[00:00:00] Phillip: And welcome to Future Commerce, the podcast at the intersection of culture and commerce. I’m Phillip.
[00:00:04] Alicia: And I’m Alicia.
[00:00:05] Phillip: And today we are going to get into one of my favorite topics of conversation, which is research, insight, foresight, and how… really, how we do that in the age of AI. And I think that one of the challenges around businesses trying to get better insight and foresight is that a lot of those teams have been hollowed out. They used to have that insight in house. Right, Alicia? Like, you had teams that focused just on that. And post pandemic, the biggest brands in the world, the brands who supposedly set the tone for culture in the world, they don’t have those teams in house anymore, and they look to agencies to look ahead for them. But in the age of AI, I find that most people are turning to tools and specifically LLMs to try to get that job done for them. And I’ve heard a lot of conversation, lot of people talking, lot of ballyhoo, if you will, about vertical SaaS, especially in the last year, about specific purpose built tools that accomplish specific types of jobs and those that are native to the industry. I know we talked a lot about this, Alicia, back during Manifest around, you know, SaaS…
[00:01:20] Alicia: Vertical… supply chain.
[00:01:22] Phillip: Yeah. Right, supply chain industry. So it was really interesting to me when we met our newest partner, who has a real hold on how you accomplish insight, research, competitive intelligence, but not only looking out into the world and figuring out what is happening and keeping your ear to the ground and sort of this internet fire hose sense, but also tapping into the data and the insight you might have in your own first party data and second party data. And I… just think that that’s such an important part of the puzzle and part of the equation, but it’s even more important when that piece of software accomplishes something that’s native to commerce. And so, I’m really excited to get into that. And we just used this particular tool for our own means and our own research, Alicia, and I can’t wait to talk about that today too.
[00:02:17] Alicia: I know. And still using the tool, mind you. Oh yeah, we’re learning, thanks to Merciv, so much in the process about what is truly possible with tools like these, because we have our own hypotheses, our own ideas of where certain trends are progressing towards and we have our own algorithm that’s feeding us information around these trends. But there is a whole universe out there that is completely untapped that is really being uncovered for us and it’s been incredible.
[00:02:48] Phillip: Yeah. And so today we will be talking about how we’re connecting those signals, if you will, into intelligence and no better person to talk about it than the builder of this platform. He is Merciv’s co-founder and chief executive officer, and he’s helping lead the company’s charge in agentic systems and something that he describes as the nervous system of global commerce, which I want to get more into. And that’s where research happens continuously and seamlessly to describe these consumer behavior shifts in real time. I would like to welcome our newest partner, Merciv’s co-founder, Shaia Erlbaum. Welcome to the show, Shaia.
[00:03:28] Shaia: Thank you, Phillip and Alicia. It is great to be on. Appreciate you bringing me on here.
[00:03:33] Phillip: Just real quick, because I think people hear a lot about new platforms, what do they do? How does a platform like Merciv actually help people in the commerce space solve real world problems every day?
[00:03:49] Shaia: Yeah, great question. And rather than boiling the ocean, I guess I’ll try to focus that in on a few kind of concrete areas. You know, we started a lot of our efforts in the domain of knowledge management. How can we actually bring a lot of the existing knowledge and the expertise that brands have already built on decades and decades of research into one fold rather than, you know, a dozen different searches and kind of help desk kind of, you know, fragmented research in different directions. And from there, a lot, you know, as you mentioned, the effort looking at LLMs and how we can start integrating more advanced technologies on top of our research stacks. A lot of that effort then turned into, well, if you can expose all of that knowledge to the right agent and the right tool, how can you actually get to quicker answers and better answers with the resources that are available? And then take that a step further. I know this is some of the area that you were poking around in with the reporting you’re doing and research you’re doing. How can we actually expose new data sets to the existing knowledge we have? What might come from that kind of correlation, those new relationships and new insights?
[00:04:56] Phillip: And the kind of data that retailers or branded manufacturers might have is everything from like inventory, right, to competitive intel, or the kinds of insights that you might be asking plain language questions, right? Like, what do I do this coming shopping season? That’s something that we’ll be working on, by the way, in the next few weeks as we work together on a new piece of research. What are those types of things that your customers are doing with Merciv today?
[00:05:31] Shaia: Yeah, great question. I think oftentimes we’ll see teams start with kind of the question you just described, like, you know, as simple as what do we know about x? That’s the kind of question that would have taken weeks and weeks in historical paradigms. You’re looking to an insights team to find the right information. You might have to go out to vendors just to scrape through the corpus you’ve built. And that’s often where our partners will start within the platform. You know, once you’ve got all of your data connected, how can we get to a quick answer as to the entire knowledge base we might have on a certain topic? From there, a lot of it turns to automation. How can we actually speed up the process to anything from our monthly business review to a new research pipeline, a new typing tool for a segmentation study or to a new category review that we might be running across the syndicated sets we’ve acquired. And I think that’s often where teams see a lot of that early value. From there, it starts splintering out in a lot of new directions. You know, a lot of the market starting to talk and think about synthetic twins and digital twins for consumers and how you can fuel those with the data you might have your hands on. You know, there are all forms of new correlations that might surface as well as you start looking at the historical corpus paired with that real time lens. What does a consumer do and say on Instagram or TikTok that might agree or disagree with the research you’ve already conducted? And what new insight might inform strategy across the board?
[00:06:59] Phillip: This is the question, right? Because there’s a lot of social listening tools that exist, right? You’ve got a lot of paid media where your customers are speaking back to you, probably in comments that nobody’s actually paying any attention to whatsoever. You’ve got this influx of CX data that you’re probably sitting on mountains of that you’re doing nothing with at any given time. And then you have internal knowledge management that you’re probably only leveraging in so much as your star employees putting that to work in their own specific roles, but not sharing it in amongst, you know, the rest of the business. And I think that what we’re trying to do is you have to, like, try to weave it all together. When we’re sitting on mountains of all of this type of consumer data, you… it doesn’t… It’s no wonder why you’ve got brands struggling to actually understand what consumers want now or what they want next. I think when I’m asking the question, what are they doing? Are there specific case study type things that you’ve seen so far with Merciv that are real world problems that they’ve solved? So far what I’ve seen with Merciv is you ask a simple question and the next thing I know, I’m seeing, you know, after a great deal of research, a tremendous amount of reporting, tremendous amount of dashboards being built, tremendous amount of updates that can turn into a weekly, daily, monthly workflow for me. How does that actually translate into business value concretely for some of your key case studies?
[00:08:40] Shaia: Yeah, absolutely. I think the beauty here is that with these new emerging technologies, there really is such a broad landscape of new opportunity. And I think when you look, you know, kind of zoom in on individual use cases and kind of the outcomes therein, a lot of them take different shape. You know, we have some brands in for example, the QSR space that are looking at menu designs and looking at, you know, new formulations of flavors and a lot of that research historically was a common kind of concept testing pipeline. You come up with a new idea, you maybe go test that with dozens or hundreds or thousands of consumers and you get a bunch of quantitative data, maybe a little bit of qual and that informs decisions. One of our customers has run 3,000 different menu formulations over the last decade or so. One thing we found was they’d never actually been able to correlate the outcomes of those concept tests with their actual sales data. And that was a really interesting one. You know, you have these validated research insights and you know, at least in a survey what a consumer is telling you. And yet they hadn’t kind of pulled everything together in order to correlate that with what are the real outcomes in market. And from there, that’s actually a fairly simple solution when it comes to agentic analysis. You can look at really large volumes and correlate the outcomes pretty seamlessly. But what that really informs is now how can we look forward at the next concept we might test? How can we actually predict what consumers might be looking for without having to go and conduct as many different alternative research studies in order to reach that same conclusion? And then briefly, you know, in other senses, you’ve got huge corpus of data sitting online, you know, across socials and review platforms.
[00:10:32] Shaia: I’ll give one other brief example in the apparel space where we’ll see brands really zoom in on the survey data they’ve got, the market research they might acquire, and they’ll look at reviews. I think reviews are very meaningful. But what’s often missed when you’re kind of looking at a 40,000 foot view is the niche detail that consumers might mention, not necessarily on reviews for your products, but maybe on a competitor’s. And one example there was a leggings brand that identified a huge gap in their own product lineup where every single… not every single, exaggerating a little bit, but a huge volume of reviews left on competitor leggings were praising this little zipper pocket that existed in their legging brand. And this… the brand in question on our end actually didn’t use that feature. They didn’t have that feature in their lineup. And that was a really simple example of something that was costing them part of their market share. And, you know, it was a fairly simple solution. Actually, since then, they’ve launched another variant of one of their key legging lines that includes a zipper. And that was a fun little success story and that it’s helping them win back market that they hadn’t necessarily recognized was lost to that specific feature in question.
[00:11:51] Alicia: I love that example because it shows what an impact even a small detail can have in decision making, whether it’s product like in that last example, or whether it’s marketing or creative. And what I have found to be equally interesting, but also frustrating about doing research over the course of my years in this industry is that there is always, and you kind of refer to this, this say-do gap, right? Consumers may say one thing, especially through social media, in comment sections or reviews, but ultimately they may do something completely different and it may feel almost contradictory to the things that they’re saying a brand should be doing or things they say a brand is missing. Right? So where do you see those contradictions kind of come up?
[00:12:44] Shaia: Yeah, there are so many examples. And I think, you know, a lot of the industry is at this point really well aware of this say-do gap and just the ramifications of it in their strategy. I think I heard an interesting example recently if you look at, for example, a Colgate study. Almost everyone answering a survey question about how many times they brush their teeth in a day will say twice.
[00:13:08] Alicia: Liars.
[00:13:08] Shaia: If you actually look at those sales figures, they simply don’t sell enough toothpaste. I think that goes to your point, Alicia, about the correlations, you know, they’re really… if you don’t look at those two data sets in unison, if you aren’t able to actually correlate them and understand where that translates from a dollar figure perspective, you’ll miss the mark. You know, you’ll forecast an inventory line that actually demands that kind of toothpaste volume and then it’ll just sit on shelves. And I think we actually see that all over the place, you know, I mean, that’s one kind of funny example. But I think underlying a lot of that distinction is consumers where they feel pressure to answer something in a survey may not come at it with full honesty. And that’s… that’s fair. And that’s kind of taken as a given in some of the research world. But there is another source where they’re brutally honest. That often happens in forums online or in, you know, Instagram comments or in a TikTok video. And if those signals can’t be synthesized at the same time, I think a lot of the do just ends up missing from the overall strategy.
[00:14:21] Alicia: Yeah. A really interesting example that we’ve had to kind of contend with is the Signal Report that we’re working on right this moment on celebrity brands. And what I found really interesting is that say-do gap almost appears to be derived from consumers like posturing around celebrity brands, right? They may say like, oh, like they’re only getting these deals because they’re famous and oh, they don’t know anything about, you know, the wellness industry or the beauty industry. But we found like we couldn’t just put that data upfront and let people take that at face value because when we dug a little bit deeper into the revenue, the new brand entries, the time to market cycles, people are buying these things, right? So we had to really unpack that nuance and understand that there was a specific tipping point from where, you know, people were possibly skeptical or pushing against the idea of a celebrity brand or feeling like there’s simply too many to that trigger point where they were actually more willing to try it even though they felt a certain way about it. So again, it’s just another case where sometimes we can only take what people say at face value, especially if they’re participating in a survey that’s quote unquote geared towards understanding true sentiment of celebrity brands. They may feel like they have to go to the table and you know, posture or present this facade of what they’re feeling that maybe they don’t actually feel that way ultimately.
[00:15:58] Shaia: Yeah. Absolutely. That happens to be a great example. You know, we’ve seen a lot of the market even like shift its perception when it comes to celebrity brands as a whole. And it’s funny also when you see surveys that might actually disagree with each other. You know, I’ve seen some point that consumers actually claiming they don’t care whether, you know, a celebrity endorses a brand or not and others where they say that might be one of their top purchase decisions in certain, you know, domains or certain regards. And I think really in my mind what that goes back to is the value in unifying brand knowledge. And not just saying, okay, I know there’s this one research file and we might dig into it or this one survey that informs a piece of the picture, but actually bringing everything together and being able to search across all of it. You know, I think that’s the value that insights teams have played for a long time. But to your point earlier in the conversation, Phillip, a lot of the utilization of insights as a function is starting to shift. You know, teams are actually getting a little smaller, but they’re the C suite, the board within a lot of these companies are starting to depend on them that much more. And I think, you know, the way they can navigate whether it’s say-do or any of the other challenges they’re running into is really leaning into any opportunity to bring all of their existing knowledge, the expertise they’ve built over the years and years of research into that same fold to inform the questions that matter most.
[00:17:28] Phillip: I keep thinking, Alicia, we heard this, it was this conversation that I was having with someone else, but it was repeated on Twitter in, I think, a very similar fashion. They said that the CMO is… steadily, you trace its progression and take it to its logical end, the office of the CMO is becoming more of an individual contributor role. That’s just where things are heading, especially in the age of autonomy, we have more things that are more automated, we have more tools that accomplish more, dashboards tell you where to put budgets, more things are being taken out of house, and I think that if I’m somebody who’s sitting in the marketing or the brand seat, I’m thinking to myself, how do I start to take more control and get more insight and more foresight for myself? That’s where I feel like there is a conversation, you know, it’s like, much of this, we’ve been drowning in dashboards forever, but how much of this is actually helping inform right decision making within the business rather than giving us more insight into more data? So I think that that’s where I’m asking, because a lot of people are using AI tools right now.
[00:18:48] Phillip: Companies have lots of AI budget and they’re using lots of AI powered tools, and they think they’re probably using intelligence tools. They’re probably just chatting in LLMs right now. Does the competitive advantage, Shaia, come from asking better questions and being more adept at working with LLMs in general and being more informed on how to coax more out of the tool necessarily, like being better with the platforms and becoming more native to the platforms? Or is that part of the equation? We can ask better questions for sure. Or is it acting faster and becoming more reactive to cultural shifts and cultural currents? Because if I’m a CMO, I’m thinking I’ve probably got to walk and chew gum. I’ve got to do both. But I’m starting to feel more and more like it’s my job to do both and not necessarily a team’s job to act on both. I’m just curious what you’re seeing and how, you know, folks are using your platform to maybe accomplish some of these challenges. Right?
[00:19:58] Shaia: Yeah. Absolutely. And I think each point you mentioned actually plays a role there. You know, it’s the speed, it’s the amount of data you might have your hands on, it’s how quickly you can get to that insight. And I think a lot of the market is thinking about that metric, like what is time to insight and how can we improve that across the board. I think there’s something else here though, because at the end of the day, this may not be the best framing for it, but there’s a world where data is already becoming a commodity. You know, I think everyone has access to the same syndicated data sets. Every, you know, advanced frontier LLM is getting hyper intelligent. And I think a lot of the real kind of advantage, as you mentioned, is less so rooted in which model is more intelligent, but maybe more so like, how much to what degree can we actually verify the information that we’re coming across? You know, any great LLM can take a clean data file and give you a really confident, really good answer. But what happens when it doesn’t have that full answer? It’ll probably just tell you confidently something, whether it’s a hallucination or something rooted in the data set it had. But if it doesn’t have context across the board, these models aren’t built to tell you, hey, I don’t know. And I think being able to really get to that level of granularity, understand, of course, with the speed that the market’s moving, how to do so quickly. But taking that a step further and having it flagged, you know, we may not have research on this topic. And maybe that’s an opportunity to go commission something new or to run research yourself. Another alternative there might be pointing a lot of that lens at consumer behavior that’s publicly available. You know, the data is… The question is more so, how can we kind of parse through it more quickly? And how can we validate that the findings we identify are real, true, and actually should impact the strategies we deploy?
[00:21:59] Phillip: Yeah. I’m curious too, like, you’re thinking about the, like, which strategies to deploy. There’s so many things too. How do you use Merciv within your business? You dog food it?
[00:22:17] Shaia: Oh, absolutely.
[00:22:18] Phillip: Yeah, I’m curious, I’m sure you’re doing market research and intelligence all the time. How does that work for you? I would like to then maybe talk a little bit about how we utilized it for our first Signal Report together, because I think that that kind of puts a little bit more skin on this, you know?
[00:22:36] Shaia: Absolutely. Yeah, we like that you mentioned dog food. We actually have a Slack channel we call dog food.
[00:22:43] Alicia: I love that.
[00:22:44] Shaia: We use the platform for everything. I mean, by the way, it’s not just market research. We use it for our own workflows internally. We just had a really large RFP we were working through. Every single asset that informed that proposal was stored in Merciv knowledge base. We were able to run pricing through the platform.
[00:23:02] Shaia: Were able to run our actual proposal preparation as a final, you know, predefined PowerPoint that we actually shared through as well. And I think, you know, that stuff is the basics because it just helped us get there quicker. But I think where the real value at least shows itself for us is having the tools to on the fly, pull in any amount of data that we might need to get our hands on. Just yesterday actually, and this was for a customer, but it was just like a fun little internal project. We ended up pulling about 6 million reviews, a historical corpus, I guess call it, of all reviews in a certain category for a large electronics manufacturer. And being able to do that over the course of maybe an hour or two, rather than forming a contract and, you know, buying that data from some incumbent provider. It’s just a lot of fun, frankly. Yeah. We can, in an hour, not just pull that data in, but now understand deeply and, you know, whether it’s a 2D or 3D visualization of a knowledge graph and how these different pieces connect with each other or a report that we might just be generating for our own knowledge. I think those often are the areas where we just get to play around and have a lot of fun. And of course that ends up impacting our customer. The way we learn is both through our own experimentation, but also through observing the ways unique customers utilize our platform. And that to me as a founder is a magical moment. You have most of the time, we have a really concrete use case. We know exactly how you’re going to use our tool and we’re going to educate you on how to do so.
[00:24:47] Shaia: And I think that’s how the SaaS industry has run for quite some time. I think the beauty in this new age of deploying agents and providing such a malleable kind of interface is getting to observe new features or new uses of our platform that I may have never even considered. And that to me has been one of the most fulfilling moments I’d say in just getting to talk to people using the tool, both in house and in customer organizations, just watching those new workflows unfold. I’d also lay in one other point there, which is not to get too deep into the weeds around specific features. But one thing we’ve spent a lot of our time on recently is a feature we call playbooks, which can essentially take weeks long, more arduous repetitive workflow and teach it to a set of agents that can carry that out dependably in the same exact manner almost deterministically over time. And that’s frankly just saving a lot of my own time. We’ve got playbooks for almost everything now and we’re rolling more and more of them out for customers. But being able to just click a button, maybe fill out a few form fields to give the right information or point the agents in the right direction. And then thirty minutes later, get an email with the output, whether that’s a new Excel workbook or something we’re working on on the pricing side or anything from documentation to new market research as we were discussing earlier. I think those are the areas where I’m just getting excited about not just the time we can save, but really like the new learning that I can do every day. Anything from wellness to celebrity reports and a lot of the internal research we’ve done as well.
[00:26:27] Phillip: And we have too. I think that’s where we were really taken aback is in the onboarding sort of wizard experience, you know, we go through this sort of classification, ask you a lot about your business, and it defines a lot of market comps and goes and finds a little bit about your competitors and who you are. It knew… I was surprised, right? It’s not something that’s built necessarily for media, although we are in the commerce industry. Merciv was able to pull a tremendous amount of information about us, what the world of media says about us, what our audience says about us. From that, when we started to build out a bunch of our own insights and research for our partnership, was building it all with our audience in mind. So it was building it all towards the end of who is our ultimate consumer of that content and research, right? And for our own information, when we’re needing to be informed so that we can inform others, so that we can make accurate predictions and accurate forecasts about where things could go in the future. It’s one thing for us to just… I think a lot of people are doing this now, you just tell me ChatGPT, right? You just tell it what to… it’s telling you what to think now. This is not that, it’s more about us synthesizing what’s happening in the world, we have to understand that, and then need to put that into a format that we believe is valuable for our readers’ time, right? And that’s what we’re exchanging for. We set out to build this first report, which was we needed to understand the state of branded search movement around GLP-1s and the coming wave of GLP-3s. And we started out with this wellness category, very broad wellness category thesis about longevity and supplements and this whole industry really just becoming much more of a larger consumer category, expanding from CPG into pharma, and then, you know, people are having peptide parties at home.
[00:28:51] Phillip: Like, how do we narrow in into which, by the way, it was like, how did we go in five years from leggings and LuLaRoe to peptide parties? I don’t know. We live in wacky times. Those are the new MLMs. But this is where we are. So how did we narrow in on what is an interesting and data rich piece of insight for us to extract? And this is where we wound up, that there is a wealth of information and a tremendous amount of competitive keyword analysis that we were able to pull out. There’s a lot of really interesting data around competition, around peptides as keywords versus branded keywords like Wegovy or Ozempic. So when we started actually digging into what Merciv was bringing back, not just in wellness broadly, but specifically around the current state of GLP-1s versus what’s coming with GLP-3s, we said, okay, that’s the piece of research, is that we can start to look at what has happened, how does that differ from what we thought was happening, and then where do we think it’s going in the next eighteen to twenty four months? And that’s what our newest Signal Report is about, and that’s what I think a Signal Report should be about, is separating signal from noise and then allowing us to project that into the future. Alicia, can you give, like actually give us a little bit more about what this current state of research is as the primary author of this piece?
[00:30:22] Alicia: Yeah, absolutely. I think you hit on our overarching goals really well. I think the other component though that is really important to call out that Merciv helped us validate, cross validate, challenge even in some instances is how the markets were progressing from a manufacturing standpoint, from a services standpoint versus what was the culture saying and doing, meaning the end user, the consumer and what were they saying, perceptions of GLP-1 usage, what terms were they using, was sentiment positive or negative, which communities were gathering and how were they talking about these different services and the like. So that was an extra layer in the exercise that I found especially valuable with mainly a retail background because we went into this process where all of the headlines that I was seeing was about how GLP-1s were impacting returns. And I’m like, okay, yeah, noted. That makes sense. And actually since this report has come out, we’ve seen an acceleration and expansion of headlines around the implications for retail. But I wanted to understand the other adjacencies. How is consumer behavior, meaning if they’re taking GLP-1s, how is that trickling down into product innovation and other categories? How is that impacting how they interpret personal style, how they decide to show up? And what we were able to unlock is that GLP-1s are activating so many new categories for growth and expansion.
[00:32:10] Alicia: Shaia, to an earlier point that you made, in menu creation for quick serve and new specialized product lines for grocery and CPG, but it’s also creating so many new extensions for services in skincare and body care, even hair care, like there are completely new lines entering the market now because of that ripple effect that we were really trying to call out. And we even have direct quotes from executives from Ulta that are really pinpointing that, you know, we’re seeing the market do X and that’s an opportunity for us. We’re really going to double down in product innovation and finding the right partners to fill our shelves to make sure that the GLP-1 consumer in this case is fully represented. And for a long time, we weren’t able to put a number on that, but Merciv has really helped us find those numbers and validate those numbers, which has been incredibly exciting.
[00:33:13] Phillip: I think there’s many times where you’re looking into a research and analysis tool where you get a number of dashboards or a giant dataset, or you have an LLM, which gives you a bunch of not useful essay style prose. And what instead we found in our usage here, it sounds like I’m glazing. Honestly, I just, like, finally found a tool that was useful for us. But what I found instead was something that allowed us to sort of just, like, drill down into the things that we thought mattered and organize that information in ways that we felt were able to separate signal from noise, which was the purpose around Signal Report. Love for people to go get that. We’re gonna have three of these at least between now and the end of the year, futurecommerce.com/signal. And that’s where you can get the first one. The GLP-1 report right now is available. We have another one on celebrity brands, as Alicia already mentioned, coming very soon. And again, thanks to Merciv for partnering with us to help enable access to these vast datasets through the partnership, through access to the platform. Anything, Shaia, I’d love to get you in on this too. I’d love to understand, you know, these are wildly different approaches to data. I think in particular, what we’re trying to demonstrate in these reports is that the content of the report, I think, is not the important piece. It’s that we can dig up insight and foresight using it. If that’s useful for our business and in these particular market verticals, it must be useful for others too. Would love for you to give me your perspective, at least maybe on this report and what you think might be coming next.
[00:35:07] Shaia: Yeah, absolutely. And I think, the market’s pretty aware at this point of the effects that GLP-1s have had as a whole. I think what’s been interesting for us to observe is even our customers in like the alcohol beverage space are really leaning into these trends and doing a lot of similar research internally. I think it’s been interesting to find the shifting taste preferences, for example. And a brand we work with in the beer and beverage space is actually exploring GLP-1 friendly beer formulations to adapt to those shifting preferences. And so I think this research is incredibly topical. You know, I think there’s also an interesting parallel just to how the rate of research is shifting over time. I think an example I was looking at recently with Botox, actually. And I believe I have the dates right. I’m pretty sure Botox was originally approved to treat like eye muscle issues in the late 80s. I think it was 1989 or something along those lines. And then years later, I think it was like early 90s, maybe ’92, ’93, someone realized that it also smooths out our wrinkles and our frown lines or whatever it might be. And that it wasn’t until early 2000s, I think maybe 2001, 2002, something in that kind of era, that that actually became something the cultural kind of recognition…
[00:36:35] Shaia: …could absorb. And that to me was a really interesting parallel because if we look at what’s happened with GLP-1s, that same kind of progression to cultural impact was not a decades long process. That was four years. The entire market is recognizing the impact of that already, whether, to your point, Alicia, whether it’s you know, a quick service kind of domain or a beer company. And I think that is, you know, a notable aspect of the way, not just markets are shifting, but the way culture is shifting. And to me, you know, not to make it all about Merciv, but I think that really does go back to this idea of being able to recognize whether through the vast dataset of search demand or how the terse you’re mentioning earlier, Phillip, between Ozempic’s quick rise and then now not even kind of standing side by side with the search term like peptides. That to me is just very indicative of how these preferences change, how the interests of consumers change, and how that should inform certain strategic processes, whether you’re a market researcher or a media company like yourselves or a beverage company selling beer to patients who may have changing preferences.
[00:37:50] Phillip: And I think that that really resonates with a subscriber to Future Commerce too, who’s not just looking to figure out how to affect the next quarter of returns. Obviously that’s an important part of the business, and we all want incremental growth. But also brands need to understand how consumers’ shifts and tastes and changes of preferences are being impacted by culture and how they are impacting culture in return. And I think you have really just accurately just kind of summed it all up for us, is that this is no longer just taking small snapshots, kind of figuring out how we do this little bit by little bit. Maybe it’s a program internally. I think this is a core competency of businesses now, and it’s required in order to stay competitive within the market. Y’all are early and you’re killing it. I feel like this is such an impressive first feat. You just came out of stealth. What’s next for Merciv?
[00:38:54] Shaia: Yeah. Yeah, absolutely. I think a lot… and I’ll try to keep this fairly brief, but a huge emphasis and kind of effort from our end is I think we, you mentioned the stealth period we stayed in for some time. And a lot of that was about really validating the technology before we came out and established our brand and took that to market. And we were very privileged to do so with some of the largest brands in the world, some really valuable agency partnerships that brought us in the door with some of these customers early on. And I think at this point, we can lead with the validation, with kind of transparent trust in the data that surfaces. I think really what comes next is the ability to start mapping those to each unique brand. And I actually appreciate what you mentioned earlier, Phillip, about your onboarding experience with the platform and recognizing how much it could understand or learn about your work and your unique expertise. And that to me really is the future of this industry. Every brand in the world has unique knowledge, has unique insight, and has unique consumers. And the same answer doesn’t apply to more than one in most cases. I think that to me is really where we go next is deepening that understanding of kind of like the unique aspects of each characteristic, whether it’s an attribute behind a persona that buys one brand or another, or the search they made in ChatGPT or an ad they saw on Instagram that led them to the checkout counter brick and mortar or to a cart checkout online.
[00:40:32] Shaia: And I think mapping those journeys from a user perspective and really understanding not just the dollar figures or the numbers that may show up in some internal report, but really how that relates to culture. Exactly what we’re doing with the GLP-1 report and with the celebrity brands report. I think those are the areas we hope to really continue to flesh out and not just provide to the market as a whole, but really lean in. We’re establishing a new function we’re trying to dub an insights engineer. And it’s kind of like taking the concept of a forward deployed engineer you may have seen from a Palantir or some of these other larger kind of tech forward organizations and saying, we recognize that research and insights as a function is strapped and, you know, it has limited resources to play with. But if we can deploy not just the technology, but a person who understands that technology and understands your unique landscape, how can we help you and how can you help us innovate within the space that we’re hoping to collaborate within? Really, that’s the approach we want to take to, you know, rather than calling it customer relationships, we like to frame them as partnerships. And I think that really is the approach you wanna take to every partnership moving forward.
[00:41:47] Phillip: Well, if you wanna go check out Merciv, hey, I highly recommend it only just because I’ve used it, not just because we’ve partnered the last month or two. And I can’t recommend it highly enough. Merciv.com, merciv.com. Shaia, it’s been such a pleasure to have you. Thank you for partnering with us, specifically because, I’ll just be honest with you, insights, research, studies are probably one of the most, you know, difficult things, but the most valuable things that we do around here. And having a partner who understands how valuable it is is extremely important to us. And our audience, they get so much out of it. And so this is such an important time right now too as we go into the end of the year and people start thinking about what are the shifts that are going to drive the changes that they need to be programming towards for next year. So such a perfect time for us to be partnered in this. So merciv.com, go check it out. And thank you, Shaia, for coming on the show.
[00:42:49] Shaia: Absolutely. Thank you both, Phillip and Alicia. This was a whole lot of fun, and it really has meant the world to just really get the insight you’ve been bringing to the table as well. I think there is no better way to push the platform than to put it through real research, real world kind of outcomes that we’re all looking to achieve together. And it’s been an amazing process so far. Really looking forward to getting the celebrity brands report out, following that up with a few more.
[00:43:13] Phillip: Thank you. Thanks. And thank you all for watching this episode of Future Commerce. If it sparked something for you, we’d love for you to like, follow, subscribe wherever you get your podcasts. It helps more people join the conversation. We have Future Commerce physically in the real world through our print shop, futurecommerce.com. That’s our shop. You can get books like this. It’s called LORE. It’s our newest. It’s about 300 pages, and it’s asking you the question, is the story of your brand one that’s already written or one that your customers are writing for you? It’s… this is big, heavy stuff. This is what’s going to help you shape your future for next year. Go get this right now. It’s worth the investment, especially as you get planning for next year, and that’s because you need to plan for the future. Why? Because commerce shapes the future because commerce is culture. We’ll see you next time.
