Why Most Marketers Don’t Trust Their Data and How to Fix It

Five out of six marketers believes it's absolutely essential

to make data driven decisions to get the best results

from your advertising. Only one in six actually

trusts the data that you use to make

decisions. And at the end of the day, so much of what

we do is doing things well, but so much of the

impact we cause is by choosing the right things.

But 61% of conversions are shared by more than one

platform and then 58% of conversions are non

incremental. So they would have happened anyway without the ad.

And welcome to a new

episode of Digital Coffee Marketing Brew. And I'm your host,

Brett Deister. If you please subscribe to this podcast and all your favorite podcasting apps,

leave a five star review really does help with the rankings and let me

know how I am doing. But this week

I have Zeke with me and he is a

serial entrepreneur, the founder of Data Speaks, an AI

powered analytics platform that helps companies

identify what drives her sales and invest in the right

marketing strategies. And he has a background in economics

and data science. Zeke has spent the last 20 years developing AI machine

learning and data analytics solution, embedding hundreds of companies

to make data driven decisions and accelerate growth.

So welcome to the show, Zeke. Thank you, Brad. Thank you for having me. All

right, the first question is all my guest is, are you a coffee or tea

drinker? I'm both. I have coffee in the morning

and then usually tea before going to, going to bed.

But I would say that I drink coffee way more than a drink

tea. Do you have like a specific favorite of coffee or is it just

whatever you can get your hands on? I love

Italian coffee, espresso roasts.

I like usually

medium roast, South American

coffee brewed in Italy if it makes sense. You

know, so there's a couple of brands, Lavazza that

I, you know, really, really like is smooth and, and

just strong but not too strong. Yeah. So you specifically like

espresso? Because when I hear Italian coffee, it's basically always espresso.

Yeah, yeah, it, it's. I,

you know, was born and raised in Argentina. We have a lot of, you know,

Italian influence and it's just stronger, you know. So if you go to

Starb, you know, any kind of coffee

shop, it usually feels like watered down to anybody

who's from either Italy or Argentina. So I like

my coffee a little stronger. So you more like the double shot or

like when it goes into your. It's more of a double shot than a single

shot because I think Starbucks does a single shot and you

usually like the double shot or more? I would say

yeah, and. Yeah, and

a little darker rose than most people have it.

Fair enough. But I gave a brief summary of your expertise. Can you give our

listeners a little bit more about what you do? Sure. So

historically, I would say I'm a serial entrepreneur. I've

had many businesses in the tech space.

Right now I'm the CEO and founder of Data Speaks. You

kind of touched on that. But we help marketing teams

understand what channels and campaigns drive their sales

so they can invest in the right marketing strategies.

Gotcha. Can you explain what marketing attribution is and why it's such

a crucial, yet challenging area for marketers? Yeah, of course.

So what happens is when you are

advertising a lot, you know, you have a presence on multiple channels,

you're running video ads on YouTube, you are

on search, Google, Bing, you're doing

social, Snapchat, Meta, Instagram,

Pinterest, and so on. So you're doing all these different

things. And then at the end of the day, your store sells,

say, you know, $200,000 a week of

widgets. And what you want to know is you want to understand

how much of that was influenced by each channel

and campaign, because otherwise it's,

you know, money going out, money coming in, but you're not really

understanding the ROI of each of these investments. So

what we found is that in the US 54%

of marketing budgets are spent on ads. So more than

half. And while five out of six

marketers believes it's absolutely essential to

make data driven decisions to get the best results

from your advertising, only one in six actually

trusts their the data that you use to make

decisions. And that's because they put all these pixels on the

website for Google Ads, Facebook ads, and

they make decisions based on those. And those can potentially

be a source of truth because each of those only

sees activity from that platform. So if the Facebook

ads, pixel sees activity from Facebook, but

it doesn't really know that you're also running ads on Google, that you

are sending emails, text messages. So you

really need an independent way to track all the activity and

how that then impacts your bottom line.

So what you're basically saying is it's fragmented data that you need to

centralize so you can understand exactly where everything is

going. It's kind of like podcasting, because podcasting

there is a lot of different third party data, but it's not all

centralized either. So I have to look at three or four different ones

just to figure out, like, what's going on. And the thing is, like Even,

even if you were so right in that it's

really a puzzle and each of those pieces helps you see the full

picture. But none of those can

help you figure out the actual revenue it drove. Because you

need to understand, essentially, it's called incrementality.

You want to understand the incremental impact of your ads. So if you go

and increase your spend

by say $10,000 a month for

YouTube, you start running more YouTube ads. You want to understand

how much revenue that's going to drive. And

that's really what good marketing teams are trying to do all the time.

Figuring out where is the best

place to invest my money, what return am I going to get from that?

And how much can I scale

each channel and do I have a way to actually measure that, test it

and see it through a real life experiment?

You know, and at the end of the day, so much of what we

do is doing things well, but so much of the impact

we cause is by choosing the right things.

And that's what we do, right? We help you understand

what it is that you should be investing in. So when you go and put

resources behind it, you actually see it pay out. And what do

performance metrics from advertising platforms like Google or Facebook

fall often to provide accurate insights?

Yeah, if you think about it, you have. They're

the source of truth for certain things. So they can certainly tell you

how much you're spending with them, they can certainly tell you how many clicks

they provided, they can show you how many impressions and reach

they had, but they have no idea how much revenue they

actually drove. So what happens is we put

this pixel on your website and they see, oh, somebody

clicked an ad or even viewed an ad and then purchased.

Therefore we drove the purchase. But 61% of

conversions are shared by more than one platform.

And then 58% of conversions are non incremental. So

they would have happened anyway without the ad. So what that

means is that maybe you got an email for

20% off for something you wanted to buy, you went ahead and bought it,

and then without you realizing this, you are like

browsing like scrolling on Instagram early that day and you happen to see

an ad, but you didn't even click on it. Well, Instagram is going to take

credit for that. I think

what's important to understand is that ad platforms are in the

business of selling you ads, not necessarily in the business

of measuring and tracking. That has to be your responsibility

as a company to make sure that you have data

that you can trust. Is there like a somewhat easier way of

figuring that part out. Because I know that's like the big piece of the puzzle.

Because like you said, meta is going to be like, oh, we did it. Or

Google's going to be like, oh, we did it. And you're like, well, you helped,

but you didn't really fully drive the sale, of

course. So I'll tell you two things

people are doing that are very

unreliable, and then two things that actually work. So what doesn't work

is looking at platform data, trusting that, or

using what's known as multi touch attribution, which essentially is

trying to piece all this into a cohesive customer journey,

and then based on that,

arbitrarily attributing credit to different touch points. So if you saw

an ad on Facebook, then click Google and then did this and this and this,

like, okay, that's four channels. Let's split the credit

in quarters, 25% for each. That's even fair.

Well, I mean, who it is to say that the first touch

point had exactly as much influence as the second one, the

third one, and so on? I mean, it's kind of ridiculous.

So that's, you know, the first thing I mentioned

is not having an attribution platform. The second one is probably

even worse because if you have that kind of attribution that is

arbitrary, you have a false sense of confidence in data that is

actually highly inaccurate. So there's really two things

that we could do. One is called media mix modeling. So

media mix modeling essentially helps you understand what

is the most likely scenario for

the revenue you got given the variables provided.

So given that you spend like last week, you spent this much

on YouTube and this much on this and this much on that, and you got

those sales, what is the most likely explanation for, you

know, how much each one contributed to? So the way, the way it's done is

looking at variance in spend and how it correlates to

revenue. So if something is performing really well and you

double your investment well, your sales should go up.

If something has no impact, you double your investment or cut it in half

or pos it all together, it should have no impact on your

revenue. So what we

do is we look at, for example, if you sell in the United States, that's

50 states. So each day you have 50

observations of what happened

when spend increase or decrease

for different states and what happened to your revenue

and that. We usually have three years worth of

data. So times 50 daily observations, that's 15,000

observations. Very clear patterns emerge in terms of

what happened in the past as you increase your decrease

spend for different channels. The

other way that you could do this is through

incrementality experiments. So incrementality, or

it's also called like a market market test where what you

do is you say for, I want to

test, for example, Google Ads. So if I'm running it and spending 10k

a day, I'm going to pause it in Colorado and Michigan

and I'm going to see for the next three weeks or so what's going to

happen to sales in those states. Or I could say I'm

going to double my, my, my budget in those states

and see what happens. So when you do that, essentially you create

an ideal environment for an experiment because you're

saying, you know, all other things are equal. Every, everybody is getting, you

know, my, my emails, my text messages, my social media. The

only difference is that some states are either not seeing my ad or

seeing my ad more often. And

so one way, the first way, media mix modeling

doesn't require that you do those experiments, but when you do both

combined, that's extremely powerful. Because with media mix

modeling, you get very, very close

to the reality in the world. Then you run experiments,

use those experiments to calibrate your model so it keeps getting more and

more accurate. So it is

not easy. Right. And we work with companies that are spending

250,000 or more on ads per month,

some, you know, several million. So that's a, you know,

highly sophisticated approach to a really

big problem. But if

you are on a budget and you still want to

know how well your ads are going, a very easy way of doing

that is to actually do go ahead and

pause your ads every now and then and see what

happens or double down and you see what

happens. It's really, if you can't see

a clear impact or when you make such a drastic change,

that should be an indication that, you know, it's not

giving you the results that it's promising. And so how has

the death of the third party cookies impacted marketing attribution and

what adjustments do brands need to make? Yeah, so I think

that it's important to know that, you know, pre Internet

we already had, you know, incrementality

testing, we had media mix modeling. And then

when the Internet came out, it just allowed us to track

individual users. And that's the key difference between

the two methods that I talked about and what the Internet

promised for a while. Well, that kind

of worked for a little bit. It

didn't actually allow us to measure real impact, but it

allowed us to see that a visitor came from email or a

certain ad campaign and so on. Now

the problem in the last couple of years is that because of ad

blockers and privacy settings in our browsers,

these pixels are not working as well as they used to.

So right now 42% of conversions are being blocked.

So that means that you don't have accurate data to work with.

It also means that the platforms that use

algorithms to essentially optimize their own performance

to figure out, you know, who should, what kind of consumers

should we be targeting, what kind of creatives should we be showing

them they're not getting the data they need to perform their best?

So the, there's two issues with third

party cookies. You know, one is our reporting just became

incredibly unreliable. The second one is

our platforms. Ad platforms can really do

what they're supposed to do if we don't collect the data ourselves as

first party data and then stream them to the

platforms. If you just take the pixel they give you,

put it on your website, your performance is going to be

far from optimal. What factors lead to

inaccurate revenue reporting from advertising platforms?

Well, there's a couple of them that we already talked about. So

one is pixels only work 58% of the times,

so sometimes they just don't capture the true value of

the conversions they actually should be getting credit

for. Then there's duplication with

multiple platforms claiming the same sales.

There's the issue that only

58% of conversions are incremental. So

you know, one third of conversions would have happened anyway. I think

that that's, that's a big one. And also like the

pixel essentially sees somebody viewed an ad and clicked on

that and purchased it, doesn't have

any clue about everything else that is going on in your world, any other

marketing you might be doing. So yeah, it's really a

variety of all the things combined that makes,

makes them that they're still the source of truth for again, clicks, impression,

spend, anything that they own. But your source of truth

for revenue is your sales, right? Like you have money in the bank, you

know, how much order, how many orders you get, how much revenue you get.

And yeah, no, no one

platform can tell you how much revenue they actually

drove. And why do some businesses rely on the

last click attribution? And what are the dangers of doing

so? Well, the why is because it's easy, right?

The same with the pixels. There's a difference between

what's easy or even most common and what's actually

the best practice. So if you think about the

customer journey, most of the time there are

multiple touch points before a purchase, there

are some exceptions for, you know, some impulse purchases, but most

of the time people just, you know, learn about it, come to your

website later and sign up for your emails and then

maybe they see an ad. And eventually it usually takes about seven

touch points for somebody to purchase. So if you're giving

credit to the last one, you're really ignoring everything that

preceded that. And you know, you would be over

investing in that last touch point. But really

it's like the first one and first few that drove people

to the purchase eventually, you know, so you, it's, it's, it's

easy, but it's, you know, highly

inaccurate and it can lead to all the wrong

decisions. And how is AI being leveraged in marketing

attribution and what are its current limitations?

Yeah, so with,

I think that the, the beauty of AI is that,

I mean, you know, what do you call a data head? But most people

aren't, you know, and especially when it comes to

marketing, a lot of people

can read a report and see trends over time.

But going beyond and really getting

deep into, you know, what will be a data science project and looking at,

you know, probability distributions, confidence intervals and

how to make sense of all that, how to interpret that, what kind of decisions

to make based on that,

it just takes a little bit of training to do that. And I think that

that's what the beauty with AI. Not only does

it have access to all your data across all channels, can see the whole

picture and can provide instant answers, but it

could iterate with you and walk you step by step

and maybe offer suggestions that you wouldn't have

thought about otherwise. So for example, you could ask about your conversion rate,

you know, why did it drop last week? And you know, maybe just, you know,

just tell you it dropped from 4% to 3%. But it

notices that most of that drop happened on

mobile devices for a specific landing page. Right. So you

can, you can say that and then offer to break it down by landing page

or device. So it's

essentially giving you the ability to be a

highly trained data analyst, even if you don't have that background.

And I would say that you also ask about the limitations.

I think that it's such a young technology that there's like still

a little bit of hallucinations and

especially when it comes to math sometimes, you know,

surprisingly it gets really basic stuff wrong, you know, so

you really need to like check and see. I mean, we, with our

AI models, we train them to provide

all the intermediate steps. So you, if there's something off

in the reasoning, you know, you, you can catch it

because you're following along rather than just getting the, the final answer.

So I think it's really important to, to know that and also to

know that nobody could just give you

the right strategy. You know, like you still have to,

you know, you can rely on this to, to, to, to get answers, but at

the end of the day, only you know what's best for your business, what's going

to work for you. So, you know, just take everything AI gives you

with a grain of salt, challenge it. But yeah,

I am very excited about everything that AI

is allowing us to do. And how can businesses identify which marketing

investments are effective and which ones are draining the budget

unnecessarily? Because we're always trying to save money. Yeah. So

again, if you have the budget for a proper attribution

and testing platform,

definitely that's the answer right

now. If you aren't really investing a lot

in ads and need a more,

I guess, like homemade way of doing it, like a low

budget, then you can for sure do a

holdout test where you just pause the channel or double

down and just see how well it does. You know,

it's not going to be as accurate, but it's certainly going

to be much more informative than what the

platforms are reporting. Gotcha. And how do

you see the role of marketing attributions evolving in the next few

years? I think what's happening already is that

marketers are becoming more and more savvy about the limitations

of the pixels that they've been using. They understand

that they really need an

independent way of measuring success rather than just

taking what the platforms are reporting as face

value. And I think that that's essentially going

to help us just waste less and make more, you know.

And a side effect of that is we

won't have to bother people with ads that are not relevant to them.

So I think that what makes for good advertising

for companies also makes for a good user experience for

users on their phones or computers looking

at information ads that are relevant to them.

And how is the shift to privacy conscious marketing, for

example, GDPR or ccpa, involving

the future of marketing data and AI powered attribution?

So there's a lot to CPA

and gdpr, but essentially what it means is

to collect certain data, you have to let people know that your

GDPR is a little more,

it has more restrictions and more requirements. So for

gdpr, for example, you explicitly have to opt in before

any Cookies can be used with ccpa.

You see all those banners where you just accept and then just move on.

And then what you do

with that data, you have to disclose how you're going

to use it. You can't sell it, you can't

rent it.

So I think that

the quick answer to that is that

I don't see much of a link between AI

and what data we collect. I mean, I think it's up to us,

it's up to each company to understand

what do I want to be compliant with, what

markets am I in. That's going to also inform what compliance you

need, what are the local laws, what do I need to

disclose, how do I need to store the data? But I

don't see AI really messing with

that. I think that those are the two separate

paths and those

are more geared towards the way the data is collected and stored,

not so much how it's processed and consumed. Got you. And then

what are the key questions marketers should ask when evaluating

their current data and attribution practices?

First and foremost is what approach you use.

If somebody tell you that they use multi

touch attribution or mta, run away as far as can,

that's not what you need. If somebody

tells you their they're

doing media mix modeling, then the questions I would ask would

be around the model creation process and

validation process. Do they take into account what makes your

business unique or do they give you the same model they give

the guy before you? What's your validation

process looking like? How are you going to make sure that

the model you create is not just overfitting past data, but you can

predict a number of different scenarios, even ones that

it's never seen before. And anybody who's

a real data scientist should be able to answer those

questions. And yeah, then

there's kind of the vibe check. Make sure that you like the people you're working

with and you see them as a long term partner in your business.

Got you. And then how can small businesses with limited budgets and resources

still apply the concepts of accurate attribution without getting

overwhelmed? Yeah, so I think it depends on what we

mean by small budgets. You know, so if we are talking about like

you're spending less than $10,000 a month on ads,

you don't really need any of this. If you're spending less than

50,000, maybe do one of these like

holdout tests, like homemade tests that we

talked about where you just turn off a channel or double it down

and then see what happens. But if you're spending 50,000 or

more. Yeah, like you. You need something a

little more sophisticated than that. Got you. And then

people listening to this episode, they're wondering where can they find you online to learn

more? You can learn more at data speaks AI or you

can find me on LinkedIn. I'm sure we can put the link in the show

notes. Yeah. Feel free to reach out if you have any

questions. All right, any final thoughts for listeners?

I'm going to go have some tea because I realized that I'm having too much

coffee. So I'm going to balance that out. That's fair. That's

fair. But thank you, Zeke, for joining Digital Coffee Marketing Brew and sharing your

knowledge on AI and data. Thank you, Brad,

and thank you for listening as always. Please subscribe to this podcast and all your

favorite podcasting apps. Leave a five star review. Really just help the rankings

and let me know how I am doing in of terms. Join me next week.

Let's talk about what's going on in the PR marketing industry. All right,

guys, stay safe. Get to understanding your data and your marketing attributions and

figuring out where you're actually getting all the sales and see you next week

later.