many clients on Snowflake
that we have some smooth data shares with.
But there're also clients
that aren't on Snowflake outside of the ZMP.
And so their data's sitting in Snowflake on the ZMP
but they need to get data from other sources,
bring it into Snowflake.
They need to get data in Snowflake,
bring it to other places.
And so we understand there's this whole network of data
where, within the data connector, we allow
for instance, the ability to pull directly
from a Snowflake source, write a query against that data--
so you can pick a preexisting query
running on a recurring schedule--and you can actually
take that data, run further transformations on that data,
whether you need to mask something
or maybe do some other
with some other set of data.
And we'll just do something...
let's see...and we're masking some data in here
pull some fields in here and call it masked email.
- And as you're writing that,
for the viewers that might not be familiar
with what a Data Share is
can you explain from a Snowflake side,
what is the Data Share
and how exactly does that benefit customers
and Zeta in this case?
- All of our clients, traditionally,
when you're moving large sets of data, you're moving files
CSVs, separated, tab delimited,
pipe delimited files that are gigabytes and gigabytes large.
And those take hours and hours just to transfer.
And that's assuming that the file already exists.
If somebody has to build the file, push it,
somebody else has to ingest it.
With a Snowflake Data Share,
what's amazing is that you can take that whole data set,
not even just what we transferred,
because we always have to cut that down to keep it small,
but a larger data set
and just directly access it, which is really amazing
because we can just take that data set and say, "Hey,
you know what? We need to make sure
that this goes directly into the Zeta data or vice versa."
And so the idea here is that's simply share,
and that data's not even moving. We're just going
and getting what we need when we need it.
And so just the level of efficiency there
for our clients has been really amazing.
And we do it both ways.
- And that's beautiful.
I mean, the amount of time and effort and energy
saved in that type of paradigm
really enables a lot.
I mean, not to mention, with the level
of data governance that you now have access to as well
with these types of Data Shares with Snowflake, right?
I mean, you'll be able to ... the customer
in this case still owns their data and they can
control access to it, govern it how they see fit,
while providing a data share capability with Zeta.
So that's pretty cool.
- And so really the last part of the platform that I really want to show you
is our experiences and how we process all this data as it comes through.
And so those events that we remember ...
I'm going to create a nice little example here...
This interface allows our customers
to create real-time and in-time customer experiences
that are just ever living.
And so as a client does something
you can create a decision tree
so that users move through this entire flow.
So we'll pretend that somebody logged into an app.
And we do have an entire ability to track unknown people.
So with our site integration, you can even
in an app login--it would not be in this particular case here--
but with maybe something like a site visit,
you may want to still pass them
through a general experience.
And so start with an event that they've done.
We can add a delay.
We can add other events in sequence, say, "Okay,
they did this and did this, or did this and did not do this."
We can add splits, but ultimately we're doing all this
so that you can determine how to communicate
and message a customer.
And so we can say, "Okay
they logged into the app and maybe really, basically,
we need to set up an email response to them."
And in this case, they have a full-fledged email template builder
in the platform where they can say, "Hey,
we need to flush out all the message details."
Add the dynamic logic in here and then send that message.
But we also have a full set of channels
that you can tap into where you can reach
into both our native channels
or custom channels per account that you can set up.
And we have a whole suite of channel templates
you can actually tap into to say, "Hey
this is how I want to reach these different customers."
- So from here, what's next for Zeta?
Where do you see the product
six months, one year from here?
- Really six months a year from now,
we're continuing to invest more heavily
in our connectivity and ease of use of that connectivity.
And so recently we've introduced things
like our data connector, our channel manager,
where we're focusing on no-code interfaces for the marketer,
with a low-code interface for an initial IT admin
or internal solutions architect that can actually
map that end-user experience more smoothly.
We actually ... that interface
is a full form builder that allows
the marketer to just select from drop downs
where somebody's actually preemptively mapped
those drop downs and those input fields
to API calls, to what we internally
have called is primer templates
that we can map to data flow changes.
But also in 2022,
we're targeting a UI to allow clients to segment directly
on a Snowflake data warehouse outside
of the platform, where currently you can query against it.
And we also have a clean UI drop down, a drag-and-drop UI
within the platform for your data sitting in the CMP.
What we want to do is make sure that that data
that currently requires queries to be written,
to be able to be accessible for the end marketer.
No SQL. Drop downs. Reach your customers
without moving the data out of your data warehouse.
- That's exciting.
That's exciting to see. For our viewers here today,
if they want to get in touch with you
or learn more about Zeta, where should they go?
- I recommend just going directly to our website
at zetaglobal.com.
That's ZETAGLOBAL.com.
And there you'll see white papers and details
about the platform and the ability to contact us directly.
- And for everybody watching here,
if you're looking to build your next application or business
on Snowflake, check out developers.snowflake.com.
My name is Daniel Myers
and this has been another episode of "Powered by Snowflake."
Thanks.
(gentle music)