We want to make sure that
we find as many of these opportunities for customers
and we make it easy to save.
This allows us to quickly filter down to say,
"Oh, these are the clusters that need our attention."
They're getting a lot of searches,
so your customers care about it,
but it also is not getting clicked a lot,
so there has to be some issue there with engagement.
And so we want to make it super easy for someone
that comes to the screen and just completely understand
what's going on.
Welcome to "Powered by Snowflake,"
where I interview technology leaders building businesses
and applications on Snowflake.
I'm your host, Daniel Myers.
And today I'm talking with Mike Peralta,
Product Lead of Analytics at Yext.
Yext is the AI search company
which offers a modern AI-powered answers platform
that understands natural language,
so that when people ask questions about a business online,
they get direct answers, not just links.
Mike, how are you today?
Doing good, how are you doing?
Doing well, doing well.
So, I want to learn more about your background
and your role at Yext.
Yeah, so I've been at Yext for about
almost nine years now. Actually it was my first job
out of school.
So I graduated in 2013 and started working here
when it was about a 200-person company.
And we were primarily focused at the time
on one product called Listings.
We've grown a lot since then.
But, since then I started out in a data operations role.
From there, grew to take over the business intelligence team
or start the business intelligence team here at Yext,
was working on our search product for a little bit,
and then, finally, took over analytics
and all the customer facing analytics that we see
in the platform today.
So, a bunch of different data roles over time,
and it's been fun.
That's awesome, that's really exciting.
So, explain to me what exactly
is an AI-powered search platform.
Yeah, that's a good question.
So, I think one of the things that we noticed is that,
we, over time, had a bunch of this data about our customers
and their businesses, and all of the things
that people would want from Google, but we didn't have a way
to power that same type of experience on people's own sites.
So if someone wanted to get information
about a business, they'd have to go to Google, or Bing,
or some other search engine to get information.
That gave people an opportunity, customers an opportunity
to get sniped by other businesses
because they're showing up in search like everybody else.
One of the things that customers really own
is their own site, and being able to deliver answers
on that site, and being able to help people get the
same experience that we expect from Google,
but in a place where you fully own, is super important.
And so we thought that was an interesting place to get into.
And because we had all this data already
about these businesses, it was kind of a natural extension
of what we were already doing.
That's cool to hear. You mentioned that you were one of
the first 200 or so employees.
You've been here for nine years, that's amazing.
So looking at it, you've been there since before IPO.
How have you seen the progress and, over time, improvement
of Yext? And how has going IPO changed the roadmap?
Yeah, it's been really cool to see.
I mean, it was interesting, like I said,
when we first started. It was a single-product company,
for the most part, focused entirely on Listings
and getting our information out on Google
and places like that.
But, as the company's grown, I think we've realized
there's all these other places that we can sit
for businesses. We have all this information.
It's just about making it accessible to people.
And so we've started to find other areas like,
developing webpages for people, doing site search
for people, all these tangential things,
which all expand on the same idea of,
we have data customers who want their consumers to find it,
so how can we do that?
So it's been cool to see how we've continued
to be able to expand from out of a very core idea.
As far as how's the company changed,
it's interesting. At 200 people,
it was a very small company, it was very close.
We all were in one office in New York.
And as the company grows, you start having more people,
you start having more offices and stuff.
And so it's interesting to see how that scales over time
and how you go from being
the small little scrappy startup, to a public company.
So I'd say culture changes
are probably the biggest thing,
although I think we've done pretty well with that.
And I think, besides that, I would say not much has changed.
I think the IPO is a cool moment for us
because it's like pre- and post-IPO.
You feel like a much more mature company.
It is a big step in a company's life cycle.
But I think, it's just a day on the calendar after a while:
You do it, you become public, and then you have
to keep executing on what you were doing before.
Awesome. Awesome.
And so, with Yext being built on Snowflake,
can you talk about how that architecture happens
and dive into a little bit on the process that you use
for ingesting data and the application solution
that you've built with Yext?
Yeah, so it's interesting.
We have a lot of different ways we can get data at Yext.
It depends on the product,
and the way and the service we're offering.
And, even within some of those products,
we may be getting data in different ways.
At the end of the day, it all ends up in Snowflake.
As an example, for our Listings product,
we kind of have two tiers of search engines that we sync
or sync data into.
Some are very large, like Google, Bing, and they have
their own APIs, which we need to build integrations with
to pull that data out of there,
and then store in our platform so that our customers can see
how they're performing there.
In other cases though, we work with very small publishers
who don't have that kind of infrastructure,
and we have our own pixel integration on their site
that can stream data from them in real time.
And so we can get that analytics
out of those publishers as well.
So those are kind of two cases where we'll either use an API
or maybe a pixel integration to get data,
but that's mainly for third-party stuff.
There's a whole host of stuff that we have
that's more first party.
And so that would be our sites products, which allows people
to create websites and to distribute information
about their business.
And then also our search product, which allows people
to have that AI search we were talking about
on their websites.
And so both of those things are first-party products
that we have a lot more control over collecting data.
So similarly we have endpoints that allow us to collect data
from there, and that data also ends up getting streamed
into Snowflake as well.
So, a lot of it is just either ETLs that we're using
for third-party tools, or our own pixel integrations
that allow us to get data off of these sites immediately,
and then send them straight over to Snowflake.
Cool. That's cool to hear.
Can we see a demo today?
Yeah, of course.
So I'm going to walk you through a couple of features
that we have today in the platform
that are all analytics related
and are all powered by Snowflake.
So Snowflake sits at the core of all of our analytics,
and it's actually expanding a lot to grow to other areas
of our business where we're storing much more data.
So here, one example of this
is a product we call Search Term Clusters.
So the goal of this product is to take all of the data
that customers are getting on their site search,
and figure out a way to make sense out of it.
So depending on the site you have,
you may be getting hundreds to thousands,
to hundreds of thousands of searches every day.
And people are typing in different things that they want
out of your business.
And all of that is valuable information,
but if we can't figure out a way to take that noise
and make it understandable, it's not helpful.
So, one of the things we found is we needed a way
to take similar questions and things that people are asking
and to group those together based on the themes
that they represent, because that allows people to scale
how they look at their business, and then understand
how they can use that data to make better decisions.
One area that we saw this was huge for
was government COVID sites that we had been running,
where they got a significant amount of questions
and a lot of them were new, things they'd never seen before.
So, they needed a way to scale all of these intents
that people had,
and that's what we were looking to solve here.
So we built this product that you're seeing here,
which is pretty simple.
All it does is it takes machine learning
and looks at all the searches that happen on experience,
and group them together based on their intent.
So, as you can see here, one we have is talking
about duplicates in Yext.
So, a lot of the times we'll create listings for people,
but there may already be a listing out on Google
or something that represents a business.
And that could be incomplete information,
and so we want to claim those things, and then remove them
from the internet so that only the correct information
from the business is powering things.
And so in that case, there's a lot of different ways
that people can ask questions about that service, right?
They can ask, how often do we scan for duplicates?
How do we suppress them?
All things like that, but they really mean the same thing.
Customers want to understand how this feature works,
they want to understand what we offer in this space.
The same could be said for adding an entity.
An entity is kind of our core object in our platform.
So you can think of an entity as a location,
like a store around the corner from you, a flower shop.
You can think of entity as a product.
It's kind of just our core object that we have
in the platform that you can then make accessible
on Listings, on Search, and like that.
And so, that's another thing where it's very core for people
to understand how this works.
And there's a lot of different ways they can ask that.
I think, one of the things I try to think about is,
there's a number of ways that customers can ask
for things from you, and have questions about your business,
but there's really only so many answers.
And so the goal is to consolidate
all those questions together, so that you can figure out
the right answer to answers to all of them.
So that's what you're seeing here.
A lot of this is about taking that data as I'm showing here,
giving you a cluster level description of it,
and then also letting you see what are the things
inside of that?
What are the things that make it up?
So you understand how are these intents related.
And once you know that,
then you can start looking at metrics like searches, clicks,
and click-through rate
to see how those things are performing.
So you have a better understanding of, what are the areas
where I need to focus on, based on the intent
that I'm seeing from my customers.
Just one last thing I want to touch on here is that,
again, I think the goal is, over time, to make it
so that customers have to figure out
a lot of this stuff less and less.
We want to make sure that
we find as many of these opportunities for customers
and we make it easy to see.
And so one of the things we've also done here
is automatically categorized the performance
of these things based on how frequently
they're getting searched, and then also their engagement.
So this allows us to quickly filter down to say,
"Oh, these are the clusters that need our attention."
They're getting a lot of searches
so your customers care about it, but also,
it's not getting clicked a lot, so there has to be
some issue there with engagement.
And so we want to make it super easy for someone
that comes to the screen and just completely understand
what's going on.
That makes a lot of sense.
So, as you were building out this platform,
the data needs for something like this
must be pretty immense.
Can you talk about how and why did you chose Snowflake
to build this platform on top of.
Yeah, it's a good question.
So, originally we didn't, and that
ended up being a little bit of a problem.
So, originally we were using Elasticsearch to power
a lot of this.
And the reason for that was that
we had been using Elasticsearch
in other parts of our product, and it was the easiest
to get up and running on.
And so, we found that that was the best place
to get started.
One of the issues we found there, though, was that
Elasticsearch didn't scale well
for very large amounts of data,
we were starting to run into issues with the bigger accounts
that we had. We couldn't display to them all of their data,
and the complicated joins and transformations
that needed to happen to make a screen like this possible,
which is not easy to do,
or even possible, using something like Elasticsearch.
So we realized we needed a better warehousing solution
that would make it easier to get this data out,
and a bunch of other stuff that we wanted to enable.
And so, that was the core thing--we needed something
that had a lot of power behind it that we knew could handle
some of these queries and complicated transformations
to make this data available for people.
So, when you were evaluating Snowflake,
was there a certain process, or what were
the specific considerations that you had to choose,
or be mindful of when choosing Snowflake?
Yeah, it's a good question.
So, there's a couple of things.
I think, one of the main ones was performance.
I think an important thing to mention is that,
we kind of had a fractured ecosystem before Snowflake
in terms of where we stored our data,
even for analytics cases.
So for the screen that I'm showing today,
we're mainly focused on data from our search product,
and that search product was using Elastic
to power its analytics.
But for other products we were using Redshift,
or maybe other places to power that data.
And so depending on the different back-ends we were using,
we had different requirements.
For Elastic, a big one was just performance.
We needed to make sure that we were able to get
the performance that we wanted, and that we were able
to facilitate the queries
and things that we were looking to do.
For Redshift, one of the major things
we had was maintenance.
We found that maintaining
our Redshift clusters became a big overhead.
We had to spend a lot of time figuring out
how large should these clusters be.
You know, how do we manage tuning performance
on these queries?
How do we build tools around it to make sure
that we're keeping the uptime we want,
or that failing over is very easy?
There's a lot of stuff that we actually had to build
around Redshift that made it possible to then provide
the functionality we wanted.
And so, in the Redshift case, what we really wanted to do
was have a vendor that was much easier to manage.
So we didn't have to worry about maintaining these clusters.
We didn't have to worry about size. We weren't scared
that if we add more data, or if we onboard a huge customer
that now we need to completely upscale our clusters.
We want to be able to just deal with the data we get,
make it accessible for our customers,
make it as easy as possible.
So I think those are our main things that we were looking at
while we were evaluating.
So that's cool to hear, how you've really gone through
this journey of previously not on Snowflake,
now you're on Snowflake.
So that's really cool to hear.
You have those battle scars, right?
And so,
what's next for Yext here, and your role?
Where do you see Yext six months, one year down the line
for your customers?
Yeah, it's a good question.
So one of the things that we're trying to do now
is make data much more accessible to our customers.
So there's a lot of data that we have in our platform
that we may be using for internal purposes,
but we just don't expose to customers,
or we haven't in the past.
And part of that is because we haven't had a good way
of doing it, and so we have focused on building better APIs
that make it easier to get that data out of our platform.
But also part of it was that we didn't have a great place
to store all that data.
We didn't have a home for everything.
And so, one of the major things that we've done
throughout the past year, is really make Snowflake
the home for everything that is either analytics related
or just like long-term data we want to make accessible.
Stuff that power certain parts of
more consumer-facing applications may use other back-ends,
but we want to make sure that, at the end of the day,
that Snowflake is the home where everything sits.
And then that gives us one place
that we can build infrastructure on top of
to make it easy to get out of the platform.
And so, that's a big focus of ours now,
making that data easier to access,
and making Snowflake the home for everything.
So yeah, really using Snowflake to eliminate
those data silos sounds pretty important.
And it's exciting to hear that, you know,
you mentioned that you used to build out these API wrappers
around the data.
But with Snowflake and the native data sharing capabilities,
it eliminates some of that need, right?
And so, you can just directly share the customer's data
with the customer.
So I mean, that's pretty powerful.
So from here, for our viewers today,
if they want to learn more about Yext and you,
where should they go?
Yeah, so we actually over the past couple of years
have developed this Hitchhiker community, which is basically
our admin community for helping people understand
how they use Yext.
We put a lot of great content on there
about our different products, guides for how to get set up,
but then also recommendations for how people can use
the product, how they can think about it
for the vertical that they're using.
So I would say, hitchhikers.yext.com is a great place
to get started if you're curious about how to dive
into the product.
And then generally just yext.com is a good place
to understand what is our mission
for what we're trying to do.
What are the different areas we're focusing on.
So you can get a better understanding of what we do at Yext.
So I'd say those are probably the two best places
for you guys to find out more about us.
That's great, and Mike, I want to say thank you
for joining me today.
This has been another episode of "Powered by Snowflake."
And if you are interested in building your next application
on Snowflake, check out developers.snowflake.com.
Thanks.