Customerland
Customerland is a podcast about …. Customers. How to get more of them. How to keep them. What makes them tick. We talk to the experts, the technologies and occasionally, actual people – you know, customers – to find out what they’re all about.So if you’re a CX pro, a loyalty marketer, a brand owner, an agency planner … if you’re a CRM & personalization geek, if you’re a customer service / CSAT / NPS nerd – you finally have a home.
Customerland
Predictive Consumer Intelligence: How Resonate Delivers 300% ROI
Technology adoption has historically taken longer than expected, but AI is breaking that pattern, creating business impact faster than most people realize. This fascinating conversation with Brian Gernert, CEO of Resonate, explores how predictive consumer intelligence is transforming marketing and customer experience in ways that were impossible just a few years ago.
At the core of this revolution is Resonate's ability to process 30 billion daily consumer interactions, creating detailed profiles with 15,000 attributes per individual. What makes their approach revolutionary isn't just the volume of data, but their focus on understanding why people make decisions, not just what they do. "If we can understand what motivates them, their values, their beliefs—that's really the way to connect with individuals," explains Gernert. This deep understanding allows businesses to personalize experiences at unprecedented scale, delivering messages that resonate with individual motivations rather than broad demographic categories.
The results speak for themselves. Resonate clients average 300% ROI in their first year, with some reporting 100% lift in in-store purchasing performance. Their approach to predicting customer churn demonstrates how AI is changing the game—delivering superior predictive models in 72 hours versus the months required by traditional methods. But Gernert's most valuable insight may be about how businesses should think about AI adoption. While most companies focus on efficiency and cost-cutting, the real winners will be those who leverage AI to drive revenue growth and increase market share. "Rational companies do things for three reasons: increase revenue (10x priority), decrease costs (3x priority), and reduce risk (1x priority)," he notes. Companies that embrace AI tools across their workforce aren't just becoming more efficient—they're positioning themselves to dominate their markets while competitors merely try to survive through cost-cutting. Ready to transform how you understand and connect with your customers? This conversation shows what's possible when predictive intelligence meets customer experience.
You know, I've been in technology for like 30 years and I had this conversation the other day with somebody actually on the team and they said you know, it always takes longer than what people really think about what's going to happen with technology and the impact that technology is going to have.
Speaker 2:And.
Speaker 1:I've been around long enough that I was in the early internet days, right, and there were conversations in the early internet days that it's a fad, the internet is going to go away, at&t is going to come up with something better. I mean all these ridiculous conversations literally a conversation with Nike how they'd never sell a pair of shoes on the internet. That would never happen. But all those things actually take longer than what people sometimes think or they think it's a fad. And one of the things that I really believe strongly in having a conversation with this individual internally was that what's changing now through AI and AI is a pretty broad-based conversation. That impact's going to happen much faster than what most people think. In fact it's happening. It's just not necessarily understood quite yet, but the changes in just a year in the conversations that I see or that I'm having or what people are talking about at conferences is dramatically different than a year ago.
Speaker 2:Today on Customer Land. Brian Gernert, who is CEO at Resonate, and Brian's team brought the Resonate kind of solution set to my attention recently and it was wondering if I would have any interest in bringing this to our audience. And it was an immediate yes, because of not only what Resonate does but, I think, more importantly at this moment in time, how you're doing it and maybe even more importantly than that, who you're doing it for. So, with that as a terrible introduction, brian, thanks for joining me. I really appreciate this.
Speaker 1:Great to be here. Mike, thanks for the time, really appreciate it appreciate this Great to be here, mike.
Speaker 2:Thanks for the time, really appreciate it. Yeah, so I think we're going to be doing a lot of editing to this conversation, because we're already doing some serious hopscotching, which just happens sometimes. But would you tell us, just to set context, what Resonate is about what you do, and then maybe we can just unpack that a little bit, because it's a really fascinating approach to that particular world.
Speaker 1:Yeah, so Resonate is really one of the leaders in predictive consumer intelligence, and I'll unpack what that means in a second.
Speaker 1:But what we do is deliver solutions to marketers to help them better understand their customer, their prospects but not just understand, but take direct action to enhance their customer experience, to retain customers longer, to cross-sell, up-sell, as well as identify who is their next best customer, and all that's in service of delivering lifetime value from their customer base.
Speaker 1:And we do that a little bit differently than I think traditionally has been done from the standpoint of data companies or other companies in the space, and that what we've built is an AI-driven predictive engine that allows us to basically predict the future, based on individual consumers, based on individual consumers, and so those solutions have driven our customers to see great returns on their marketing spends, on the retention efforts, and we continue to roll out new solutions using this predictive engine to predict things like your next customer is going to churn and why they're going to churn, and, using our AI infrastructure, actually change the identification to churn, and using our AI infrastructure, actually change the identification of churn based on issues that they may not see within their own walls.
Speaker 1:So, for instance, right now I think today, as we woke up this morning, we realized that we, as a country of 180 different countries who put tariffs on yesterday, that's going to impact consumers. That's going to impact consumers and potentially churn for companies. Because we have a predictive modeling engine, we can, as they see that churn. We can rerun the models without human interaction to actually identify who else may churn, based on economic impacts to their customer base. So really going beyond how people have thought about doing these types of things, that's the business we're in.
Speaker 2:There's always been in this space talk of nailing, personalization. We finally got it down. You know this is, but people have been saying those kinds of words for I don't know 15 years or something like that. You know, even though it's looked different the whole time individuals, their actions, their tendencies, even their psychologies seems to be a pretty new way of approaching it. Do I have that even close to right?
Speaker 1:I think you have it very close to right. It is literally taking massive volumes of data. So we collect about 30 billion directly observed interactions of consumers on a daily basis and then we tie that for our customer into their first-party data as well and then we can make predictions based on any ground truth that we bring. And when we started the company, the thing that we really started and focused on was understanding why people make decisions, which goes well beyond what marketers have had access to historically. Demographics any scaled data sets were really difficult to get, so we as marketers had to rely on pretty coarse demographics and other things. But the premise of the business is if we can understand why people make decisions, what motivates them, what's their values, what's their beliefs. That's really the way to connect with an individual on why they're going to make this decision, and so that's how we started the business to determine those things. But it is really about mass customization and, quite candidly, when we started the business, many of the tools that were required to do that didn't exist. The ability to understand people we were creating, but the actual engines to actually deliver that result didn't exist. A lot of that's been built over time as we get closer and closer to things like dynamic creative that is really good and scaled, using again the generative AI solutions, and things like dynamic creative that is really good and scaled, using again the generative AI solutions and things like that. Not only can you customize your interaction, but you can customize what people are seeing in that interaction, and so we are very much in the space of designing a personalized world. Is how we've communicated.
Speaker 1:I think, as we continue to get more sophisticated, the interactions that we have are really designed for us as individuals, and you know, I had this conversation with my father which I told him. You know, you don't see the same YouTube that I see. He's like what do you mean? I said, well, pull your YouTube up, I'll pull mine up. And that was like a moment for him, like wait. You mean I said, well, pull your YouTube up, I'll pull mine up. And that was like a moment for him, like wait. You mean this isn't the same for everybody and that's an oversimplistic example. Much different is interacting with a business, for instance, about buying a certain product, but it's a similar concept. What you and I get, or what you and I see, are different based on what we value, what's important, where we are in our buying cycle.
Speaker 2:All those things really matter to deliver better results. I'm dying to insert myself into and seeing how your world actually operates, just because it's fascinating and also because it's directly impactful to basically everybody who listens to this podcast. To just see how the data gets ingested, where it comes from. What does homogenization look like, how is it dealt with in your systems, what your predictive engines are predicting and how those are built. This is way deep in the weeds that, frankly, nobody cares about me, but you know, the outcomes of those are, um are where the rubber hits the road, and, just to be really candid and maybe too candid, but I speak to a lot of technology providers and I'm an encourager of most of them. I think everybody's got a real passion for what they're doing, and a lot of them are doing genuinely innovative things, things almost everybody phrases their, their unique service or product offering in similar words that you described and I say it with an awful lot of respect Um, but, but the differentiator is what does that really produce on an outcome basis? What does that really mean, and who are your clients and how are they using your insights?
Speaker 2:In this case, yeah, having said that, though I did do a little poking around the site. I do notice that you're Northern Virginia based. As a fellow Virginian. That's pretty cool for me, but that probably means that you do work inside and around the Beltway. I'm just guessing that you, you know, if politics are a place where Resonate plays, boy oh boy, would I like to be a fly on the wall in some of those data sessions. I won't invite myself because that's rude.
Speaker 1:So we do. We sit outside the Beltway. Being from Virginia, you understand I'm not sure everybody will understand what that means but we are outside the Beltway. But we are outside the Beltway and that market's not our primary market but it is a market that, from a political perspective, we don't work directly with the government on any of the government agencies but we do have business that we've done to support kind of political campaigns and understanding kind of voters and those types of things. Candidly, it's a smaller piece of our business but, being in this area, invariably there are conversations about those things as well. Really, where we see the return and you know we've done some economic studies On average our customers see over a 300% return on ROI in the first year and they see performance metrics, not just on things like engagement rates and things like that, but increases in revenue. I don't know if I can disclose, but they just share with us that tying all the data back to POS in-store saw a 100% lift in performance for in-store buying based on the data, which is a great outcome. Right, 100% is kind of everyone gets 100% right. So you know, as far as tying it to actionability, you know we have a little. We have a conversation internally.
Speaker 1:Insights are interesting.
Speaker 1:Results are what people want, that's what people buy, that's what people value, and so insights are helpful to marketers and to companies. But at the end of the day, can you deliver the result that they're looking for? Because those insights are in service of something, whether it's, like I said, getting new customers, retaining customers, growing customers, and so our whole focus has been getting data into our platform to provide insights, but, most importantly, getting data out to places that drive results, and that results could again be in a CRM system, could be in a call center, could be in advertising, whether it's connected TV or it's digital advertising, or it's used for linear TV and kind of better, more efficient buying of traditional media. So our whole focus has been how do we deliver results? And the results means it has to be actionable. Results means it has to be actionable. How we do that is we tie it to customers' first-party data or we put it in the ecosystem from the standpoint of the ability to use it for online targeting and other things email marketing and those types of solutions.
Speaker 2:I'd like to talk a little bit about the technology ecosystems that you work with. That you work with, you know, as you said earlier in the conversation. You know ability to derive insights is kind of a new thing but kind of came ahead of the ability to do anything with them at scale, and it's only become more recently more universally available to enterprises that they could find technology to harness those kinds of insights and actually turn them into things. So are there tech ecosystems where Resonate is kind of more resonant, kind of plays more at home, or could you kind of connect to any CRM decisioning and kind of go from there?
Speaker 1:Yeah, I mean pretty much any CRM or decisioning engine, whether it's from a CRM, is really relatively easy as long as you. We have 15,000 attributes that we continuously update on individual consumers, 15,000 attributes that we continuously update on individual consumers. And the biggest challenge with that on the CRM side is that no one. The capacity for 15,000 attributes, first of all, is overwhelming and not needed. But that can be overwhelming in a CRM system, right, but it's really about what's most relevant and important and our platform helps identify what's most relevant and important. So, if you're a banking client, you know we have a bunch of banking data. That's really interesting. But the platform also identifies other attributes of who your consumers are and how they're different from your competitive banks. That will be relevant in your marketing efforts or in your acquisition efforts.
Speaker 1:But from a decisioning engine perspective Adobe or Optimizely if you you know personalization, I mean there's a number of different ways to kind of leverage the data, because we create what, what we deliver, is actually the data that drives the decisioning engine. So it's it's, it's a function of do you, is it a? Is an append? Is it a? Is a real time? Does? Is it a data data set that sits within your four walls virtually Well, actually realistically, four walls to drive those things. It really depends on what the client needs. It's not just software dependent, it's also there's InfoSec and privacy and all those other things that are important. So our focus is to deliver the data the way a company needs it to drive those results, and so we deliver in multiple ways to do those things.
Speaker 2:Just again a little too deep in the weeds for practicality. But could you also derive the same level of insights if you were working with, say I'm thinking of another large financial institution on their internal customers Say, they've got a couple million people you could still work with that data set and produce the same kinds of insights. So you wouldn't really necessarily be able to triangulate. Maybe you could with outside data sources to make that happen.
Speaker 1:We actually can. We use kind of privacy-friendly solutions to do that. So you know, basically hashed emails and other things that de-identify those people. That allows us to bring in those first-party data sets, sync it with our data and then basically give them the insights and understanding back to them. We can also do that with, for instance, identifying potential churn. You know, we bring it.
Speaker 1:So what we ask of a customer and I don't know how familiar you are with how churn models are built, but historically, or even today, I say historically because we do it differently. So I guess we're changing the history in a way you either have an internal team or a consultant that comes in. They take all your churn data, they, they, they match it up against other third-party data sets and then they try to identify. You know, basically they use algorithms to identify who else looks like the people that churned, and that's a two, three, four-month process. It's expensive, it takes a long time.
Speaker 1:Our solution is very different. All we ask is that you give us 10% de-identified of your file flag, half of that with people that churned and half that didn't. We upload it into our predictive modeling solution and in 72 hours we deliver back to you the prediction on the 5% you didn't tell us. And if we outperform your churn model, you should be a customer and if we don't, you shouldn't be a customer. But we do that because we know we're going to have a better predictive model that identifies potential churn better.
Speaker 2:But that's very different than spending two, three months and then getting a static algorithm that doesn't change until you spend the money to redo it a year later From your perspective as CEO of Resonate, of a company who's doing really smart, innovative and useful things in the world of data and personalization, and by virtue of the fact that you out of it, what do you feel like you would you see out there on the horizon, good or bad or otherwise, through your, your work there, that the rest of the world doesn't Um, and and what do you think they should be thinking about relative to it?
Speaker 1:So it's always interesting to kind of figure out what everybody else knows and they don't know.
Speaker 2:I realize I put you on the spot.
Speaker 1:Sorry. Well, my goal is to figure out to your point, like what do people know, what do they need to know and what's missing that we can help them with. I'll just say this as an overarching conversation. I think the way companies have looked at the kind of rise of AI and I'll say specifically generative AI and now agentic AI is ways to become more efficient, reduce costs, speed up processes and things like that. I think what's really missing is that those conversations are valuable but expense isn't nearly as interesting or valuable as driving additional revenue, and I think that the early kind of things that I'm seeing are more identifying and focused on reduction of expense or automation of actions, and I think there's a whole level of opportunity around growing revenue and increasing market share and things like that. That I really don't think is the focus yet in this.
Speaker 1:If I you know, one of the things I mentioned, I think, is that insights are interesting, but people pay for results and people want results, and those results that companies really drive kind of the most importance to is increasing revenue and market share, and so how do you not become more?
Speaker 1:It's not about being more efficient, it's about growing your business and I think the companies that think that way, that embrace AI in a way that's focused on those areas, will be the winners. And I think the ones that are looking for cost efficiencies are going to be trying to survive in a lot of those cases because they're going to miss the opportunity that there is to really grow through these capability sets. And I think most people are focused there today through these capability sets, and I think most people are focused there today and I think a lot of companies still are kind of we don't know what to do with it. And you know my conversation internally to the company is that you know those that embrace AI and look for revenue are going to be the winners and the others are going to try to survive through cost cutting and that's how they're going to use AI. And you want to be in the forward category, not the laggard category, in that conversation.
Speaker 2:Yeah, it's interesting Just again because of what we do here, talking to a lot of technology providers that fact. It's remarkable how often that kind of gets lost in conversation and we do some coaching to some of these people on look, if your go-to-market plan isn't quite working the way you thought it would, what is it you're focusing on that you shouldn't. How can we fine-tune that for you? And what's really interesting, what surfaces a lot of the times is they're selling efficiency and yep, has value. Clearly it does, but the sale goes so much smoother and quicker and the cycle is so much shorter. If you're selling revenue and some of these technologies, these are run by really smart people, people who are way smarter than I am. I could never think in those terms, but somehow it kind of missed that critical extra step like yeah, here's efficiency on its way to revenue and tie it there somehow, and sales seem to happen much easier. I mean, if you're a CFO, you're looking at revenue. You know efficiencies are a secondary kind of phenomenon for you.
Speaker 1:You're looking at cost if you have a revenue problem, right right. So you know my mental frame is rational companies and companies should be rational. Just irrational people sometimes run companies, but rational companies do things for three reasons only Increase revenue, decrease costs and reduce risk. And they're not equal. Revenue is a 10x, cost is a 3x, expense is a 3x and risk is a 1x. It's like buying insurance.
Speaker 1:No one really wants to do it, but we all do it because we think we need to in case something happens. One really wants to do it, but we all do it because we think we need to in case something happens, but as a company, that is how companies, I believe, prioritize decisions. So to your point, yeah, talk about the revenue. You're going to help them increase and it's a much easier sale. It's a much easier conversation. If you talk about risk, yeah, they're worried about risk, but they don't want to spend a lot of money on risk. And I say that because the last company I built before this was a help bill, was a cybersecurity company and that was all about risk and it's a lot more fun to be on the revenue side, I can imagine.
Speaker 2:I mean, yes, it's, you can sell, you can sell risk. I mean, there's entire industries built on that, but but it's so much easier selling revenue so much easier, which equates to fun.
Speaker 1:Beyond what I talked about.
Speaker 1:I do think and this is going to sound like efficiency, but I think investing heavily in making your team really smart about using AI tools is something that's undervalued in companies, and I think sometimes that's because they're afraid of losing control, and sometimes it's going to take jobs and I go back to what I said before it's going to take jobs if you don't win, but if you win, it's going to just help you do additional things, and I still see many, many times a lack of adoption in kind of leveraging the tools that are already out there, and we have a program internally that we emphasize across the company for every department how do you use different AI solutions for your day-to-day job?
Speaker 1:And if we can become 30% more efficient now I'm talking about efficiency right, I'm not talking about revenue but even for the existing employees we have, how much more could we get done in a day? How much better could we be as a company? And that's a real conversation internally, and so one of the things that and so you know, one of the things that we do is, every two weeks we have a company update and we've introduced now how we're internally leveraging AI as part of that update, so we'll highlight something that an employee did or someone in management did, or how they're using AI to kind of enhance and kind of deliver a solution. So I think, so I think there is just the embracing of it. I think that continues to be a challenge in a lot of places.
Speaker 2:First of all, there's just a lot to talk about about the ways different companies can be using your service, and I think you know some specific use cases that you may have or your team may have. Be really interesting, because this is, this is dialed in to exactly what this audience needs to hear about. First of all, it's interesting, but like this is this, is it? If you, if you learn this, if you know this, you can be a lot more successful than otherwise.
