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BRUNO AZIZA: Hi, everyone.
I'm Bruno Aziza, and welcome to another episode
of Data Journeys.
This is the place that we come to to learn from data analytics
leaders, their success, their dos and don'ts.
And today, we're going to talk to Nir from SearchKings.
He's going to tell us why they are the data kings.
Nir, thank you so much for taking the time
to talk to us today.
Let's get started.
What is SearchKings?
NIR KUSHNIR: So SearchKings is a digital advertising agency.
We're based out of Toronto in Canada.
85 employees.
We're in a business of building and managing digital campaign
for our customers.
Most of them are in home services
and professional services.
We have around 5,000 customers.
And we manage around half a million dollars day of spend.
BRUNO AZIZA: So demand generation marketing.
This is bound to bring a lot of data together.
So tell us a little bit about the use cases
that you're deploying today and how
data preparation is becoming a part
of your work on a daily basis.
NIR KUSHNIR: So our core product is lead generation.
That basically means attribution.
So we're SearchKings, and we are the data kings,
if you want to call it.
Our customers expect return.
Every conversation starts with number and ends with number,
so we have to measure quite a bit.
Over the past, let's say, 6 to 12 months,
we start getting involved more and more with data.
Actually, with the acceleration of machine learning
and automation, we found ourselves in a place
where we need to not only understand the data,
but to make sure the machines are doing
what they're supposed to do.
And one of the best way to do it is
to provide your own set of KPIs to your customers that
verify that the goals they would like
to achieve really being achieved or in expectation of being met.
And we did it with data preparation
because we found that we have lots of platforms we work with.
And we have our own proprietary platform.
We look for a way to bring it all together
and provide a unified view for our customers.
And data preparation was the way to do so.
BRUNO AZIZA: So and you're also working with a lot of scale.
2 million jobs daily.
75 Google functions that [INAUDIBLE]
replication in real time.
So tell me a little bit about how the whole journey started
what drove you to make the decision
to use the platforms that you're using today.
NIR KUSHNIR: So we are a Google [INAUDIBLE] partner
on the advertising side.
And on the technology side, we use, obviously,
Google Cloud technology.
And we built our own proprietary platform since, I would say,
2010 when we started thinking about that.
And it started in a very, very basic idea
of looking around Google Cloud and try
to find a solution to a problem that we came across,
which was really lots of raw material data sitting
in one side and the need to bring it, in our case,
to Data Studio on the other side.
And the fact we have our own platform.
We looked at Data Studio to start,
but obviously Trifacta and data preparation
is the tool we're using in a way to bring it and enhance
our platform.
As you mentioned, we are running over 2 million jobs daily.
A typical customer of SearchKings
will spend anywhere between, let's say, $50 to $200 a day.
And those data points will include their lead generating,
impression share, and lots of other digital indicators
that we manage right now in a consolidated view
and run different flows that together
create views for our customers on Data Studio.
Both customers and channels.
So channels will be set of customers who work together
and benefit from our solution on the data side.
BRUNO AZIZA: And what kind of data are you bringing?
Where does the data originate from?
What does the pipeline look like?
NIR KUSHNIR: Yes, so that's where it
became very interesting for us.
We have the traditional way of counting leads,
and leads might be phone calls or text or web forms.
But then, over time, we realized that we're actually
able to start looking at what we call trust signals on our side.
And trust signals are our indicator
that comes from the platform that allow us to make sure
that what we are working on and what we are trying to achieve
is really progressing the right way.
And the new area that literally in the last few months
we have been working on is to try and provide
trust signals to our customers from the market.
So a good idea will be, can we take Google Reviews, which
is a very important indicator in digital advertising,
and analyze and benchmark it for our customers
so they understand how they can develop trust
signals with their customers?
In this case, it's homeowners who are mainly
in a B2C environment.
BRUNO AZIZA: So trust signal is the result
of bringing data together, running a model on top of it
so you can assess the value or the credibility of data?
I mean, how did you come up with this algorithm
in the first place?
NIR KUSHNIR: So trust signals-- let's break it down and just
actually talk about a very, very simple example.
When you are a homeowner and, actually,
you're buying product from a contractor.
So a trust signal, the way I define it,
is what is the probability that my future experience will
be positive and reflect the past experience
with specific provider?
Now, let's take it to the data side.
Can I look at a set of data, use different trust signals
that verify that the information that I see on my side
really indicate future trends?
And I think what we find that the technology is definitely
not the barrier anymore.
Even implementation has become very easy.
What we find is a gap that we worked really hard on, is how
do you work with customer or user,
which is not only super sophisticated,
and provide them with two of the three indicators--
we call them the trust signals--
that give them the two or three pieces of information which
are very relevant to them?
So a flow that may include 50 or 60 data points
may end up being only two or three specific areas
that we focus on for each customer.
BRUNO AZIZA: So this is interesting.
This is removing, basically, the noise
in getting to the signals.
Because marketing departments tend
to measure a lot of things.
But you seem to figure it out, OK,
what are the key metrics that truly move the needle?
NIR KUSHNIR: So I think for friends,
I usually say what SearchKings does, I say,
we're leaving the gap between what technology can do
and what users can understand.
And if you talk about where the world is going
and how we fit in, data prep was one tool
to help us mine this gap.
We feel very, very confident the human interaction with data
is going to accelerate while automation is going
to accelerate at the same time.
So the gap between what technology can do
and what users can understand and use probably
widen and not actually become any smaller.
And data prep is a great way to mine this gap.
BRUNO AZIZA: That is a great--
I'm sure things that the audience here will relate to.
A lot of technology, a lot of data.
And then there's what we can actually understand and act
upon.
Now, you've been in this space for a long time.
And so I want to kind of take from you what you've learned.
If you were to talk to yourself at the beginning
of this journey, what will be the things you would do?
What would be the things that you would avoid?
Let's start with the positive.
What would be the one piece of advice
you'd give everyone listening they must absolutely
do in order to be successful with data?
NIR KUSHNIR: I would say that your super user or the project
leader-- and I'm focused less about the title,
but more about the person who's leading
it-- should have very, very good understanding of the business.
I think that today, it makes much more sense
to take a user who knows very little about data
but knows a lot about your business
and introduce them to the concept of using data at scale.
So if I could choose one thing, I
would definitely have the project leader
to come from the business side and not
necessarily from the data side.
I think this is the number one.
The number two, I would say you should probably
say yes to every opportunity for the first three months
when someone say, can we do that?
So I believe the default answer should be yes.
You may want to look at it again after 90 days to see
do you really add value.
But there is no better way to learn about new technology
other than actually trying, and trying at scale.
BRUNO AZIZA: Wait, so yes to everything?
That sounds kind of scary, but tell me more about that.
Why is that?
Is it because you imagine that you just
don't know what you don't know?
Or what's the idea behind this?
NIR KUSHNIR: So I believe most of the audience today
understand the concept of MVP, the Minimum Viable Product.
So we're all familiar with that.
I would actually take it one step even further and say,
while you're trying new stuff, you
have to understand that you're building skill internally.
And sometimes the problem itself may not justify
the investment in the long run.
But in the short term, it's a great opportunity
to learn new skill.
We love the night that at 4:00 PM or 5:00 PM Eastern time,
we figure out we're completely stuck,
and wake up at 7:00 AM with a new solution.
So getting stuck is also very, very important
part of the process.
I believe manuals are fine, and webinars are OK.
But there is no better way other than facing a challenge
that you have to go and do it in a different way.
And Trifacta as a partner head and unbelievable community,
together with Google Cloud, it's less
about solving the problem for us,
but introducing us to someone who faced similar problem.
And I think you only hit those problems when
you are trying new things and you're
trying to dare more and more when it comes to data.
BRUNO AZIZA: Yeah, I can't remember
who said if you're not failing, you're doing something wrong.
So that's the idea, is just develop skill
and keep on trying.
I want to go back to this idea of connectors
because I think at your company, you
recruited what you called ambassadors for data prep.
What is that?
Where did they come from and what's
the profile of an ambassador?
NIR KUSHNIR: So we are a young company.
By the way, you are talking right now
to the oldest person of 85 people at SearchKings.
So our employee base, young individuals.
And they like to try new things.
We found very quickly that working
with a set of five or eight individuals
with their customers directly and empower them to--
I use even the term "to celebrate data."
So celebrate data means to take the power of data
and bring it to a level that it doesn't only add value.
It differentiates your company and take the conversation
to a completely different path.
So ambassadors, their job is not only
to accept what you suggest, which is sometimes--
or accelerate the message.
Their job is also to lead you and push you in the direction
that you didn't think originally.
I think when we talk about the word ambassador in change
management, we always say, we need this core group
who is going to accept the strategy and run with it.
I would say, in our case, the ambassador
are those who actually point us in the right direction and say,
I have a need hear that maybe you can solve for me.
Usually, those needs started very basically.
Here is where Google Analytics doesn't give us
the information with Google Ads, and we
have a Microsoft advertising how will you do it together.
But sometimes it went completely differently
to add the data feed of a third party or something we never
thought about before.
How whether impact lead generation in the HVAC industry
is a very, very common example.
We did quite a bit of work with that, as well.
BRUNO AZIZA: What's the profile of an ambassador?
How many years in college?
What type of field?
Where do they come from?
NIR KUSHNIR: Yeah, so those ambassadors are
SearchKings employees, right?
So they are staff members.
What's really interesting to, at least,
take into consideration-- and I think
you want to think about it internally when
you select those ambassadors.
Sometimes the newest members on your team
are the best ambassadors.
So you always try to balance between,
OK, do I put on my team the individual who's here
four or five years and when he says something,
everyone listens?
Or you're actually trying to engage those who just joined
and there's something beautiful about them.
They don't know much about the business yet.
And maybe they'll look at these new tools about new way
of doing things for you.
But for them, it will be the only way for doing new things.
So I would say the idea around ambassadors should be--
the most important part, they should
be individuals who have very strong communication skills.
Explain what they would like to see,
and more importantly to go after the fact and share with us
how it can be used at scale.
BRUNO AZIZA: That's great.
So we learned a lot about data ambassadors.
Now, let's flip to the other side.
The negative stuff.
The stuff you want to avoid.
NIR KUSHNIR: Yeah.
BRUNO AZIZA: What would you say would be the one or two things
that maybe you wish you'd avoided,
or you know is a best practice to not be distracted by?
NIR KUSHNIR: I think that it's OK to try different things,
and it's OK to make mistakes.
I think when you work with data, you
must maintain the data integrity at all time.
So I think one thing you want to be very, very careful--
early in the process, you don't want
to start producing different solutions
that the end user question the data integrity.
Because to go back to the end user and say,
we're still working on data.
It will get accurate.
You are going to pay the price in the long run.
So I think it's OK to slow down production
as long as you keep the data integrity in place.
The other thing, and I would-- on the Google Cloud side,
I would bring and think about cost from the very beginning.
So when you build infrastructure,
I don't think you have to worry about the dollar and cents
it's going to cost you to run different flows.
And by the way, it's going to cost a little bit more
than dollar and cents.
It can get pricey very quickly.
But I would say this.
When you build a new system, ask yourself,
what happens if we become 10x to 100x?
Is that something that we have the appetite
to work with in the long run?
Or I'm building something right now,
we should truly value it only for the pilot phase,
and it will be challenged later.
So those are the two points--
cost from the beginning and data integrity.
BRUNO AZIZA: How do you guarantee data integrity?
Are there particular things that you've
done, either by using the tool or through processes
you implemented, that make sure you're staying on top
of this data integrity problem?
Because it is a huge issue, right?
Garbage in, garbage out.
NIR KUSHNIR: There are two things we've done.
Number one, we try not to integrate and connect
all the systems right away.
So we try to run with parallel systems,
and it allows you a very, very simple, easy-to-use benchmark
that you can look at data from one side and the other
and said, OK, does that make sense?
The trends you see here, is it something
that you feel comfortable with knowing
that those are not integrated?
I think you get a little bit into trouble
when you try to integrate too much, too fast right away.
Number two, I think this is where experience can help.
And when you combine someone very new with someone very
experienced in one team and you let them put together,
sometimes it's OK to ask, does that make sense to you?
And we're not arguing right now if what we see on the screen
is actually what we found through our process.
But if we didn't have all this great technology--
might have a piece of paper and wrote it down--
does that make sense to you?
In a few cases, we found some major error
at the very, very beginning just by leaving the computer
and have a whiteboard discussion.
Or we look at the data and say, does that make sense?
We know we manage around a half million
dollar a day of spend at SearchKings,
so we have good understanding of the business
before we start using the advanced tools.
And we try not to lose this perspective once we engage
with those advanced tools.
BRUNO AZIZA: Gotcha.
OK, so this is super helpful on how to maintain data integrity.
Part of it is process.
Part of it is, actually, the speed at which
you're integrating all that data.
Now, I didn't prepare you for this question,
but I always ask it.
What is your favorite Trifacta or Google data prep feature?
NIR KUSHNIR: We're big fans of Trifacta.
We were introduced to Trifacta through Google Cloud.
So we found the solution on Google Cloud,
and we watched a few videos and said, it looks really cool.
I think the UX--
or the user interface, sorry-- of being
able to visualize a flow and realize that you can visually
understand how the data moves-- in our case,
it's from bucket to bucket.
And the idea that you can look at a recipe.
And I think the language around recipe
introduced a new concept of searching that we're really
looking at that.
We have raw material.
We brought it to the kitchen, and now we're
going to cook something.
So I think the language that sounds probably very
normal for data prep, we were introduced to the language.
And this language creates almost like a shift in our culture,
how we look at data in general.
So we look at raw material, and then we're going
and have a recipe in place.
And at the end, it has to be like every other meal.
For a meal to be successful, it has to be enjoyable.
BRUNO AZIZA: That's excellent.
Well, we have learned a lot from you today
the concept of the data ambassador,
how to maintain integrity.
I hope people are going to reach out to you and, of course,
ask you questions and connect with you as part
of an exceptional member of the community.
Nir, thank you so much for taking the time
to talk to us today.
If you want to find out more from stories just
like this one, make sure to click on the link down below.
Until next time, I'm Bruno Aziza.
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