In this episode of the Executive Connect Podcast, Melissa Aarskaug sits down with Jeff Greenfield, CEO of Provolytics, to unpack why marketing ROI has become one of the biggest trust gaps inside modern organizations. Jeff explains how fractured data, outdated attribution models, and over-reliance on clicks have caused finance teams to lose confidence in marketing performance. They explore how AI-powered modeling is helping marketing teams move beyond cookies, rebuild credibility with CFOs and CEOs, and finally connect media spend to real business outcomes across digital, retail, and marketplace channels. This conversation breaks down what AI actually means in marketing today, how privacy changes accelerated innovation, and why AI should elevate marketers rather than replace them. If you are responsible for marketing spend, executive decision-making, or proving growth impact in a privacy-first world, this episode will reshape how you think about measurement.

Chapters:

(1:31) The Measurement Crisis Explained

(2:40) How Digital Broke Attribution

(4:47) Rebuilding Measurement With AI

(6:30) Privacy Changes And The End Of Cookies

(9:17) What AI-Powered Really Means

(11:21) Measuring Marketing Without Clicks

(13:23) Proving ROI To CFOs And CEOs

(17:09) AI Elevates Marketers, Not Replaces

(22:41) The Future Of AI-Driven Media Buying

(27:46) Final Advice For Finance And Marketing

Jeff

(0:00) The level of recommendations that we’re able to provide, even today, are at a level of granularity that most marketers cannot execute upon. (0:08) But our tool can actually go in and tell you, you need to spend X on this day, Y on this day. (0:15) And marketers have a lot of difficulty in terms of executing things at that level.

(0:20) It’s just, it’s too much for them to figure out. (0:25) Folks can’t go in every day and adjust budgets like that. (0:27) AI could do that.

Melissa

(0:29) Marketing’s biggest problem was never just about the data. (0:34) It was about trust. (0:36) Enter Jeff Greenfield, the CEO of Provolytics, who not only tackled the measurement crisis, but rebuilt the C-suite’s faith in marketing itself.

(0:50) Forget cookies, forget outside models. (0:53) Today, we’re diving into how AI is helping CMOs, CFOs, and CEOs finally speak the same language. (1:03) And it’s not binary, it’s results.

(1:06) So if you’ve ever questioned your marketing ROI, buckle up, this episode might change your entire playbook. (1:14) Welcome today, Jeff.

Jeff

(1:17) Thank you so much, Melissa. (1:19) It’s a pleasure to be here.

Melissa

(1:20) I’m so excited to talk with you today. (1:24) Jeff, let’s get right into it. (1:27) Marketing’s measuring gap just isn’t about data issues.

(1:31) What is it? (1:32) What is going on with marketing today?

Jeff

(1:36) It’s really a matter of trust. (1:38) Marketers for a number of years have been talking about how they’re data-driven, but the reality is marketers, we don’t really understand numbers. (1:48) We’re creatives.

(1:49) Creative folks are not good with numbers. (1:52) And the biggest issue right now is that for most marketers, there’s no single source of truth. (1:58) There’s like 10 or 15 of them.

(2:01) And when we go in and talk to finance, finance laughs and kicks us out of their office. (2:06) And that just is a breakdown of trust across the whole organization.

Melissa

(2:12) So trust, big issue these days. (2:16) So let’s talk a little bit about the story behind that and how your company can help fix that.

Jeff

(2:26) Yeah. (2:26) Well, really, if we kind of step back a little bit and pan back the camera, we have to understand that marketing has gone through this massive paradigm since the birth of digital marketing. (2:40) It used to be as marketers to send out messages and gain new customers and build a business.

(2:47) We only had like three or four places to advertise. (2:49) It was TV, radio, print. (2:51) And these things were a lot of work to put into place.

(2:54) And once a spot was on TV or on radio, we’d kind of sit back and wait three or four months to see if it worked or not. (3:02) And we would use math and statistics to determine that. (3:06) And it was all aligned with finance and everyone was working well together.

(3:10) And then when digital marketing came out, everyone was excited because we felt like it gave the appearance that we could actually account for things a bit more because we could actually get a report that said we got so many clicks or we did all of this stuff. (3:26) And now the number of places that marketers can purchase, it’s in the hundreds of thousands, a multitude of different channels. (3:35) But what that means as well is that the measurement has not caught up with it.

(3:38) And so now what ends up occurring is if I actually had a thousand sales in a day and I look inside my cash register, if you will, my digital cash register and said I had a thousand orders. (3:52) When I go to Facebook and I go to Google and I go to all of these places and pull my reports to look at my numbers, it doesn’t say I had a thousand sales. (4:01) It actually said I had eight or 9,000 sales because Meta, Google, they don’t know about each other.

(4:08) They don’t know that I’m also running Amazon and Walmart. (4:11) Nobody’s talking to each other because they have their own silos that they all live in. (4:17) And then it lands on me, the creative, to try to figure out the numbers.

(4:22) And, you know, I’m lousy at that. (4:24) Marketers are just not good at that. (4:26) And that has led to this breakdown in this loss of trust from a financial standpoint of view.

(4:33) And so what we do is, is that we’ve been in this space for a long time. (4:38) We saw these changes coming that were happening with privacy that made this even more of a difficult problem. (4:45) And we’ve gone back to the future.

(4:47) We’ve taken the statistical modeling techniques that we used before digital. (4:52) We’ve brought them forward with AI. (4:55) But we’ve also modeled into it this concept of giving tactical recommendations that today’s digital marketer needs in order to do their job.

(5:07) That’s what was missing from the techniques of the past is that it was high level, just designed for the C-suite. (5:15) But today’s marketers are like, they’re in there. (5:17) They’re turning the dials and they need measurement that gives them that level of granularity.

(5:23) So we’ve taken from the past, brought it forward, augmented it with AI, and it delivers a package that provides that on the ground marketer with the tools they need, but also elevates up to the C-suite. (5:36) So it regains the trust that finance has in marketing.

Melissa

(5:42) I love that. (5:43) It’s like the death of the cookies, a catalyst, not a crisis. (5:48) I wanna talk a little bit about this end of the third party cookies and what York organization can…

(5:58) It seems like it originally was a moment of setback, but now it’s actually a moment of opportunity. (6:04) So how has the industry shifted towards privacy, actually pushed innovation with AI forward? (6:12) Can you share a little bit about your insights?

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Jeff

(6:30) Yeah. (6:30) So this all started because of what happened in the EU a number of years ago. (6:35) And the idea was that the EU kind of came out and said, hey, you’ve got all these profiles of customers.

(6:44) Well, they’re gone. (6:46) We’re getting rid of them. (6:47) You can rebuild them, but you now have to ask for permission.

(6:52) And a lot of companies based out of the US that operated in the EU, they just said, nope, we’re gone. (6:58) We’re leaving the EU. (6:59) It’s too much work.

(7:00) And those kind of changes made their way over to the US. (7:04) And one of the first privacy regulations happened in California, but they did a little different. (7:10) They just said, well, you need to give users the opportunity to opt out.

(7:14) They can amend information or they can say, get me out of there. (7:18) But they don’t have to opt in to begin with because that’s California. (7:22) But where this really got crazy is there was an iOS update, Apple, who’s an international company.

(7:29) And everyone remembers that update where all of a sudden you went to Facebook and Instagram and there was a pop-up that said, do you want them to track you across other apps? (7:37) And of course, everyone said, absolutely not. (7:41) Of course, people didn’t realize that they were able to do that before that update.

(7:44) So that opting in was very similar to what happened in the EU. (7:51) And then we got the death of the third-party cookie, which essentially is controlled by Google and their Chrome browser. (7:59) And they said, we’re going to get rid of it.

(8:00) And then they said, we’re not going to do it yet. (8:03) And this went on for a series of three or four years. (8:07) It really gave the industry time to kind of prepare for what was coming, and it still hasn’t come yet.

(8:15) But I think pretty soon in the next year, you’re going to go and reload Chrome and you’re going to get that same pop-up that’s going to ask you, do you want to allow sites to track you across other sites? (8:25) And everyone’s going to say no. (8:27) And that’ll be the final nail in the coffin.

(8:29) But it has given the industry a number of years to kind of prep for it. (8:35) The way we prep for it is that we saw the direction it was going in. (8:40) There’s no longer a way that you can track users.

(8:43) That’s not the way forward. (8:46) The way forward is you have to step back, pan the camera back, and look at aggregated daily data. (8:53) Now, the question is, how can you take aggregated daily data and get the type of insights that we have?

(9:00) And the answer is, use AI to fill in the gaps. (9:03) I mean, they’re using AI to figure out.

Melissa

(9:06) And I hear this word a lot, AI-powered. (9:10) AI, it’s thrown around a lot. (9:12) What does that actually mean?

(9:14) Help me understand what AI-powered means.

Jeff

(9:17) Yeah, well, that’s a great question because the truth is, most of it is marketing speak. (9:23) Let’s be honest. (9:24) It’s kind of like when blockchain was the big thing.

(9:27) All of a sudden, overnight, every company was running blockchain. (9:32) Before that was the cloud. (9:33) So there’s all of these marketing terms.

(9:37) The reality is that most of AI is actually what you would call machine learning. (9:43) And what I mean by that is that you have a program that’s running, that’s designed by humans. (9:50) And people always talk about the biases that are in AI because they are designed by people who have a perspective.

(9:57) And what we did with our platform is that we had a series of very sophisticated mathematical techniques. (10:04) And it was a series of four or five steps. (10:07) And at each step along the way, a human would have to come in, do an analysis, make some tweaks and changes, and then put it forth to the next step.

(10:16) And it was very time-consuming. (10:18) So we trained AI to actually replicate what a human was doing and learn from it. (10:24) And our systems get smarter as they go along.

(10:27) The more data that comes in, it actually gets smarter for every single one of our clients. (10:32) So that’s what AI is. (10:34) One of our chief folks here, he likes to say IA, Intelligent Assistance versus AI itself.

(10:44) But AI has become so popular. (10:47) But you have to step back and say, ChatGPT has grown up very quickly. (10:51) But there’s a lot of other companies that the level of sophistication that they say they have is not equated with what you and I think of what AI is, especially when we base it on like movies and things like that.

Melissa

(11:05) Yeah, absolutely. (11:07) I know from a marketing perspective, I want you to walk us through how Provolytics gets results without the cookies, the tags. (11:18) Like how does that actually work with your tools?

Jeff

(11:21) Yeah, so we’re looking at the relationships between the unified metric. (11:26) And this is important because most marketers today, they are addicted to the click. (11:31) And they’re so hyper focused on cost per click and everything they look at as cost per click.

(11:37) And a lot of finance folks, they live inside of Google Analytics. (11:41) It’s part of their internal dashboarding system. (11:44) And the problem with that is there’s no place for non-click media, like connected television, digital out of home, podcast advertising.

(11:54) There’s no place for these really cool upper funnel type channels. (11:58) And so when we built out the platform, we decided upon a centralized metric. (12:03) What would unify everything is the impression.

(12:07) So the platform is looking at the total number of impressions every single day across your entire media buy. (12:13) And the relationship between that and your main KPIs are conversion. (12:19) So when we think of e-commerce, we think of orders, we think of revenue, we think of how many items someone adds to their cart.

(12:26) And what it does is using these techniques, it actually goes through what we call simulations. (12:32) It’ll take a granular line item like a campaign, an ad group, a creative, it will compare it to every single other one that’s running. (12:41) And then it’ll remove it for a particular day.

(12:45) And it’ll ask the system to say, how many orders would we have received if this was not there? (12:51) And so it takes into account not just the immediate impact of that one line item, but also what we call the ad stock, which is the drag or the lag. (13:01) Because something can have, you can run an ad today, but it can have an effect tomorrow and the following day.

(13:06) And then it also looks at synergies as well, the combinations of things. (13:10) And by running those simulations, it’s then able to determine the incremental impact of every single one of those campaign creative combinations. (13:20) And it does it for every single one of them.

(13:23) And it has to run sometimes hundreds of thousands of simulations to come up with that final answer, which is here’s what’s working. (13:31) Here’s what’s not working. (13:33) And here’s what you should do with the money that you have left over and where you should reallocate it in order to get bigger bang for your buck.

Melissa

(13:43) That’s great. (13:44) And I’m glad you mentioned the dollar. (13:45) Cause I think that’s, you know, right now all the CEOs are, how much is it going to cost?

(13:51) And what am I going to get for the spend? (13:53) So, you know, it’s not just about the CMOs anymore. (13:57) You need to get the buy in from your CFOs and the CEOs.

(14:01) So I’d love to get your perspective on what’s resonating with them and how we can speak that marketing language to get the buy in.

Jeff

(14:11) Yeah. (14:12) And it’s a process to get buy-in because what ends up happening is that you have to start first. (14:18) You have to actually start from the bottom and work your way on up because you have to get consensus.

(14:23) Because in order for this to work properly, you have to get everyone working together and you have to get people following the recommendations. (14:32) So first starts with the internal marketing team, then starts with the agencies that are aligned with it. (14:38) Once everyone is on board, then you can work your way up to the CMO.

(14:42) And then at that point, now you’re talking the CMO to the CFO to the CEO. (14:48) Everyone is talking. (14:49) What is getting the C-suite excited is having a single source of truth where all the numbers are there.

(14:58) And what I mean by that is that if you’re an omni-channel retailer and you have your own website, but you’re also selling on Amazon, Walmart, CVS, and you have retail, typically the report that you’re getting from marketing is only reporting on the website. (15:14) And then there’s a separate report for the marketplaces like Amazon and Walmart. (15:18) And then the stores are completely separate report.

(15:22) And because they typically do not have marketing, does not have any insight in terms of how marketing is impacting in-store sales or the marketplace sales. (15:31) And so this, what’s resonating is there’s a single report, which is validated, that brings everything all together. (15:39) So you can accurately see, improve what’s working across everything.

(15:45) And to have it put all together is amazing. (15:47) So a single report that’s unified where the numbers actually add up, that is what’s resonating because it’s math at the end of the day. (15:56) And when the math works, everybody gets happy.

Melissa

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(16:23) Just visit moneyripples.com forward slash secrets and enter the promo code EXEC. (16:31) Simplicity, right? (16:33) I think it’s having one report is a lot better, like you said, than having to pull analytics from multiple different places.

(16:40) I want to talk about, you know, humans versus AI right now. (16:45) I think a lot of times there’s a lot of fear with AI that, you know, AI is going to replace marketers and people. (16:51) So we’re talking about elevating, not replacing people.

(16:55) So it seems like your product is empowering people. (16:59) So can we talk a little bit about how Provolytics elevates instead of taking jobs away?

Jeff

(17:09) A thousand percent. (17:11) One of the things that we’ve seen because I’ve been in this space since the early 2000s and back in the early days, especially of digital marketing, one of the coolest things that marketers did was that they would create different versions of creative. (17:27) Because when you think about before digital, most of the creative marketing was TV and print and radio.

(17:34) There’s a lot of work to create different versions, but with digital, it was pretty straightforward. (17:39) But as marketing has become more complicated, more math oriented, we’ve seen a decline in the different versioning, if you will, of creatives. (17:49) It’s just not happening.

(17:50) Back in the early days, the good agencies every month would produce 20, 30 different versions of creative, different colored backgrounds, different actors or actresses in the ads, lots of different versions. (18:03) And that’s a creativity part. (18:05) That’s been lost because of the level of complexity.

(18:09) And it’s been lost because marketers are stuck in spreadsheets instead of stepping back and closing their eyes and being creative. (18:17) So what Provolytics does is it does the math, it checks the math, makes sure the numbers are correct so that marketers can get back to doing what they’re supposed to be doing, which is creative and creating new versions of creative. (18:31) Because at the end of the day, it doesn’t matter where you buy, 70% of the effectiveness of all advertising is the messaging, the creative itself.

(18:41) What are you saying about yourself and how are you being presented? (18:44) So the color of the background, the person in the ad, the message that they’re saying, that’s 70% of the ad effectiveness. (18:51) And very rarely do we see people tweaking that like they should.

(18:55) You should be tweaking it every month and running tests on it. (18:58) But to be honest, they just don’t have the time to do that. (19:01) And they’re exhausted because of dealing with numbers.

(19:05) Provolytics coming in replaces that major task for them so they can get back to doing what they’re supposed to be doing, which is being more creative.

Melissa

(19:14) Yeah, it made me think when you were saying, I know I’ve used different AI tools to generate images and the tools and the image that was created, like words were misspelled or things weren’t 100% correct. (19:29) So we need the human to spot the incorrect pieces of these images and what we’re trying to create. (19:38) Have you seen that as well?

(19:40) Using these AI tools to create some images versus the creators creating it. (19:47) Any insights on that?

Jeff

(19:48) Yeah, the tools aren’t quite there yet. (19:52) They’re gonna get better though. (19:53) I think very quickly you’re gonna tell it what you want and it’s gonna spawn something.

(19:59) What I hope doesn’t happen is that folks go to rely upon those tools exclusively. (20:06) They come up with great ideas, but I think true creativity is not something that people create. (20:15) It’s something that just shows up.

(20:18) When you talk to great artists and you ask them what inspired them, they’re like, I have no idea. (20:24) This image just showed up in my head and I had to paint it on the canvas. (20:28) The same is true of poets and writers as well.

(20:31) I think that also extends into marketing and advertising when you get involved with a brand and you start to think of it, something pops into your head. (20:39) I think where AI is great for creative types is they can use it as a way to spawn ideas because you see something and you’re like, oh, that makes me think of this. (20:50) And then it becomes, you can use AI as almost like an editor or like a copywriter to help you iterate ideas very quickly.

(20:59) But there’s that human element that makes us very unique. (21:03) There’s something about consciousness that we’re able to connect. (21:07) We’re connected to every person and we’re connected to the world.

(21:12) Whereas this AI machinery is a machine that has taken in all this information but doesn’t have that same level of connectedness. (21:22) And I don’t think it ever will have what we have. (21:25) But I think the tools are gonna get a lot, lot better.

(21:29) But I think that we’re always gonna find that stuff that’s made by humans and spawned by humans and actually comes from a different place is always gonna be more effective than something that some bot created, in my opinion.

Melissa

(21:43) Yeah, no, definitely. (21:44) I think I’ve made the personal mistake of telling a tool to do something and didn’t go check it because I assumed they were gonna do it 100% correct. (21:52) But we need to check and balance.

(21:55) We need to tell the tool what we need, right? (21:57) The tool’s not telling us, we’re telling the tool what to create. (22:01) And it made me think the other day, for me, I know the Barbie in a box thing, right?

(22:07) Is a popular thing right now on social media. (22:11) I created my own Barbie in a box. (22:14) And man, if you wanna ruin your self-confidence quick, like my Barbie in a box wasn’t great.

(22:21) But I wanted to talk a little bit more about looking out further. (22:26) I mean, AI is evolving so quick, so much quicker. (22:31) How do you see it playing out further?

(22:34) Maybe a year or so as it relates to AI and marketing analytics?

Jeff

(22:41) Well, for us, the level of recommendations that we’re able to provide even today are at a level of granularity that most marketers cannot execute upon. (22:52) And what I mean by that is that most marketers, you give them a recommendation that says, hey, for this month, you should spend X percent less. (23:02) And they can do that.

(23:03) But our tool can actually go in and tell you, you need to spend X on this day, Y on this day. (23:10) And marketers have a lot of difficulty in terms of executing things at that level. (23:15) It’s just, it’s too much for them to figure out because folks can’t go in every day and adjust budgets like that.

(23:23) AI could do that. (23:24) And I think that we’re gonna be at a point where tools like ours are going to be connecting and directly to the platforms and executing those buys. (23:35) That’s what I think kind of the next level of this is, is going to be at that level.

Melissa

(23:41) Yeah, that’ll be interesting because marketing is a huge spend right now. (23:47) I see how much money in my industry, they’re spending on marketing, not just customer acquisition, getting customers, keeping customers, making sure that they’re front and center on their thoughts when they’re ready to buy. (24:05) I think that’s the key and that’s where the money costs.

(24:08) So if we can give and enable marketing teams to have the data to make the decisions, they’re more empowered to kind of lead that journey. (24:17) I know there’s so many different tools to decide on and they’re just getting harder and harder to pick and decide on which one works best for them. (24:26) This episode’s sponsor is a couple I absolutely love, David and Alexis Kiwi.

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Jeff

(25:06) Well, you hit right on it. (25:07) It’s that old adage from John Wanamaker, half the money I spend in marketing is wasted. (25:13) The only problem is I don’t know which half, but you hit on the most important aspect, which is you have to spend dollars to keep your message out there so that you’re top of mind when they’re ready to buy.

(25:26) And the problem with that, because that’s how marketing works. (25:30) That’s exactly it. (25:31) You buy marketing impressions.

(25:33) You’re buying eyeballs to build awareness. (25:36) And when awareness is built enough and they’re ready, they’ll walk into your store and they’ll be ready to buy. (25:42) The problem is connecting those eyeballs, those ads that you buy all the way through to revenue or to a lead, there’s that timeframe.

(25:51) And all of the tools that are out there are based on just tracking clicks versus looking at the relationship between those ad impressions you put out months ago and the revenue that comes in today. (26:03) And that’s where tools like ours that use very sophisticated modeling techniques. (26:08) And we borrow from the past of how we used to do things.

(26:11) You think about all the big brands like the Pepsis and the Cokes of the world and how they grew without having digital. (26:18) And no line of sight, they would run TV, run print. (26:21) And yet they were able to predict and build their sales over many, many decades using sophisticated math.

(26:28) And that’s essentially what’s happening today. (26:30) The only difference is the math is at a much higher level, more sophisticated. (26:34) It runs a lot faster and is at a much level, a deeper level of granularity.

(26:40) So it provides marketers the tactical data they need to make those on the ground decisions.

Melissa

(26:46) Which is key, right? (26:48) Like I’m as a person that likes to solve clients’ problems. (26:52) I see marketing as such an important part of the client journey.

(26:57) It is constant nurturing, keeping them in the know, touching them, letting them know what we’re working on, letting them know what conferences we’re gonna be at. (27:06) And I think keeping clients constantly touched through marketing strategies is really key because there’s so many options these days, right? (27:17) There’s so many different competing products that I love kind of having the idea of having the analytics so we can make the best decisions for the business and keep some of the money versus spending it all on marketing tools and many different reports.

(27:36) So I think that’s really interesting what you’re sharing. (27:39) I wanna get kind of any final thoughts or anything that we may have missed that you wanna share with our listeners.

Jeff

(27:46) Well, I think what I would say is that for anyone in finance, when you’re thinking about this in terms of how can I get a handle, a better handle on what’s happening from a marketing perspective. (27:59) And a lot of finance folks, they like to dig into data on their own. (28:03) What I would say to them is to reach out to the marketing team.

(28:08) And instead of looking at clicks, ask for the last 12 months of data, but ask for at an impression level, get the total daily number of impressions that have happened in the last 12 months and then start graphing that and look at the relationship between impressions, maybe visits to the website and then maybe to some of your KPIs and look at those different correlations. (28:31) And they’ll start to understand that that relationship that when you buy the ad doesn’t mean it’s gonna have an impact today, doesn’t mean it’s gonna have an impact tomorrow, may have an impact three months from now. (28:42) But I think it’s great for finance to start to understand those relationships and the timeframe between them.

(28:49) And it’s very easy. (28:50) It’s just a matter of getting the right data in front of them.

Melissa

(28:54) Yeah, I love that. (28:56) All right, Jeff, if you could leave our listeners with one core idea about how AI is reshaping marketing, what would that be?

Jeff

(29:05) What I would say with AI, the way it’s reshaping marketing is providing a better understanding of how marketing impacts the bottom line because it’s giving you a picture of what’s actually happening via AI and very sophisticated analytics. (29:23) You can actually see how ads that you’re buying, where you’re trying to tell people to go to your website, people are actually going to Amazon instead. (29:32) And ads on Amazon, where you’re telling people to buy on Amazon, they may be walking into your retail store instead.

(29:37) So AI is helping you to see and visualize these relationships and is increasing the confidence that the C-suite has back in marketing, which is a wonderful thing for everyone.

Melissa

(29:50) That’s great. (29:52) Jeff, thank you so much for being here today and sharing your knowledge. (29:56) How can our listeners connect with you?

(29:58) What’s the best way?

Jeff

(30:00) The best way is go to provalytics.com. (30:02) To make it real simple, you can just go to Get Prova. (30:05) Prova is proof in Italian.

(30:07) So that’s G-E-T-P-R-O-V-A dot com.

Melissa

(30:12) I love it. (30:13) Thank you so much for being here today, Jeff.

Jeff

(30:17) Thank you so much, Melissa. (30:19) It’s been a pleasure.

Melissa

(30:20) And that’s the Executive Connect podcast.