Too often artificial intelligence in marketing is about volume, pushing out
more content, more ads, more noise.
But today we'll hear a different story, one where AI is used like
a scientist measuring, testing and refining to ensure every marketing
dollar drives real results.
It's a smarter, more precise way to grow without the waste.
My name is Alexandre Nevski, and this is Innovation Tales.
Navigating Change one story at a time.
We share insights from leaders tackling the challenges of today's digital world.
Welcome to Innovation Tales, the podcast exploring the human
side of digital transformation.
In this episode, we're tackling a big challenge in marketing attribution.
How do you know which campaigns actually drive sales and which
ones are wasting your budget?
We will break down why ad platforms often give misleading data.
How AI can reveal what really works and why testing, not tracking people
is the key to better decisions.
Our guest today is Zeke Camusio, founder of Data Speaks, an AI
powered analytics platform that helps businesses spend smarter.
With a background in economics and data science, he spent 20 years
helping companies move beyond guesswork and make data-driven decisions.
If you've ever wondered whether your marketing dollars are going
to the right places, this episode will give you the answers.
Without further ado, here's my conversation with Zeke Camusio.
Zeke, welcome to the show.
Thank you, Alex.
It's good to be here.
Well, I'm so glad you found the time for this conversation.
I have been looking for an angle to cover AI impact on marketing, but I
needed an expert like you, someone with a background in data science.
Let's make sure we bring the audience along however, and set the context
while providing a few definitions.
We agreed to discuss marketing attribution.
So how did you come to focus on this capability and how do you
explain the importance to people outside of the marketing team?
Of course.
So in any business, regardless of what kind of business it is, usually
what happens is that you have a set of activities you do that you hope
to drive a certain outcome, and then you have the outcome itself.
So an example of that is if it's B2B, maybe you're trying
to get leads from your website.
If you have an e-commerce store, maybe you're trying to get sales.
Regardless of the outcome, there are certain things that you do.
You advertise on LinkedIn, on Google, on Facebook.
You go to trade shows.
You.
send emails, do podcasts, and none of these, activities, even though
they have data that they provide in dashboards, none of them can give you
a real understanding of the impact they're having on your outcome.
So if you got a million dollars in revenue at the end of the month, how do
you know how much of that was driven by Google, Facebook, Instagram, and so on.
And if you don't know that, how can you possibly decide what's the best
way to invest your marketing dollars?
So that's the problem we solve.
We give marketers the ability to track what drives their conversions
and revenue so they can invest in the right channels and campaigns.
And were you already in marketing when you started your latest venture?
Yeah.
I, this is all I've done since I, graduated high school, about 25 years ago.
Had my several businesses on my own and had a marketing agency for a few
years, and when it was acquired in 2015.
I was working with a few, clients, doing consulting and this one client
that saw a metric for Facebook ads known as ROAS or return on ad spent,
and the ROAS said it was five.
What that should mean is that you could invest if, $10,000
and get $50,000 in revenue.
So that happened.
He invested $10,000 on that channel, and he got $20,000 in revenue back, and he
was, I. and we know we didn't manage the campaign or anything, but he was really
confused by the fact that he got a 2X return on investment instead of 5X as the
platform reported, and he didn't know why.
I had been working in digital marketing for a long time.
By then, I knew that the customer journey is very, complex.
People interact with you across multiple channels, multiple devices, and no one
platform can give you a true understanding of the influence on, revenue in this
case, because none of these platforms has access to the other platforms.
for example, if somebody clicks on a, an on an Instagram ad and Google
ad, those two platforms will take credit for the same sale because
they don't talk to each other.
Neither one of them has access to your source of truth, which is your web sales.
And, as a result, I noticed that it wasn't only this client, but a lot of our clients
were having an issue with not being able to know what kind of return investment
they were actually getting for the dollars they were putting into marketing.
So that gave me the idea.
it became really evident at that point that, it was a, really big problem
that marketers all over the world had.
And they had just accepted that.
there was no reliable way to figure that out.
So that, that's what inspired me to start Data Speaks.
And I guess, traditional marketing before digital was very much magic anyways
in, in many respects because yeah, you don't necessarily have the data, you
have to extrapolate, you have to, to sample I guess With digital technology
now you have a lot more opportunities to leverage, the signals you're getting back.
But tell us more about like how easy or difficult it was to find
a, a product or platform that, that can, provide this capability.
Yeah, of course.
It's very relevant to what you said before.
Before the internet, we didn't have pixels.
For example, Google Analytics has a pixel that helps you track website usage.
Facebook ads, Google ads, every platform gives you a pixel.
And prior to the internet we didn't have that, right?
So we had to essentially rely on modeling, media mix modeling, and
it worked for media that where you couldn't track individual users
or individual customers, right?
it was perfect for radio, tv, print.
And the internet then gave us the illusion that, oh, now we can track
things on a much deeper level.
And although there's some truth to that, I think it also gave us the illusion
that we could get data that is way more accurate than it actually is.
So when I started looking into this, I essentially kinda
saw the writing on the wall.
I saw that a lot of the startups that were trying to solve this problem
was were taking the wrong approach.
They were trying to track individual users across every
device, every channel with pixels.
But you know, these users were demanding more privacy.
So there were more users on using ad blockers, enabling privacy settings.
And essentially if you wanna, track people that don't want to be tracked,
you're gonna have a really tough time.
I started looking at, how this was done in many other scientific communities
and how different areas of science were looking for these correlations
and causal, causal relationships.
And, it became evident to me that really what you wanted to know as a marketer
is what happens to my revenue for every dollar that, that I invest in X?
It could be Google ads, Facebook, whatever.
And we formulated a hypothesis, which is, tracking the increases or
decreases in, in budget, would be a much better way to help you understand
the impact of that on your revenue.
So to give you an example, if you have a shop where you have a hundred customers
a day and you start running radio ads, and now you get 120 customers a day,
then you know that's helping you, right?
if you start ru running radio ads and you still have a hundred customers a day,
then the amount of impact is very low.
That's a very simple example.
And what makes this so complex is that there's a lot of different channels.
We had to analyze the individual impact of each.
What we do is we essentially look at each state every single day.
So for example, Colorado, what happens to your, if you
decrease your Google Ads spend.
What happened to your conversions?
Did it go down or up or stay the same?
If you went down by how much?
If you doubled, Facebook ads for Maine, what happens to your sales in that state?
Now if you look at one state one day, you know there's no, no way you can
make any inferences based on that.
but with machine learning, if you have 50 daily observations on a daily basis,
over time you see very clear patterns.
It's literally taking the scientific process of experimentation and
applying it to an area where, yeah, the alternative approach runs into,
like you said, a lot of privacy issues.
How do you control variables though?
Because it's not the scientific experiment done in the lab.
It's out there in the real world.
I guess that must be like a methodological constraint that, you
must work with your clients to respect.
Yeah.
So there are two ways that, we are, our model works.
you can do design experiments or natural experiments.
So with the design experiment, essentially you say, I wanna
test, say Google ads, for example.
I. So what the algorithm does is it makes a suggestion for what
markets you should test and for how long you should run a test.
Maybe it says, in Colorado and Maine, for 22 days, you can
do one of these two things.
so you can do a holdout where you say, I'm just gonna shut it off to
see how much my conversions drop.
Or you could say, I'm gonna scale it up by, 25-50% and see
how much my sales increase.
The algorithm does a really good job helping you find the markets that have the
highest statistical significance, but the lowest, negative impact on the business.
So if you have a lot of customers in California and New York, it's
not gonna mess with those markets.
It's gonna find markets that are smaller markets, but still, tell you a lot
about the overall, country in this case.
So that essentially helps you measure, the incrementality if you scale it up,
or how much you lose if you, turn it off.
Sometimes it's not possible to do an experiment and there's
a lot of reasons for that.
Sometimes, our clients don't really wanna.
mess even with the small markets or, it's just not practical for whatever reason.
So our model still needs to learn from, what happens with natural experiments.
So it's very normal.
even like in terms of demand, you are not gonna spend this
the exact same amount every day.
So even if you take one state, let's take Colorado.
You're not gonna spend the same every day.
So some days you're gonna spend more because people
are searching more for stuff.
Some, sometimes, people are not scrolling social media, so
there's gonna be less demand.
, And then over time you also increase or decrease budgets,
sometimes pretty substantially, so that's what it, it analyzes.
And again, it's, if it looks at one day, one state, could just be a coincidence.
But if there's, say, if consistently every time you increase a dollar
here you see $5 more revenue.
And that happens over time.
And based on that, the system makes predictions that turn to be very accurate.
Then, that's a really good indication for the system.
So our system allows for what in, in science is called randomized
control trials where you have a control or a treatment.
but even if you don't have the ability to do that, it still
learns from natural experiments.
I'm so glad that, I got to talk to you about marketing because I think,
what I see, I. These days where, generative ai, for example, is being
applied to marketing, it's in order to just push a lot of things, it's just
to throw things and see what sticks.
I think this is a very, clean, elegant approach.
And before we move on and talk about the features specifically, but I'm
really curious, how long did it take for you to get to a point where your
clients can rely on the platform.
And did you have to go for some time with more of a, like a hybrid
manual consultancy plus the platform?
Yeah, so we engaged about 20 different brands and agencies, in a pilot, and
it took us about three years to build a solution to a point where we could predict
with at least 95% accuracy what's gonna happen if you invest a dollar in whatever.
and, yeah, they were, really gracious with us.
They understood that we were building something that was never, didn't exist
to the, this degree and didn't allow for this real time ingestion of data.
That was converted into insights that helps you optimize right away, so they
knew that we were going after a, a big challenge that all marketers have wasn't
gonna be easy to solve it, but they were very good at providing feedback.
And not only feedback on the user interface that allow us to, may make
it more intuitive, but also they.
made re implemented the recommendations.
Our platform, was provided and saw the results.
And that, that's what helped us calibrate the models to a point where
we said, okay, we're ready to open the doors and, take in more clients and
yeah, it's been fantastic since then.
Super exciting.
All right, let's talk about what actually is required for such a platform to work?
I think you've already mentioned a few things, but let's just summarize them
in, in, in one place to facilitate, the listening experience for our audience.
What are the core features that you would say are necessary for this platform?
Yeah, so we need access to all your data.
So every platform these days has an API.
So we will connect to your Google analytics, Facebook ads, email, so on, we
have about 250 connectors at the moment, so we connect to pretty much anything.
We create automated data pipelines that ingest all the data, put
that into our data warehouse.
We have a ton of different processes in place, clean the data, look for outliers
issues and, the way we approach it is, we actually build one, attribution model
for every client that we work with.
So if you are selling furniture, you're not gonna get the same attribution as
somebody selling accounting services.
What we do is the first, we connect your data.
We start building the data pipelines.
But in parallel to that, we have three meetings, one hour each with our
clients, and we focus on your customer segments, product mix and media mix.
So we need to understand what are all the different personas that buy your product?
What products do you actually sell?
And what are all the different channels that you use online, offline channels?
Where do you sell your products?
Only on your website.
Amazon, Walmart, Target, other retail stores.
Are you doing, non trackable marketing activities?
Are you going to trade shows?
Are you appearing on podcasts?
So we have to learn everything about your business.
And then we, based on that, we create custom model that explains your business
in particular and with all these elements that we already talked about.
By how much does the lack of a pre-trained model delay the, how long does it take
for a client who expresses, commits to this approach and then when they
start, when they can see results, isn't the lack of a pre-trained model
like lengthening this quite a bit?
Yes.
if you use a, an, outside the box solution, you could probably be up
and running in four or five days.
With us it takes six weeks and we believe that it's much more important to have
accurate data than to have data quickly.
Most of our clients manage hundreds of thousands of dollars, or millions
of dollars and they make decisions of moving maybe 2 million from here to here.
So they can wait a couple of weeks knowing that the insights are gonna be worth it.
They wanna make sure that they make the right decisions.
It doesn't take that long if, for example, if you think about the media mix modeling
pre-internet, it used to take six months.
And the problem is that not only are you looking at data from six months
ago, but also like you don't get to make decisions for six months.
So in this case, once it's built, it will start like ingesting data automatically.
So yeah, it takes six weeks to, for the setup, but after that,
it's gonna be up to date a hundred
It's not, it's not a snapshot.
it's learning as it goes.
Absolutely.
Every single day.
So every day is learning and adjusting and getting better and better.
Yeah.
Amazing.
And so what are the key challenges to in, in training models,
models for each client like this?
Yeah.
I mean there's, there are a lot of challenges.
We have a process, that it's, we, it's a, the model of validation process.
So we build the model with all the assumptions that we had plus,
conversations we have with our clients.
We input all the data, run a couple of diagnostics.
I would say about half of the time.
It passes everything with great grades from the get go.
But most of the time there's something there that we're not quite happy with.
and usually what it is we forgot to include some factor.
A very common one is promotion.
So you did a black Friday promotion.
We forgot to model that.
So we see that something that was predicting to be here, it actually,
it, ends up being much higher.
Okay.
That's, one promotion that we forgot to build into something.
Or maybe, what is this bump around September last year?
Oh yeah.
We got, featured in this magazine.
So it's getting a initial set of insights, talking to our clients about things that
our model wasn't able to predict well, and make sure that we are accounting
for those things moving forward.
How does the artificial intelligence, the machine learning that, you've
built into the platform now change the role of, the marketer, the strategist?
Both of course on your side and your customers, in their organization.
There are two key aspects of it.
One is accuracy and the other one is access.
You can have the best AI model in the world, like very pleasant to chat
with, but if it has the wrong data, you're gonna make the wrong decisions.
So first and foremost, we wanna make sure that our model is very good at
capturing that underlying reality and the causal relationships in the data.
So the key characteristic of a great model is that it should be able to
predict a ton of different scenarios, even scenarios that he hasn't seen before.
So maybe you spend a thousand dollars a day on, on Google, or $1,500 a day,
but you've always been in that range.
What would happen if you spend $5,000 a day?
Like the model has never seen that, but it should still be able to predict
with a high degree of accuracy, things that it has never seen before.
And that's how we, he knows that it's, understanding the
true impact of that channel.
So accuracy is key.
The quality of our decisions, the quality of our outcomes are directly
related to the quality of the data that, aids those decisions.
The second one is access.
And what I mean by access is the average brand in the United States has 12
different platforms, and that means you have to log to 12 different places and
look at each one and start analyzing.
And yeah, sure if you have infinite time, you could do it.
It's really not a matter of, you're not smart enough, so
you need AI to do it for you.
But it is more of a matter of, you have usually somewhere between one
and 10 million data points a day.
There's no way that anybody can go through all that and look for patterns.
So AI is very good at spotting problems, opportunities and helping you prioritize.
In our case we have a checklist of 150 different things that it
checks for, and there's all this conditional logic built into it.
So it checks for this, and if this is a yes, then it goes, checks for this.
But if it, this is a no checks for this.
So it has all that built into it.
Plus whatever you as a user add to the system, because you want things
different than the guy next to you, right?
So the value in that is that, not only does it analyze all that and
filters out the noise and leaves you with a signal, is even if you had
150, insights, that's not actionable.
It is way too much for anybody to really, act on.
So it has to help you understand what's not relevant and what are the three to
five things that you need to be doing, paying attention to right now, that
are gonna have the greatest impact.
So if you go and ask a question, you don't have to learn how to use a user interface.
Like you had to click here and then here, and where was that?
Next time you forget, with AI you just ask a question and
it just gets you the answer.
So that's being able to access these 12 different, platforms and
ask questions in plain English.
Maybe you don't know what a certain metric is called.
You call it revenue and the platform calls it sales.
The AI understands that revenue is sales.
So that context is critical.
That's a big part of it.
But yeah, the other part is even if you're not asking a question, having
a system that can process data million of times faster than any human.
And can then give you that information so you as a human who knows the
business, has the context, knows about the strategy, can then, with that
context, know what decision to make.
I think that's, that's something that is, is probably the, one of
the highest competitive advantages that you can have as a business.
The core of the message there is that to compete, to be, successful,
businesses must learn to buddy up, right?
Their employees, their experts, I think initially you were talking about
how human oversight of the models is critical because our ability to
take things in context of course.
Then you've also mentioned how there is both from an automation perspective when
there's way too many platforms, but also way too many data just coming at you.
And so it's important to learn to use these tools to augment your,
analytical, essentially capability.
You've mentioned that as well.
And what's interesting about this is that, most business leaders or marketers are
gonna agree with the fact that accurate data helps you make good decisions.
And I would say accurate data that is readily available.
But the problem is that it's not always easy to have
accurate data or available data.
But if we are thinking not even five years into the future, two
years into the future, every competitor is gonna have that.
And I think that's because we already believe that if I have the right
information in front of me, I'll be able to make better decisions.
Making sure that every person in your organization that needs to
make decisions, has access to that.
It's probably one of the best investments that you can make.
Excellent point on which to finish.
But before we do, I actually have a couple of questions I always ask.
So the first one is, what is a book, a tool or a habit that has made
a particularly significant impact on yourself in the last 12 months.
I start each day, visualizing what I want the day to, to look
like, and then planning it.
So every call I, I have, I wanna make sure that I know what I
want to get out of the call.
That I make sure that the other person want gets what they want out of the call.
Every minute that I spend working, I wanna make sure that I'm working on the
most impactful thing at any given time.
And before doing that, I was just coming to the office and things come
at you that you're responding to.
So therefore you're not never really taking the time to, to say, this is the
most important thing I need to do today.
So now I still leave about two hours of time open time for everybody
else that needs something from me.
But I make sure that I start the day by doing the thing that is gonna
move the business forward most.
That's a very good one, very deliberate and mindful of you.
And the last question is, is there maybe one thing that you would expect
to remain constant among all the other things that are changing in the
moment, let's say 10 years from now?
The, the human quality, and the importance it has in, in, in business.
Sure, we are gonna have AI systems that have access to all the data that
that we need access to all our tools that we'll be able to send you a
notification, slack or email or, give you recommendations for, hey, like this
happened, what should I do about it?
And so on.
But I think that, that brings a lot of concerns to people and I think that
when we think about what machines are good at, the machines are not good at,
there's a certain aspect of connecting with somebody else and being real and
being, open about the challenges you have and what, the goals that you have
and why, certain things are, complex.
And I think that's never gonna go away.
I, I hope that it never goes away, but, it's really at the core of who we are.
So I think that's that's something that has always served me, to put relationships
first, to think long term, to invest in people and that's not gonna go anywhere.
We are human beings having a human experience.
I don't expect that to ever change.
Such a great answer.
Thank you very much, Zeke.
Thank you, Alex.
Zeke shared two big ways AI is changing marketing.
First by making data more accurate so companies can see
what actually drives sales.
Second, by improving access, turning complex data into simple, useful insights.
But as Zeke pointed out, AI isn't here to replace marketeers.
It's here to help them make better decisions.
If you want to dive deeper.
Zeke's team has a free resource, the Data Speaks Playbook, which
lays out 20 years of best practices for using data to drive growth.
You can download it at dataspeaks.ai.
As always, we'll have more exciting topics and guest appearances lined up, so
stay tuned for more tales of innovation that inspire, challenge and transform.
Until next time, peace.
Thanks for tuning in to Innovation Tales.
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digital revolution with confidence.
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