Media Monitor's Conversations at Cannes: Metrics that Matter with Preeti

AI is giving marketers faster access to data and insights. But having more data doesn’t automatically lead to better decisions.

In this episode of Media Monitor's Conversations at Cannes, Guideline Chief Product Officer Steve Silvers sits down with Preeti Croke of Analytic Partners to discuss how AI is changing marketing measurement, why ROI requires more context than a single number, and what happens when marketing, finance, and analytics aren’t working from the same definition of success.

Preeti shares how Analytic Partners is using AI through tools such as Ask Genome to make insights more accessible and decisioning AI to support scenario planning in an increasingly uncertain market.

The conversation then turns to a bigger measurement challenge: despite the amount of data available to marketers, marketing investment can still be perceived as less data-driven than other business decisions.

The issue may not be a lack of data. It may be a lack of shared language.

Steve and Preeti discuss why CMOs, CFOs, finance teams, and analytics leaders need to agree on the business outcomes they are trying to influence before deciding which metrics matter. They also discuss why measurement should account for far more than advertising alone, from competitive conditions and pricing to broader economic and market forces.

In this conversation:

• How Analytic Partners is applying AI to marketing intelligence
• What Ask Genome brings to ROI benchmarking and decision-making
• Why scenario planning is becoming more useful in uncertain markets
• The disconnect between having data and making data-driven marketing decisions
• Why marketing and finance need a shared definition of success
• Moving beyond KPIs that don’t connect to business outcomes
• Why Analytic Partners uses “commercial analytics” rather than simply marketing mix modelling
• How non-marketing factors can influence business performance
• Why finance should be part of the measurement conversation from the beginning

Media Monitor: Conversations at Cannes is a special summer series featuring conversations with media and advertising leaders about the ideas shaping the future of the industry.

If you’d like access to the benchmark report or want to suggest a topic for the next part of the programmatic series, reach out to press@guideline.ai.

If you enjoyed this episode, be sure to follow or subscribe so you don’t miss future conversations on advertising, media strategy, and cultural marketing moments.

And if you’re listening on Apple Podcasts or Spotify, a quick rating or review helps more people discover the show.

Transcript

SPEAKER_00 0:08

You're listening to Media Monitors Conversations at Can, a special series that we're dropping just this summer. Thanks for tuning in and listening to this week's Media Monitors Conversations at Can. You're about to tune in and listen to our Chief Product Officer Steve Silvers interview Preeti Croak from Analytic Partners.

SPEAKER_01 0:32

Hey, welcome to Media Monitor here in CAN. I'm Steve Silvers, Chief Product Officer from Guideline. And with me is Preeti Croak from Analytic Partners. Preeti, how is your can? First of all, thanks for having me. So, you know, obviously we're here, we've been talking about AI all week. I have to imagine that out on the Closette, you've been talking about AI. What is your AI talk track here in CAN? Like what are you guys talking about?

SPEAKER_02 0:58

Yeah. So I think, you know, Analytic Partners, we are, we're managed service, so we have a platform, of course. And really we're we've been using AI, of course, everybody, everybody is. But we've been using it kind of really in a couple of ways. So and really it's all focused about around how to drive quicker, faster value for our customers. So I'll I'll talk about a couple of the ways that we're using it. There's there's many, of course. But the first is um something we have called Ask Genome. Um so it's really all about accessibility. So in this case, accessibility of insights uh to our customers. So you can think about that as um kind of going in and querying and getting kind of your insights at the tips of your fingers versus having to dig through different reports. And that's really kind of powered by our ROI genome, which is uh we we kind of have mapped the genome of ROI, is kind of what we say.

SPEAKER_01 1:45

But it's a genome or genome.

SPEAKER_02 1:47

Genome, genome, yeah.

SPEAKER_01 1:48

Like a DNA. Yeah, like DNA.

SPEAKER_02 1:50

Like the DNA of of your ROI. And really it's uh what that is in real terms is kind of our 26 years of collective intelligence. So our domain knowledge. Uh and it's like a benchmarking. Yeah, but it's more uh so it's kind of more intelligently badged benchmarking. So it's based on a number of parameters that we know drive ROI. So say your, I don't know, Coca-Cola. Maybe Coca-Cola is a bad idea. Uh big red spaz here, financial services company that wants to know like how high is high, how do I rank versus other uh companies in the space, which country are we in? So it kind of takes different markers like you know, the amount of spend, the competitive landscape, the country that you're in, the industry that you're in to make kind of more of a intelligent.

SPEAKER_01 2:31

So everyone gets their own custom model, but you have knowledge and insights from all the years of experience that you have so that they can compare their custom model and their outcomes to what someone in their vertical could.

SPEAKER_02 2:43

Exactly. So if we go back to Coca-Cola again, if you're a small self-drink entering the category, we wouldn't necessarily give you Coca-Cola as a benchmark because you're not that doesn't make any sense, right? So it's more um understanding what those nuances are so that you can uh better understand what what you can do, how high is high, what are best practices. And that's all kind of we're surfacing that through Ask Geno, which is our uh the AI component, which again is making those insights more accessible to people, not just analysts, but also like finance leaders or commercial decisioning folks who can actually use the results for a natural language tool. Exactly, yeah. So that's one. The other one I'll talk about is decisioning AI. So that's more um within our platform, you can run scenario planning and optimizations. So our platform is called GPS Enterprise, and our customers are using it every day. So there's thousands of optimizations and scenarios being run over time. So we have all that kind of knowledge of what are uh types of scenarios being run or what are common things that folks are looking at. So we can actually surface uh common scenarios or things you might want to look at in terms of you know changing market dynamics. And I think that's really important because we're seeing a shift uh like away from just forecasting more to kind of scenario planning, so more multiple futures, especially with all of the things going on in the world today. You can't just have one number, you kind of need to understand how do you plan for different pieces.

SPEAKER_01 4:03

There is a there's a lot of uncertainty.

SPEAKER_02 4:05

Exactly, a lot of uncertainty. Uh so I think with that, it's allowed our customers to very easily kind of go in and plan versus and kind of use common scenarios and learn from each other within the technology. Um that's how AI is that sounds very cool.

SPEAKER_01 4:19

Now just to step back for a minute, previously you were talking about ROI analysis. And obviously, or maybe not so obviously, sometimes ROI can be hard to define. You know, you guys have done a bunch of research around trying to get the CMO buying center and the CFO buying center better aligned so that we can have better conversations about ROI. What can you tell me about that project?

SPEAKER_02 4:44

Yeah. So we actually just launched, uh we've been doing a survey called the Commercial Decisioning Survey. So we've interviewed, I think it's 455 leaders in marketing, finance, and analytics. So kind of evenly split across. And there's some really cool findings. So what we're finding is there is alignment. It's getting closer, but it's not quite all the way there. So one thing we found, which I think is very interesting, is so marketing finance are and finance are working closer together. We know that. So less in isolation. But we have some agreement that marketing is a growth driver, which is great. But we're actually seeing the finance and analytics are saying that more often than marketing themselves.

SPEAKER_01 5:21

So which is you think marketing would be better at marketing what they do.

SPEAKER_02 5:25

Yes. So that to us was like that was a really interesting finding. Um the other thing that we're seeing is that when making investment decisions behind marketing, it's listed as one of the least data-driven, which is fascinating, yes, across across the everybody that we we interview. And so this is not just our clients, this is like a more of a macro survey.

SPEAKER_01 5:43

Um, I'm just wondering why that is. I mean, we as an industry have been generating lots of data for a really long time. Like we're a big data industry. Yeah.

SPEAKER_02 5:55

And I don't think it's a data problem. Okay. Definitely not a data problem. I think it's a common language problem. So a shared language problem. So there's like a credibility gap that I think we're seeing where they're a lot in practice, but the actual uh implementation is is more challenging. And I think it's like kind of getting alignment across those functions and bringing them closer together in terms of what outcome are we trying to drive, what data we do we need to do that. So what's the data to make the decision, not just getting data in a dashboard, but what how do we actually use the data and how we get it in format to actually leverage it for results? And I think that's where the bridge is needed. Trevor Burrus, Jr.

SPEAKER_01 6:28

It's interesting. I mean, one of the things that that we're I was talking about with I think it was on other podcasts that I don't remember, is that a lot of times if you give a if you have a metric, you can optimize for that metric. Yeah. If you don't have absolute metric, it is hard to describe something as we're I think we're talking about quality. Yeah. Right? You're talking about it's hard to have quality as a metric. It's easy to have a number as a metric, but quality is hard. Is is there is there a metric mismatch here? Are we optimizing on the wrong metric? Because if if we'd used a different metric with the CFO buying, would the CFO believe our models more if we optimize for something different?

SPEAKER_02 7:08

I think it's I think it's less about what the CFO wants and more about what is like can everybody align on what that is? Right. And that's where uh we've seen the most success with organizations is when they can align together on like what is that goal that we're trying to hit. It could be multiple goals. It might be m you know, managing across multiple KPIs and waiting. But you really need to like determine what the outcome is or else you're never gonna you you can have all the best data in the world, but you're gonna not make the best decisions, right? So I think that's really it's the shared language, it's it's less of a data challenge and it's more of a change management challenge or an organizational challenge, I think it's also it's interesting that you bring that up, right?

SPEAKER_01 7:42

Because you know, in my experience having worked at measurement companies before, you know, producing a report is easy, but you know, socializing that the that those results and getting the change management in the organization to change things could be really hard. Like you could tell people you know, so you're blue in the face that you know CTV is more efficient than linear and you go every day, everyone's this great, and then the next year comes around and they don't move the money to keep it where it is, right? So are adoption is strategic. Yeah. So i is this something that that you guys, the analytic partners, are helping your customers with? Are you helping them shape their conversation to talk to the CFO in a way that they understand?

SPEAKER_02 8:27

Aaron Powell Yeah. So I think it's it's kind of a misunderstood topic in that it's not necessarily about like the best sash boards or understanding exactly what's going on in the models, right? It's more about, again, kind of going back to that shared understanding, like understanding every like your finance team knows modeling, right? They're forecasting, they're doing their own plans, they know how a model works. It's less about the They're probably better at math than we are. I think it's more about like understanding, can we all rally around this? We know that there's assumptions involved in any single model. There's no perfect model. No. It's there's just useful ones, right? So can we rally around what that shared understanding is, how we use the model results or the measurement results? And then also can we know as a group across marketing finance analytics, when we use the results, when we challenge them, and how do we make real change? So I think it's it's more of that dynamic and getting on the same page. And we kind of find that it works best when you have finance in the room from the very beginning. Right. Because then you can actually talk in those terms, you can get their questions, you can discuss as a team. And actually getting them in the technology and using and scenario planning and leveraging, you can actually tie together with their own forecast. So their model uses a forecast, you can you can agree, maybe we connect the two and we have better assumptions from this model because we're controlling for all these non-marketing factors, or we have more data in the model and that sort of sort of piece. So I think it's again, it's like a it's not necessarily data literacy or dashboards or model. It's like just coming together on something we can trust and use and then knowing when to challenge and when to when to a common understanding.

SPEAKER_01 10:01

Common understanding. Yes, exactly. So you know, for people that are are trying to put a measurement regime in place, like is that your best practice now? Is it is a recommendation that it's not just a marketing activity, that it is marketing and finance and analytics together? And are you are you making that part of a best practice as you interact with clients going forward?

SPEAKER_02 10:24

Yeah, 100%. So we actually don't even call it marketing mix modeling analytic partners, we call it commercial analytics. Um, because we really believe you have to measure everything that's going on in your in your ecosystem, whether it's marketing, whether it's gas price changes, whether there's a war in the Middle East, like those are all important things you need to control for and understand and plan against. And and and you know yourself, like the marketing only drives sometimes 10 to 20 percent of the business. So that 80 percent you really need to know. Right. Um in order to understand what you need to do with that 20% to um upset.

SPEAKER_01 10:56

Some of that's brand, some of that's organic. You're the first thing I saw when I walked up to the shelf. It's kind of hard to pull those apart sometimes.

SPEAKER_02 11:04

Exactly. Yeah.

SPEAKER_01 11:05

Awesome. Well, this was super interesting. Thank you so much for coming in.

SPEAKER_02 11:09

Thanks for having me.

SPEAKER_01 11:10

You I'll see you out there on the closet.

SPEAKER_02 11:11

Sounds good. Thanks, Steve.

SPEAKER_00 11:13

Thank you.

SPEAKER_02 11:13

Cheers.

SPEAKER_00 11:16

Thanks for listening, and don't forget to also subscribe and listen to our Wednesday podcast, Media Monitor, where we go deep on trends, look into the data, and try to understand where the media market's going.