Are you sure you know where your marketing dollars are making the biggest impact? In an omnichannel world, pinpointing where your ad spend is most effective is tougher than ever. What if you could use AI to make it easier?
Welcome to today’s episode, where we’re discussing how to optimize marketing effectiveness through Marketing Mix Modeling, AI, and predictive analytics with Mike True, CEO & Co-Founder of Prescient AI.
Today, we’ll explore how to ensure your marketing dollars are delivering maximum return on investment and the role AI plays in getting it right.
Mike True is the co-founder and CEO of Prescient. Prior to starting the company in 2019, Mike was responsible for helping clients of App Annie, IBM, and Oracle generate millions of dollars in revenue through the implementation of various artificial intelligence and analytics solutions.
Resources
Prescient AI website: https://prescientai.com/
Wix Studio is the ultimate web platform for creative, fast-paced teams at agencies and enterprises—with smart design tools, flexible dev capabilities, full-stack business solutions, multi-site management, advanced AI and fully managed infrastructure. https://www.wix.com/studio
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Transcript
Note: This was AI-generated and only lightly edited
Greg Kihlstrom:
Are you sure you know where your marketing dollars are making the biggest impact? In an omni-channel world, pinpointing where your ad spend is most effective is tougher than ever. What if you could use AI to make it easier? Welcome to today’s episode where we’re going to discuss how to optimize marketing effectiveness through marketing mix modeling, AI, and predictive analytics with Mike True, CEO and co-founder of Prescient AI. We’re gonna explore how to ensure your marketing dollars are delivering maximum return on investment and the role that AI plays in getting it right. Mike, welcome to the show. Before we get started though, why don’t you give a little background on yourself and your role at Prussian AI.
Mike True: Yeah, so my name is Mike True. I’m the co-founder CEO of Prescient AI. Been in the tech space since 2010. Jumped around from IBM, Oracle, went in the mobile analytics space with a company called App Annie, now Data AI. And then got the itch to try to take all the learnings over those years and met my co-founder and started off on this journey. Day-to-day is a variety of managing our investor relations, company culture, product, and just making sure that everything is going in the right direction.
Greg Kihlstrom: Yeah, yeah, great. Well, yeah, so definitely looking forward to talking about a few things here with you. So I wanna start with just the overall topic of just how important it is to know where your marketing dollars are going. I know CMOs and marketing teams are under increased pressure to be able to show this. Why is it, from your standpoint, why is it so critical for businesses to have this deep understanding of where their marketing dollars are most effective?
Mike True: Yeah, I think it comes down to being able to do more with less. Marketing organizations are under a tremendous amount of pressure to deliver. It’s a highly competitive market. And having a strong understanding of where to allocate their media mixes and having confidence in what is that predicted outcome going to deliver, that incremental growth, with their marketing budgets that they have set each year.
Greg Kihlstrom: Yeah, and so some of this might be pretty straightforward to those that are doing this day in day out. But could you talk a little bit about what are the risks of not having clarity on where ad spend is generating returns? Particularly, we’re talking omni-channel, we’re talking new channels seeming to pop up out of nowhere, consumer preferences changing, all that kind of stuff. What do you see as the risks here?
Mike True: You know, it’s risky. I kind of associate this like gambling, right? When you are you’re gambling with dollars, right? Would you want to make the most educated bet that you’re going to, you know, deliver the results? You know, marketing in the measurement space is just rapidly evolved, you know, over the last 10 years with the ability to you know, scale across different channels. Traditionally, I’ve seen a lot of brands that have, you know, really grown their business on, you know, Google and Meta, you know, but there’s a requirement now to start finding new audiences, finding new customers, and that requires you to go into more top of funnel channels, things like, you know, linear, CTV, podcast, radio. You know, YouTube, TikTok, and having a pulse on the performance of those channels, knowing where to double down. If you don’t have that type of clarity, you’re going to miss opportunities and you need to keep up and it needs to be fast and need to be continuously optimizing. And so not having the right pulse on that and confidence is a really risky, you know, exposure for any business.
Greg Kihlstrom: Yeah. And so I think that’s a great segue here of talking about a way to do this. So marketing mix modeling. with the goal of optimizing that return on ad spend or ROAS. So can you talk a little bit about, that’s a lot of acronyms there, MMM, ROAS. Can you explain a little bit for those less familiar, what is marketing mixed modeling and why is it so important for optimizing return on ad spend?
Mike True: Yeah, the notion of an MMM or marketing mix modeling was really defined to help allocate budgets across and within various channels. And it’s using historical learnings, right? So it’s looking at all the historical data, the inputs or things like sales, the advertising spend, the platform reported metrics, seasonality component, word of mouth. And it’s really trying to make sense of the impact or the statistical relationship between your spend, in whatever KPI you’re looking to measure and optimize against. It doesn’t require any pixels or user level tracking where you would mostly see with like a multi-touch attribution. It’s trying to look at the holistic picture and trying to figure out like, where should you make that next bet? And then what was the predicted outcome of deploying budgets within those channels and campaigns?
Greg Kihlstrom: Got it. So then, you know, I want to talk a little bit about how you how you approach MMM at Prestian AI. So could you talk a little bit about that and how have you seen it transform some of the marketing strategies for your customers?
Mike True: This is my favorite topic. This specific question actually. The the the MMMs historically in the past, kind of kind of went full circle right in the 60s, they came out and you have a call it a retailer, you know, you’re selling a product or a razor blade within a brick-and-mortar store, they built these models to do media planning, right? And so they would run once a year, you gathered as much data as you could, and then it would produce this report and it would tell you, hey, here’s where you should allocate X amount on radio, billboard, TV, catalogs, newspapers, so on. There was no online then, right? So it was all offline and it’s trying to figure out, well, which one of these channels was driving and what’s most effective. I got really lucky when I met my co-founder, Cody. I like to consider him as like Michael Jordan of research in the space, in the sense of his level of talent, but also his competitiveness. And so a lot of the MMMs that you’d see evolved would leverage existing research papers and they would be enhanced. Well, Cody questioned the existing research papers and says, well, why does an MMM have to run once a year? Why does it have to run twice a year or even quarterly at a channel level? Why couldn’t you make a more dynamic MMM that was more like real time and more granular? And MMM, it runs every single day at the campaign level. It’s omni-channel. And so, you know, we’ve helped brands like Hex Cloud, Good American, Jones Road Beauty that are really omni-channel brands having a insight into how is my paid spend on connect to TV driving sales to their DTC store to their Amazon store. And now we’re offering a retail model as well. So you’re allowing to use an MMM for more dynamic optimizations. And so we’ve built what we call Halo effects and essentially a halo effect really helps brand scale top of funnel where We’re essentially taking credit from the bottom of funnel. So last click and we’re redistributing that up to the top of the funnel where we’re saying, Hey, we have the highest level of confidence that the awareness from this TV campaign or this Tik Tok campaign that was not clicked actually was what was the driver downstream for somebody that might’ve went to an Amazon store or went directly to their website and converted from there. And so, um, we wrote our own research. It’s fast, it’s granular, it’s omni-channel and it can be used to do really dynamic media optimizations versus a traditional MMM where you’re thinking of as more like long term budget planning has been incredibly effective for, you know, for the consumer and retail ecosystem.
Greg Kihlstrom: Well, yeah, and it also just seems like it’s that way of thinking is built for the way that marketing is being done more and more often. I mean, you mentioned like TikTok. So, you know, is the same TikTok video going to be playing for, you know, three months at a time? You know, so like doing is doing MMM quarter over quarter even gonna work in a tick tock environment versus you know, you’re talking if you’re talking daily, I mean, that’s, that’s about probably the attention span of most tick tock videos, right?
Mike True: You hit that spot on. We’ve seen it. What’s been very interesting for us is like, you know, more of the enterprise brands, they really have been honing in on, you know, a lot on Google, on branded search, and they just want a little bit of confidence. They know they want to start scaling into these new channels, but they just wanted a little bit more confidence to do that. We’ve seen some more of the, I’d say, the modern-day brands like the Hexclads of the world, which are growing incredibly fast, but they’ve taken this approach of adopting multiple forms of measurement. So, you think of an MMM that’s going to tell them how to reallocate their budget, do more dynamic optimizations, and then triangulating with other forms of measurement like incrementality testing to do holdouts to validate some of those results. And so we’re seeing NMMs just take this really new role inside of organizations and adopting it in a way where they can be more proactive and reactive at the same time using these models alongside other forms of measurement.
Greg Kihlstrom: Yeah. Yeah. Well, and, and of course, you know, attribution, nothing’s easy, but there’s some things are a little more difficult than others. Right. So, you know, when we’re talking about attribution and an omni channel environment, you know, this, this stuff can be particularly challenging. I wonder if you could talk a little bit about that part of it and just how, you know, even how MMM plays into that, but, you know, why is it. particularly challenging to understand attribution and effectiveness when we’re talking about, I mean, omni-channel marketing environments.
Mike True: Yeah, I feel the core source of attribution over the last years has been through GA, through the platforms, and through a multi-touch attribution solution where they are using a pixel. They’re very click-based deterministic forms of measurement. And when you think of an omni-channel brand, you know, if you see an ad on, you see a YouTube ad, right, and you don’t click it, but they go over to Amazon and make that purchase, traditional forms of measurement make attribution incredibly challenging, right? And so there’s this trade-off of a probabilistic versus deterministic, where you can tell, be very confident with a small subset of your conversions, but you’re missing the larger picture, especially for omni-channel brands. With an MMM, it’s not deterministic and it’s probabilistic, but it’s giving you that holistic picture. I like to think about like an MTA or Multi-Touch will tell you a whole bunch about a little, and an MMM will tell you a little about a lot. with our MMM, with the granularity and speed, we’re able to tell them a lot about a lot, essentially. And so I think that’s been primarily challenged of attribution. And then obviously you see things with like iOS 14.5, cookie deducation, you know, ad blockers. where these traditional forms of measurement were relying on these clicks and probabilistic, I mean, deterministic journeys. It’s just created what I like to say is like moving towards an anonymous internet. And so I think that’s why you’ve seen the role of MMM plus incrementality really, really come into the forefront today.
Greg Kihlstrom: And so how should businesses be thinking about this tracking? I mean, certainly it’s a different approach to do multi-touch versus MMM, but how does a business still ensure that accurately tracking which channels are driving the most value?
Mike True: Yeah, I like to say there’s no silver bullet with this. I, you know, I’ve heard like this, this source of measurement is my source of truth. And I fundamentally disagree with that. I’m saying that the source of truth is the marketer, right? They have a good pulse on every aspect of the business associated to all their marketing spend. And there’s different forms of measurement to help you triangulate with that, where, you know, an MMM is not trying to tell you that, you know, Greg saw this ad, this ad, this ad, and then converted where it’s trying to take more of a holistic approach. So, MMMs, if you’re going to look to scale into top of funnel, an MMM is perfect for you. So, if you have built your business and you’re heavily on meta and Google, right, you have a really good pulse for that using platform GA and MTA. But when you start going into these, you know, more view based channels out of home channels, right, this is where an MMM should be applied. When we work with our clients, right, you just don’t onboard with an MMM and say, click a button, we can run a simulation in 45 seconds. It’s going to give them a predictive media plan compared to their existing spend and just go nuts with it. You really want to understand which channels you’re looking to scale and be very methodical about you know, how you are reallocating these budgets and not making dramatic shifts and starting to learn, let the models learn, let the AI learn. And the more you start to make these changes, the smarter the MMM gets, and you can start applying this into new strategies as you expand your channel mix.
Greg Kihlstrom: Yeah. And so you kind of just touched on this, but I want to want to dive in a little deeper on the role of AI here, because, you know, certainly marketing mix modeling, you know, predates some of the recent AI hype. But as you’ve already shared, you’re using AI and MMM in some innovative ways here. So can you talk a little bit about that? How does AI improve that process? And what advantages does it offer over more traditional marketing mix modeling?
Mike True: Yeah. Specific to the role of AI within our AI is when you ingest all of that historical data that we talked about, those inputs, the AI is designed to learn that statistical relationship between your spend and your revenue, but there’s other factors that are going to be driving revenue and conversion and performance. Things like seasonality of the business, promotional periods, What is the consideration cycle of a product? If you have a $20 price point, the consideration cycle of people seeing that and then the likelihood for them to convert in just a click and convert is higher. But what if you have a product that’s a $1,000 price point where there’s a longer consideration cycle? And so AI is used to take all that historical data and really try to understand what those relationships are and being able to quantify that in a way that the marketers feel confident in that forms of measurement. MMMs, they do not like to see consistency. We love to see change. And so this allows them to go test new channels, right? So if you start playing around with your different budgets across your different channels, it’s going to start to learn what is that sweet spot by identifying diminishing returns or saturation plots. And what I mean by that is if you spend $2 and you make $10 on this campaign, you can’t expect to spend $2 million and make $10 million. Eventually, it’s just going to be some sort of for some form of saturation. And so, you know, for us is we’ve really approached the AI side where traditional saturation plots to find those sweet spots, we’re using linear regression models. Those linear regression models, they assume that the shape of saturation is the same for every channel. It’s the data is the same, right? You have your spending impressions and revenue. It’s plotted the same on an XY axis. But we’ve created some technology and AI where we’re trying to find the shape of the data, which sometimes these saturation plots looks like camelbacks, but it allows the AI to get very specific and very precise on that direct point of saturation across all of your campaigns. And as you start to make these changes, it learns and it only gets smarter and smarter. So it understands that really that sweet spot of where you should be spending.
Greg Kihlstrom: Yeah, yeah. And so, I mean, that really kind of highlights the next topic I wanted to talk about, which is just, you know, the power of predictive analytics in general. So, you know, certainly traditional modeling is looking backwards and building on that. But I think there’s a ton of power in prediction and predictive capabilities. Can you talk a little bit more about that? I know you just kind of touched on it, but can you talk a little bit more about how should businesses be looking to predictive analytics to not only maximize revenue, but also looking at things like profitability?
Mike True: Yeah. When you think about implementing predictive analytics, specifically with an MMM, it’s going to tell you, based off of your current spend, what do we predict that’s going to happen over the next 30 days? And then you start layering in optimization models on top of that, based off of those saturation plots and saying, hey, If you implement these changes, we predict with a certain level of confidence that the incremental growth compared to your existing strategy will be X. When I think of predictive analytics, I also think of prescriptive analytics. You can leverage AI to say, hey, we’re going to prescribe you to increase your top of funnel spend during this time period of seasonality because you need to start filling your bottom of funnel during this Black Friday, Cyber Monday season, and this is when you should start to implement these changes. And so there’s a prescriptive nature based off of the predictive outcome, which should be some sort of value add or incremental growth on profitability compared to your existing strategy. So there’s a nice blend in between taking actions based off of the confidences and predictive outcomes. In my opinion, I think the world is going to move towards a, you know, more of a confidence-based automation. So, hey, we predict this is what’s going to happen if you make these changes and just kind of let the machines take care of the activation on that side.
Greg Kihlstrom: Yeah, yeah, definitely agree there. I think Things are definitely moving more. I mean, there’s too much to do and not enough resources to do it, right? So to be able to automate to that degree seems to be the right way to move. So you gave one example of being able to use prediction to enhance a marketing strategy, but I wonder if you could give maybe another example or two of what are some of the insights that predictive analytics can reveal about a company’s marketing strategies?
Mike True: Yeah, I think a lot of it comes down to a lot of these businesses are heavily seasonal, right? So you can predict what’s going to happen based off of the spend, right? But there’s other variables that are going into that are alongside just how you’re deploying your marketing budgets, right? And so when you start thinking about, hey, how can I predict the impact of the seasonality of of my business and how that correlates into our marketing spend. On the retail side, are they leveraging things like in-store foot traffic or in-store promotions alongside your paid spend? It gives a much more broader view of being able to have the finance team and the marketing team really align all things that are tied to a marketing budget outside of just how you’re traditionally making your spends. So it can go very granular into things like promotional codes for a specific retailer by a DMA. And so I think using predictive analytics to tell a higher level story for media planning and optimizations, but getting more granular in real time and leveraging data points and inputs that are just not tied to your spend that can impact the business.
Greg Kihlstrom: Yeah, yeah. So, you know, looking ahead in the coming months, you know, how do you see AI and predictive analytics continuing to shape the future of how businesses shape their marketing strategy, their ad spends, you know, what should we be keeping an eye out for?
Mike True: Yeah, I think there’s I go back to this triangulation a lot. We have multiple forms of measurement that are being combined into one model and being able to understand the impacts and what those forms of measurement are trying to tell you and really coming out with very prescriptive recommendations that can be applied across any marketing channel, across any industry. Where I do think the future is really going to be held in is generative AI with creating creatives, making creatives. There’s a buddy of mine who just started a company and you take one picture, it turns it into an influencer into a hundred different videos that can be applied there. And so the combination of using measurement plus creative, I think the AI is going to tell a powerful story. And then tying it back to the automotive side of things is you know, could you reallocate resources to focus on more tactical things, creative strategies versus having to do, you know, having a human execute campaigns where, you know, something we’re really focusing on is, you know, that can you automate portions of media buying based off of confidence scores and predicted outcomes?
Greg Kihlstrom: Yeah, I love it. Well, thanks again for joining today. I’ve got one last question for you. I’d like to ask everybody on the show, what do you do to stay agile in your role and how do you find a way to do it consistently?
Mike True: you know, on the personal side, I’m a daily runner. It’s where I go. I live down in Miami. I run around Brickell Key and just really process what’s going on in the day, what we’re going to be doing tomorrow. I love to read books and I love to jam out with my co-founder on different ideas and just really, you know, level set and making sure that, you know, we’ve got the right culture and, you know, just a lot of, you know, a lot of time to focus on the health, but also making sure that, you know, the business is running in the right direction with my co-founder.
