#520 Attention Over Clicks: Jeff Greenfield on Disrupting Digital Marketing Analytics

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Mehmet: Hello and welcome back to a representative of the CTO Show in Mehmet today. I'm very pleased joining me from the us, Jeff Greenfield, CEO of Provalytics. Jeff, thank you very much for being here with me today. I'm really excited to talk to you. The way I love to do it is [00:01:00] I keep it to my guests to. Introduce themselves.

Tell us more about your background, you know, what have you done before and what you're currently up to. And then we can take the conversation from there. So the floor is yours. 

Jeff: Oh, awesome. Thank you so much my, Matt, it's uh, it's a pleasure to be here. I'm Jeff Greenfield, CEO of Protic. Uh, at Protic we solve kind of the old age problem that marketers have had for years, which is this concept that half the money I spend in advertising is wasted.

The only problem is I don't know which half. And that's, that's been the struggle for marketers, uh, probably since the beginning of marketing, and it's only gotten more complicated. Since the beginning of digital, because there's just so many more places to go where you can spend your dollars, and there's less and less signal than we ever had.

So in terms of trying to figure out what's working and what's not working, it's, it's nearly impossible. Uh, especially using a spreadsheet. You need to use some very sophisticated math and machine [00:02:00] learning and ai, and that's what, uh, Lytics does. I've actually been in this space since 2008. Wow. I got in here because I was a marketer myself, and, uh, I, I got into measurement, not because I thought it was cool or anything, but because I had this problem, which was that for every sale we had at this client of ours, we had eight vendors claiming credit.

So we needed a way to figure out who to actually give credit for each individual sale. Then that led to almost every week adding new features. And before I knew it, we had ended up building out the first uh, enterprise multi-touch attribution solution, which was a company called C3 Metrics. I scaled that company up for about 12 years and exited about a year before the pandemic saying, I'm never gonna go back to measurement.

But when COVID hit, there were all of these monumental, uh, shifts and changes to the advertising ecosystem, which [00:03:00] essentially made the system that I built before stop working. So, uh, there was a need for something else and I thought, okay, well let's, let's see if AI can actually work here. And, and ended up working like a charm.

And so now I'm back at it again. And so it's, it's, and it, I have to say to a lot of marketers, they see measurement as this negative thing because it's a lot of math and spreadsheets. I see it as very sexy because we're in the middle of everything that's going on. So I get to see what some of the smartest, biggest marketers in the world do in terms of strategy and thinking.

And I've learned more from my clients, uh, than I think they've learned from me. So it's a, it's a great seat to be in. 

Mehmet: Great. And thank you again, Jeff, for being here and what, you know, like you took you, you answered my first question actually, and I see the passion you have of for what you do, uh, especially, you know, because you mentioned something, um, which is.

Was interesting to me like about like [00:04:00] who's respons for bringing this dollar to us. Right. And uh, uh, and this is a big question, but you know, like there's a lot of changes that are happening in this space. You mentioned ai, you, you know, there are a lot of other things. So I want to start from something which I touched on before, but.

On previous episodes, but you know, for me, like it's a collective opinions that, you know, form a better picture for myself and of course for the audience. So when in, in the, in the digital world, right? So, so we were talk and you mentioned, you know, like, uh, understanding attributions. So now we are in a, what people call cookie less world, right?

Right. So this is how we used to track things. So. Uh, and you know, like people cook, you know, use sometime the term cookie collapse. Uh, from your perspective, what's the misconception people still have about, you know, a [00:05:00] live after cookies? And of course we're not talking about the cookies we eat, we're talking about, you know, the, um, the technology that allows browser to track what people do that well, that, 

Jeff: that's exactly right.

So this is a, a monumental shift to how the internet. Actually functions. And, and, and to be honest, for a lot of us old schoolers, this is something that we never, ever thought would happen because it's, it's, it's part of how the browsers function. Cookies are, are natural. I, I never thought in a million years that they would ever go away, which is why the entire advertising ecosystem was built upon it.

But essentially what it means is, is that when you go to different websites, uh, and you're viewing different things. Uh, the companies that are, are putting those ads out are not gonna know what you actually saw. So let me give you an example. Let's say I'm Volvo and I have a new car that's coming out and meme, I decide that you are a target for this, uh, for this new [00:06:00] vehicle.

I think that you're right in the demo for this, and I've determined that the best scenario is for me to show you. Eight ads a day maximum. 'cause if you see more than that, you're just gonna get turned off, okay? Right. So you go to one site and you scroll through a couple of pages, and there's three ads that you see now in the world of third party cookies, which are the ones that are going away.

It would just be a counter for Volvo, and every time you would see an ad, it would go from one to two to three. Then you go to another website, completely unrelated. You would see an ad, it would go to four, and by the time you get to eight. It would, it would put something on your browser that says, don't so show any more Volvo cookies for the rest of, OR, or Volvo ads for the rest of today.

But instead without these third party cookies now you're gonna see Volvo ads all the time. Now what's happened is, is that over the course of the last couple of years. The technology companies have started to adapt and [00:07:00] change and create new methodologies so that you don't get hammered over the head with Volvo ads over and over and over again.

But what this has also happened is, is that the way we target, so before digital was around, we used to buy tv, print radio, and we would target like an area, a geography. We would target certain shows. Research shows people in certain age groups or demographics listen to it. And that was it. That's all we could do with digital.

We could actually go down and I could say I wanna target people that are driving Volvos that are on a lease, that are gonna expire in three months, who like to smoke cigars. I mean, that's how specific we used to be able to get, but not anymore. So essentially what's happened is we've had to pan the camera back.

To look at things the way we used to before digital, and as a result, our measurement as well has to also adapt and change too. [00:08:00] But the issue we run into is that digital marketers are used to this always on stream, if you will, of reporting. They're also used to being able to look at things at a very tactical level, so not just, Hey, I'm running ads on billboards, but I'm running ads on billboards and this city and these different creatives.

So they like to be able to get to that tactical level. That's some of the problems that have led to us developing Protic is to provide the digital marketer, if you will, that level of tactical optimization, but also at the same time respecting the privacy and having that camera panned back far enough.

So that we can work in today's kind of new world, I, let me give you a good example. Sure. So that the listeners can understand. Let's say ette, you decide that you've created this really cool new guard nose. It's this amazing hose. It doesn't leak. Okay. And you decide, you shoot a video, it's very [00:09:00] compelling.

You start running some ads on meta, so Facebook and Instagram with the video, and it's got an a cool name. So you run ads on Google so that when people search for the name, they'll find it. So someone comes along and they're scrolling through their Facebook feed and they see the video, they stop, but they don't click on it.

They don't even watch it, but it captures their attention. Okay? A couple days later, they're scrolling through Instagram. Now they see the video again. They remember it. Now they watch the whole video, but they don't click on it. But they make a mental note in their head that says, you know, when my hose starts to leak?

This is the hose I'm gonna get. Now, a couple weeks later, they're out doing some gardening. They go to turn on the hose and it's leaking like crazy and they say, oh my God, I should have gotten that hose. What was the name of it? They pull their phone out, they type it into Google, they click on it, and they buy.

Now, if you were to look in your reporting analytics on that specific sale, it would tell you that [00:10:00] that sale came from Google because nobody clicked on anything on the Facebook or Instagram ad. Now, let's say you have a great month, things are really taking off. You sell a thousand hoses, and so what ends up happening at the end of the month, you look at your numbers and you're just like, my God, all my sales are coming from Google, Facebook, meta, Instagram.

They're not, they're not doing anything for me. I'm gonna take all my money away from meta and I'm gonna move it over to Google. Now, what has just happened? Well, what we've done is we've cut off the top of your funnel, the attention you were getting, the awareness you were driving, that's now gone. It's not going to happen automatically.

'cause remember there was a delay in the time of purchase. So about maybe two months later, you're gonna notice that your sales are gonna flatten out and you're gonna say, my God, what's going on? That's the attribution problem. The attribution problem is that fact that you're having, you're using sophisticated analytics like Google Analytics.

And [00:11:00] it's pointing you in a direction that is not the best use case for your marketing dollars. That's the problem that marketers have today with this whole cookie problem and everything that's going on. 

Mehmet: Cool. Before I ask about the solution for this, Jeff, um, and, uh, you know, of course doing some research before, you know, joining, you know, today's, uh, recording.

Uh, uh, I know like you have some statistics about how much we can quantify this problem, so. Percentage of drop in revenue. So can you share, you know, some numbers with us, some metrics where people like love to, to, to hear these metrics and, you know, uh, unpack, you know, the, the, the business consequences is of, of this.

So it's, other than, I don't know where my. Sales leads are coming from, or which top of the funnel is working. So I got this, but [00:12:00] from business perspective also, like the privacy, how it affected the revenue. 

Jeff: Yeah. Well the, the biggest issue, and you just kind of nailed it right there, ma, Matt, is that, you know, and using that hose example is a perfect example because we can multiply that by tenfold or a hundred fold or a thousand fold for larger companies.

What ends up happening is that when you are spending dollars in the wrong place, your revenue goes down and you're losing the best use case of your money. And so when you think about this cookie collapse, if you will, across everything, because essentially what it did is, is it blinded Facebook. Facebook themselves doesn't know what's working and what's not working.

And then on top of it, you have all of these walled gardens. Facebook is one, Amazon is another, Google, YouTube is another that are not talking to each other. So we're talking about a 15 to about a [00:13:00] 25%. Revenue drop for companies that have not found a proper solution for this. I mean this, these are huge, huge numbers.

And this is not just talking marketing side. This is something that CFOs are starting to notice as well. And when you start to think about it, you know, the CFO finance are the ones who allocate out that budget to marketing. It's really on the finance side. 'cause you start to think about where does analytics.

Marketing analytics live in an organization. A lot of organizations, it lives with marketing, but the reality is, is that marketing analytics is really marketing accountability. I take that word from accountability. I take counting out of there because one of the complaints from finance is that marketers don't know how to count.

They don't know how to add up real well, and it's not that they don't know how to add. I mean, come on, marketers know how to use a spreadsheet. You can just sum up two, two cells. It's [00:14:00] so easy. It's just that their sources of their data are typically wrong. So their numbers don't add up properly. So the reality is, is that this marketing accountability really needs to be part of finance to get a handle on things because, you know, 15 to 25%, that's a massive number in terms of revenue drop for most organizations that don't go to handle on this cookie collapse.

Cool. Now let's add some 

Mehmet: complexity to the mix also, Jeff. So in a, because you gave the example of Facebook meta, you know, maybe TikTok and, and all this, right? So there are also some new channels, right? So which are called, I think, if I'm not mistaken, uh, the, let me just try to remember the name. The. Yeah.

Non clickable digital channels. Right, right. So, [00:15:00] so, so we have like anything that I see on the road, we have podcasts like this one, although like I don't have much of ads yet. Right. And some other channels. So how this is complicating the world more, I would say, Jeff. To, to, to, to, to add on top of what you mentioned so far.

Jeff: Yeah. No, it adds significant complexity for most marketing organizations, and especially to finance as well, because most organizations are still using Google Analytics for GA four, which is click-based attribution. In fact, most marketers today believe that you invest dollars to buy clicks and clicks lead to sales, and that's not.

How marketing actually functions, marketing functions by investing dollars to get attention. Just like that hose ad that we talked about earlier. You get attention. That leads to awareness. When awareness is built up to a certain enough level, people will walk into your store, [00:16:00] and if your store happens to be online, that is what clicks are, and then that eventually leads to sales.

So that's that important aspect. The problem is, is that most marketers. Aren't measuring attention or awareness. They're only measuring clicks. And there's this whole new area of digital marketing that are non click based. And you, you, you nailed it right there. Manette, we call them non click digital channels, the biggest one, the one that is the largest growth area in all of digital.

Is what we would call CTV Connected Television. Uh, and that's huge. You could also throw into this streaming. In fact, right now in the United States for the sixth month in a row, the number one TV network is YouTube. Now we're not talking YouTube, somebody viewing on an iPad or a phone, but someone having their app on their connected television and on the big TV at home watching YouTube that is being [00:17:00] watched more than N-B-C-C-B-S, Fox and all of the regular networks and all of their ads are streaming connected TV ads, meaning that there's nothing for anyone to click on.

So if you are running connected television or streaming ads. It does not show up in your Google Analytics, which means good luck being able to prove out to your finance team that this is working for you. You need to have something else in order to prove it. And you mentioned the other two as well.

Podcast advertising, huge growth area. It's only audio, sometimes video, but nothing to click on. And then also digital out of home. All of those billboards that are digitized these days. So, and, and who knows what's gonna come next? That's the other thing is we have no idea what's gonna happen next. Every year there's something new and they will be impression based awareness, driving media.

That does not result in a click. So marketers have to wake up to the [00:18:00] fact that what they've been using for measuring isn't gonna work in this new world of cookie list, non click based media. Jeff, like 

Mehmet: I'm imagining myself now, as you mentioned, like I, like you mentioned different personas, right? So maybe I am the CMO of the company.

Maybe I'm the CFO, the the one who's giving the money approving the budgets, and I'm telling guys we are. In a blind spot, we, we are not able to see what's happening, what's going on. So now let's talk about, you know, the solution. Like, walk me through, as you know, with Proletics, you position the company as AI driven attribution solution.

So tell me about, you know, how do you utilize the AI and how exactly you are able to, to solve this problem of knowing. My ROI, knowing what's working, what's not. 

Jeff: Yeah. Great. Great question. The key [00:19:00] here is to first, as we step back and look at the big picture. Is we have to have a unifying metric that allows us to not only look at click based media like pay per click and affiliate and those types of marketing along with this new digital world of connected television, and even add in things like direct mail in out of home.

So what's a unifying metric? Well, the unifying metric is when we think about attention. That attention metric is known as impressions. Impressions are how many ads are out there, how many eyeballs are you attempting to influence each day, which every single ad so we use as our kind of unifying metric is impression.

So that's the first thing is that we're not click base. Clicks are the after effect of the ads that you're purchasing and buying. We wanna be at that awareness level. So that's number one. Number two is we wanna make certain that the output is [00:20:00] actionable. In order for it to be actionable, I need to be able to go and duplicate certain buys and add more money or decrease less money for that.

And everything in the digital world, and even in the traditional world is also all impression based. So that's where we live. Is that the impression level? Now, we're also living in this world where, I don't know, MeMed, if you go to Facebook, which ads you saw there? Facebook knows, but then if you go to walmart.com, I don't know, as the marketer or the brand, I don't know which ads you saw there.

Walmart knows, but nobody's talking to each other. So all we know is how many ads I as a brand have in market. Every single day. And I also know the level of granularity of those ads. So you know, it's at a campaign, there's a certain type of creative, and so we unify it all. So we take into our platform.

Daily data at [00:21:00] that impression level. Very, very, very granular. And then we look at how many sales you had, how many orders you had, how many visits you had to your website. And then we ask the AI and the machine learning to see what are those relationships. And what we're looking at is we're looking at this concept called incrementality.

I wanna know how many additional orders I'm getting each day because of every single ad. And so what it utilizes in order to do that is kind of the gold standard of advertising, which is the concept of an experiment. Now, the way you run experiments in advertising is I pick one city like Dayton, Ohio, and I decide I'm gonna run CTV ads there.

I'm gonna run ads on YouTube only in Dayton, Ohio. And I have a theory, which is, I think when I run CTV, it's gonna improve my sales. So what [00:22:00] I should see is that I should see in Dayton, my sales go up compared to other markets that are of similar size. Then if I see that, I can prove that this is actually working well.

As it turns out, the Matt, when you look at a plan on a daily basis, at a very granular level, even if the marketer is spending the exact same amount of money, the actual number of ads that are shown each day are different. They vary a lot because the world of digital marketing is a biddable world. So you may spend a thousand dollars today and get 5,000 ads, spend a thousand dollars tomorrow and get 6,500 ads.

It varies day to day. And those variations are actually micro experiments that you're not running consciously, but they're running on their own. And then when you compare those micro experiments. To the changes in visits to your websites, the orders and the revenue. [00:23:00] It's at a level that a human cannot figure out.

But the AI and machine learning can run very sophisticated mathematical equations to determine what lift there is at a very granular level across your entire marketing plan. Essentially what we're doing is, is that we're using the micro experiments that marketers are running that they're not even aware of, and using that to determine the incremental impact of every single ad.

So this way marketers don't have to run experiments. They're actually being run already. 

Mehmet: The question that came to my mind, Jeff, that's all perfect, like amazing. But we know like. You know, especially for anything that requires machine learning, ai, we need, we need two things. We need data and accurate data.

Right. Um, so, so what are, I mean, the tactics or strategies, um. That would make sure that whatever data I have [00:24:00] is complete and it doesn't have any bias. Right. So, and the bias here can be anything. Right. So maybe it was a season where, I don't know, maybe these ads were showing in that city on that day because they have a local festival.

Right, right. Versus. What should happen on a daily basis. So how you, you, you, you tackle, you know, these challenges of, of data accuracy and completeness, I would say. 

Jeff: Yeah. Great. Great question. And it really comes down to as well, to add to that is, is because we need to have that accurate data. To train the model, because with AI it's all about the training.

And so the way we get unbiased data is we're getting the data directly from the platforms where the advertising is going on. So these are typically fixed numbers, how many impressions, how many clicks, how much was spent, and then they campaign hierarchy, whatever they're calling [00:25:00] it. So that data comes in.

The way we make certain that we account for things like seasonality, like the day of the month, the week of the year, the day of the week, and the month of the year is that we typically request from our clients to get 12 months worth of data. If it's available. If it's available, then we're able to account for seasonality.

Some clients, they're new and they're like, Hey, we've only been advertising for eight weeks. Well, great. That's all we have. We, the model will make assumptions based upon that, but it will get smarter as it keeps going forward. By having this type of data, not only were you able to return reporting, meaning, Hey, I spent X amount of dollars and this is what I got back for it, and this is how it breaks out.

This is what's working and what's not working. But then we're also able to ask the model. To forecast ahead. It's almost like a genie where we say to it, Hey, next month I'm gonna spend the same amount of [00:26:00] money and I wanna look out over the next month, and how should I change how I'm allocating my dollars?

It will go through and essentially spit out the perfect plan, meaning it'll say, if you do this and do this over this next 30 days, you're actually gonna get more bang for your buck, more return on your marketing investment. And you're able to go and say, well, what happens if I spend 30% more dollars? And the model is able to then provide a simulation and a simulated.

Of what that outcome would be. So not only tells you what happened, but more importantly tells you also how to reallocate your spend to get a larger ROI. 

Mehmet: Now, the question that came to my mind, Jeff, so we used to think in kind of how I would call them. I'm, I'm trying to find the right word to, to describe this.

So. [00:27:00] We needed something to be deterministic, right? Um, with, with whatever what we do. So we say like, this is gonna give us 5% ROI, 10%, whatever this channel is giving us 40%. So our, is the market shifting now to towards kind of. A cumulative ROI for all the spends that we do on the marketing. And instead of spending too much time guessing what's working in one specific channel is look at it into more a holistic approach where we just focus on, you know.

The data that, that we, we, we care about as, as a whole. So, so, so it's like coming from a, being deterministic to more probabilistic maybe. Is this where, you know, we, we are heading 

Jeff: Yeah, no, it, it certainly is. And our clients. Look at the [00:28:00] data on a holistic basis, and if there's any marketers that are listening that are still doing things where certain channels have one ROI versus another.

So one channel has an allowable CPA at one level and the other one has a different CPA and they blend the, they they, they try to come up with this crazy mess. The important thing to know is that the CFO and finance. Looks at things holistically, meaning I give you x amount of dollars, this is the return I'm expecting on it.

I don't care how you spend it or what you do with it. Uh, and those kind of crazy methodologies. It's less probabilistic and it's more of marketers trying to figure out what's working and what's not working and how to make things work with tools that are not giving the proper numbers back. That's really what it is.

It's, it's, it's kind of a hodge po. Of putting together of the numbers that, and it leads to problems for marketers 'cause it leads to them in the wrong direction [00:29:00] and it leads to them where the numbers don't add up to the same numbers that finance has. And that's where we run into problems with getting bigger budgets.

So the key is, is that the holistic approach is the way to look at it. And, and I'm not just talking on paid media. Because for example, let's say you've got all this paid media that's going on, but you're also, your company is spending, let's say, $200,000 a year on a PR team and you're running public relations.

Well, that's marketing cost. And they should get a piece of the pie. They're obviously having an impact. We can account for those types of, of marketing activities. Also in our model, a lot of companies also have a full team that does all the organic content on paid on, on the organic social, like. Facebook, Instagram, and, and x, uh, we can account for those in our model.

So the, the more holistic look you get, the more correct view you have of [00:30:00] what's actually working so that you can, you know, push the pedal to the metal, uh, and, and move things forward, which is what most brands really want today. Absolutely. 

Mehmet: Now one thing, so, so what you're doing, Jeff, and we talk about is.

You're literally, you know, uh, disrupting the status quo, right? Somehow, um, talking to a lot of, of people, you know, founders, entrepreneurs like yourself, um, you must be educating the market a lot. So who are you talking? To the most. Are you convincing more CMOs? Are you convincing more CFOs? Are you convincing boards?

Like who, where do you spend most of your time educating the market about this shift that's happening? Of course, before positioning what, what you can offer today. [00:31:00] 

Jeff: So CFOs, they get it. They understand the need for marketing accountability. Uh, what they don't get is they don't understand. They've been told that it's impossible.

They don't understand that the math is out there and that what we're doing can be accomplished. That's the first thing. Uh, so we're talking to a lot of CFOs to say, Hey, you know, this, this stuff, the math is, this is, this math has actually been around for a while. We, you know, we actually could have done this 10 years ago, but it would've taken years to complete the mathematical calculations.

With ai, machine learning and the, the speed of today's compute computing power, uh, we can actually get it done an hour so we can actually do what we've always wanted to be able to do. So that, that's the first piece of, of who we're doing a lot of speaking with. Marketers themselves are aware. In fact, any marker that is not aware of this, I, I don't know where they've been living.

They've had their head in the sand. They're aware. Most of the [00:32:00] educating that we we do to them is, is talking to them to, to how to build out a case internally for this shift. Because, and this is the issue, is that a lot of people think, well, we should just change measurement. Let's just shift it. We're using GA four, let's just shift to lytics or something else.

What's the big deal? Well, the big deal is that bonuses, jobs, uh, quarterly numbers are all tied to measurement. Measurement when it comes to marketing within an organization is like a religion. You can't expect people to change their religion overnight. You're gonna have some detractors. You're gonna have people that are unhappy.

You have to slowly build consensus that the other view is not wrong. I will say, I'll be the first one to say that our view at Protic is not right. In fact, no measurement on marketing is correct because I can't get inside your head [00:33:00] and figure out exactly what made you decide to bot purchase one product or another.

What I will say is that the movement of marketing measurement should be that we should strive as marketers, that every month, every quarter we are, we are trying to improve. The way I like to say it is that our approach at Lytics, I will guarantee, the guarantee I make our clients, is that it is less wrong than what they're doing today.

It's not correct. Anyone who says things are correct is, is. It's a falsehood. These are models. They do a great job of explaining things and we can prove that it's less wrong than what they're doing today, and that is we feed more data into the model. It gets smarter and smarter every week and every month, but it's never gonna be absolutely correct.

But we, we strive that it will be less wrong every time we give it more data. Cool. Jeff, 

Mehmet: [00:34:00] question also that came to my mind. Um, when we talk data, some. Business owners, decision makers will tell you, Hey, like, uh, listen, I don't have much resources. I don't have data scientists. I don't have, you know, people who, who can, I don't know, clean the data and do all this.

Uh, would, would prolix be the answer for them? Um. If they don't have enough capabilities themselves also to, uh, to understand, you know, the, the data landscape that they have today. 

Jeff: A a absolutely that's part of, so there's the, the, the SaaS aspect of it, which is we hook it up, it brings in the data and it, and then it displays the results of what happened and recommendations for what to do next.

A dashboard, like a Looker studio type environment or any type of internal dashboards. But you bring up a great point, which is [00:35:00] I, I want insights because that's what people really want, is they want insights. Or I have a meeting in two hours with my CMO and I would love to have a bullet point or two about what's going on right now.

And in fact, that use case right there. Is a perfect use case for a large language model, like a chat GPT or something today. In fact, what we've been data testing now for some of our clients that have requested it, is we've trained a, um, an LLM on their data set, and we've made it available where they can ask questions via text.

That's their requested methodology and it's like, Hey, I, I have a meeting with my CMO. Mm-hmm. Give me a bullet point about last week's data that will make me sound smart. Intelligent because that's what people really want is some insight. Give me a nugget that I can pull out Absolutely. To use in a meeting.

And that's really the key is that when uh, companies [00:36:00] are lacking like an analyst team, uh, analysts, their job is okay, here's the data in the dashboard. So we provide not only the, the, the output, but also the insights available there. And like I said, we believe that in the future, that's what an LLM will be perfect for.

Great. So if I 

Mehmet: want to now have a bird eye view on the MarTech, which is like marketing technology landscape, what you're doing already, Jeff, is, as I said, it's a disruption, is changing how people. You know, kind of memorized how to do things. So, and you mentioned AI a lot and LLMs. So what are other trends, you know, as marketers we should keep eye on, uh, and maybe something you're currently working on at 

Jeff: Protic?

Well, I, I think one of the biggest things that's been forgotten by most marketers is that [00:37:00] we, we spend a lot of time at Lytics talking about ad effectiveness and how to improve the return on marketing investment. And that has to do with how much money we're spending, how many impressions we're generating across the different campaigns.

But people tend to forget that the message. That is behind those ads. The ad itself is 60 to 70% of the effectiveness, and there's been a trend over the last several years that ads are hyper-focused on benefits, discounts and things like that. It's almost as though a whole generation of marketers either we're never taught or have forgotten that there's an emotional component to advertising that's hard to quantify.

That connects someone, and we know that when you can make someone laugh or make them cry from an ad. Right. They'll better remember your brand. They'll remember the ad, and when they're in market later [00:38:00] on, they'll have a better preference for your brand. So what we're working on is helping our brands better understand the makeup of their ads, the makeup of the messaging, what messaging in the ads is working better than others.

Then also how to take those components within the ads and use that to then iterate new versions of creative. But it's that creative piece that I've seen has been stale probably for the last couple of years. I would even say the better part of a decade for most advertisers. There is a trend right now in meta.

To have these AI generated ads where yeah, they will, you know, change the color, do all sorts of things, and. Meta because of the amount of information they know on, everyone actually has the capability where they're going to take your ad. And for every single person who sees the ad, the ad will be different.

So if you're [00:39:00] a dog person and my ad has cats in it, you're gonna see dogs in it for. And, and let's not forget that for meta, 90% of their advertisers are not the large brands that we work with. They're the local brands like a florist or a dentist. So for them that's perfect. For larger brands, they need to have more brand control.

And for them, it's the type of analysis that they need to do to in order to properly iterate. Because platforms like meta need lots of versions of creative in order to really, uh, train their engine and get it moving. Jeff, 

Mehmet: when you were talking I was thinking like, um, because you were mentioning brands, right?

If I am in B2B space, can I still benefit from this? 

Jeff: Yes, you absolutely can. What's important though, to understand is to understand the B2B journey. Okay. So, and for most B2B, there's two [00:40:00] aspects. You have your salespeople, like your outside pal, salespeople that are generating leads or deals, and on the same time, you also have a marketing team that's out there.

That's generating awareness. They're, they're at events, they're doing ads online, and they're generating marketing qualified leads to come in. So there's, there's two ways that leads can come in and marketing can touch both of them. Salespeople can touch both of them. And what ends up happening is that the platform where we've tested it is perfect in terms of showing you how marketing is impacting the leads that are coming in.

But in B2B, once a lead comes in, there are then multiple interactions with salespeople that when you talked about like data, bringing data into the platform. Those sales conversations are very difficult to quantify and to explain to an engine like ours. And so what we recommend is is [00:41:00] that for lytics, the things that you can either accelerate or decrease on as your paid media.

That's where Lytics really does a best job for B2B marketers. So it does great in terms of telling you about how marketing is impacting both your sales leads that are coming in through outside and your marketing qualified leads that are your inbound leads. After that, you know you've got your averages.

You can figure out how it goes from MQL to SQL, then eventually to a closed deal. But you should use our type of engine at that kind of MQL stage, if you will. 

Mehmet: That's still cool, right? So Yeah, absolutely. Yeah. Um. As we are almost close to the end, Jeff, like, um, one thing, which as as a CEO in, in a MarTech, you know, a business and you are a marketer yourself also as well.

[00:42:00] Um, final things you maybe want to share with fellow entrepreneurs in that space. Um, final thoughts and of course like. Tradit thing. I ask all my guests where people can find more and get in touch. 

Jeff: Yeah, the the best place to reach me. You can find me always on LinkedIn. I love chatting with other entrepreneurs, definitely about their journeys as well.

To learn more about Pro Lytics, you can go to pro lytics.com or you can go to get prova.com. That's G-E-T-P-R-O-V a.com. When you're there, we have a resources section and we have an attribution certification course. You know, my belief is that the best way to understand what's coming next is to study the study history.

So we've put together a course that talks about the whole history of marketing and marketing measurement, you know, the, the past, the present, and what we see as the future. And we hope that helps marketers kind [00:43:00] of look their way to the future. It's no cost. Take about an hour and a half, and at the end you get a certificate to share on LinkedIn if you pass the test.

Yeah. Any final thing you want also to share, Jeff? No, I think that's about it. Listens. It's been great to meet you, man. Man, I mean, the most important thing, like you said, this is a disruption. Marketers definitely have to wake up and understand that if they wanna check out and live in the world of these really new, cool channels, you, you can't account for it.

And Google Analytics for you have to do something different. And Lytics is one of many solutions out there. So open your eyes and start figuring out how to do things differently. 

Mehmet: Absolutely. Well, Jeff, really, I enjoyed the conversation. I learned tons of things from you today, so thank you for sharing that.

And for the folks, all the links that Jeff mentioned, they will be in the show notes if you're listening on your favorite podcasting app. If you're watching on YouTube, you'll find them in description [00:44:00] and uh, again. I, hi. I will end all my episodes. Uh, so this is for the audience. If you just discovered this podcast by luck, thank you for passing by.

I hope you enjoyed If you did, so, give me a favor, subscribe, share it with your friends and colleagues. If you are one of the people who keep coming again and again, thank you very much for all the support, for all the help, and also for recommending us to other guests. To other audience also as well. And don't forget, um, finally and after long, long, long wait, uh, the, the book is available, so from Nowhere to Next is now available on amazon.com.

Go grab your copy today and just let me know what you think. I tried to, to take all the knowledge I grabbed from my guest, including you, Jeff, maybe in part two of the book in like a very short book where I share what I learned. So. Just grab your copy, and as I say, always stay tuned for an episode very soon.

Thank you. Bye-bye.