Athos Commerce CMO Gary Lombardo on why only 14% of shoppers see one consistent brand

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Speaker B: Hi, I'm Greg Kilstrom, host of the Agile Brand, and here's a question for you. If a growing share of your customers now begin their shopping journey on a platform you don't own and can't see into, is your website still your most important marketing asset? Agility here isn't just about reacting to new customer behavior. It's about building an infrastructure that can meet customers wherever they show up and still have the brand behave like one company when it gets there. Today we're going to talk about the great fragmentation of product discovery and why traditional marketing funnels are becoming obsolete strategies for maintaining brand and data consistency across countless channels when only 14% of consumers say it's done well today, and the role of AI and automation in not just personalizing experiences, but optimizing for new discovery engines. Welcome to season eight of the Agile Brand Podcast. This season we're going all in on Expert Mode, MarTech, AI and Customer Experience, talking with the people and platforms behind the brands you know and love. Again, I'm your host Greg Kilstrom and I help Fortune 1000 companies make sense of martech, AI and marketing ops. Hit subscribe or follow to make sure you always get the latest episodes and leave us a rating so others can find us as well. And make sure you check out our sponsor, Tech Systems, an industry leader in full stack technology services, talent services and real world adoption. For more information, go to techsystems.com now let's dive in. To help me discuss this topic, I'd like to welcome Gary Lombardo, CMO at Athos Commerce. Gary, welcome to the show.

Speaker C: Hi Greg. Thanks for having me.

Speaker B: Yeah, looking forward to talking about this with you. Before we dive in though, why don't you give a little background on yourself and your role at Athos Commerce?

Speaker C: Sure. Happy to. Uh, I'm the CMO at Athos Commerce. We help brands connect the right shoppers to the right products across every channel they show up in, not just on their own website. Uh, that's become a much bigger job than it used to be because um, every channel now includes AI platforms, social platforms and marketplaces, uh, that brands don't control. And uh, to give you a sense of scale for us, we work with um, more than 2,700 brands over 50 countries, uh, with brands like Adidas, Burberry, New Balance, Clarins, Marks and Spencers and others spanning across all different industries from beauty, sports and fashion amongst others. Uh, and our roots go back to 2007. So we've had a long Runway to watch this discovery platform, the discovery problem rather evolve and now accelerate with AI uh, before this my background has really been in the intersection of commerce, um, e commerce, search and data. I've been in the industry for a while and I think these skills that I bring to the table sort of are at the right moment. Right. Which uh, this sort of confluence of um, AI kind of changing the landscape of commerce, uh, is an opportune uh, time for me to be at Athos, uh, and in part of the industry. So excited to be here. And um, yeah, we got some good data that I think we'll delve into as well in a recent report that we released, uh, with Draper.

Speaker B: Yeah, yeah, let's dive into that actually right now. And so your recent connected consumer research, well does a few things. One of the things that highlights though is a major shift with 60% of consumers now using a tools while shopping. I know we saw a big part of that and that growth last holiday shopping season, but it's certainly continued. So when discovery starts somewhere a brand doesn't own and can't truly observe, what's the first thing that breaks inside a large marketing organization?

Speaker C: Yeah, that's a great question. Um, usually it's the org chart that breaks first I think before the technology actually does. Most large marketing teams are still structured around channels that they can own and measure like paid, the website owned email, maybe social, maybe some others. And the ah, moment that 60% of your customers are researching through a tool you don't own, don't control and um, can't instrument with your usual analytics tags, for instance, you've got a structural gap. There's no one whose job it is to own. What does uh, AI say about this? Although that's starting to change as well. As we come smarter with A.I. um, we see this gap constantly really, um, with a lot of brands, including in our own customer base. Right uh, across the 2,700 brands that we run into. But like I said, they're getting smarter about it as well. Um, but certainly than they were, say like 18 months ago. I think the second thing that breaks is the confidence in the funnel itself. So teams built their whole planning cycle, like budgets, KPIs, calendars, campaigns, et cetera, around a linear top of the funnel to purchase model. Um, when discovery starts on a platform you can't see into, that model doesn't just get harder to measure, but it stops being true. Right. So organizations don't reorganize around this new truth quickly. That's really the first crack. So I really think it's about the org chart and sort of the funnel being changed, uh, in terms of what, uh, we're seeing today in marketing organizations.

Speaker B: Yeah, yeah, definitely. I mean, the funnel, uh, it's breaking a lot of what was held to be just a constant and true. Right. So it's definitely. I think the other thing is, uh, and your report certainly says as much is, you know, only 14% of consumers find product information consistent across channels. And what we know is consumers are jumping from channel to channel. I mean, they've been doing this for a while, but it seems to be just the norm now. And yet again, less than 15% are finding consistency in product information. When you trace that back to its source, is this inconsistency? Is it a content problem, a data problem, a decision logic problem? What is it? Maybe all of the above.

Speaker C: Ah.

Speaker B: And does the answer change? Um, who inside the company owns fixing it?

Speaker C: Yeah, for sure. I think it's tempting to call it a content problem because that's the visible symptom. Right. Like you get the wrong price on a marketplace or maybe there's a missing size on a social post, whatever it might be. Uh, but if you trace it back, it's almost always a data problem and say, kind of wearing content's clothes, so to speak. Right. So most brands don't have one canonical structured source of truth for product. They have, you know, PIM, product information management system that is 80%. Right, right. A feed file that gets patched maybe by hand by one channel and a merchandiser spreadsheet that is more current than the others than either. Right. So it's this data, this product data problem specifically, uh, that's at the core of it. And we've seen this with our clients as well. Like, uh, one of our big clients called the Accent Group, uh, helped tighten up their feed accuracy and data consistency through Athos. Right. They saw a 60% increase when they did this in their revenue over the course of eight weeks. Really, it's kind of being able to ensure that that product data is, is true, is, is solid. Right. So that you don't have this, this disconnect right across what consumers are finding, uh, across channels and yeah, kind of around the question of ownership. You know, really who owns it. I think it, this, if we think of it as product being in the center, does change who owns it. Right. I think it's, um, if you try to frame it up as a content problem, marketing owns it. Right. It's pretty clear. Um, but if you frame it up as a data and decision logic problem, which I think it is, becomes a joint mandate across marketing, e commerce ops and it, with sort uh, of one team accountable for this feed pipeline itself, that, the pipeline of that data that needs to go off to the various channels, including your own website, to ensure that it's accurate. So that team needs to really be on top of it. And I think that reframe for organizations really is really the, the key unlock when they start to think about how can we get better and not have this, as we saw in our research, this 14% of consumers not finding the product information consistently across the channels. Um, it's been a problem, as you said, for a long time, but I think it's exacerbating now. Ah, particularly with the advent of AI.

Speaker B: Yeah, yeah. I want to get back to the funnel conversation a little bit here and talk a little bit about, um, geo. Generative Engine Optimization, sometimes called AEO or probably other acronyms out there too, but certainly getting a lot of attention. We've talked about it a few times on the show already as well. I think one of the common, uh, misconceptions in some organizations is that it's more similar to SEO than it actually is. And so just what do you see that's structurally different about that as compared to just saying, oh yeah, let's put the SEO team or let's, you know, kind of apply the same lessons we learned from SEO to it. Uh, you know, what do you. And what do teams get wrong when they treat it as such as just sort of another, another flavor of SEO?

Speaker C: Yeah, for sure. And I think this is, it's still early on, even though we've been talking about GEO for, seems like forever now, but reality.

Speaker B: Right.

Speaker C: Pretty, pretty short time frame and kind of similar, you know, we think back. I'm old enough to remember when SEO was the hot term.

Speaker A: Right.

Speaker C: We're all trying to figure that out. I think that's kind of where we're at as well. But SEO is fundamentally like a retrieval game, right? You're optimizing to be one of the ten blue links or whatever it is on the SERP page that a human will scan and click. Uh, GEO is really a synthesis game. You're optimizing to be the fact the model is choosing to restate. Right. Oftentimes there's no click at all. Right. So those are really different jobs, really vastly different jobs. One's rewarding the ranking, right. The other's rewarding being unambiguous enough that the model can lift your attribute and repeat it correctly. Right. So that's two, I think, very fundamentally different things. And we're all trying to figure out, okay, great, what does that mean? How do we best do it? And I think the mistake teams that make is porting over the concept of SEO specifically around keyword thinking, stuffing the pages with phrases they think ChatGPT, Gemini, Cloud, et cetera is searching for. Right. But these models aren't ranking the keywords necessarily. They're synthesizing, uh, structured facts pulled from uh, the catalog, from reviews from your product feed, wherever they're trained or grounded. And again, it comes back to that sort of central problem we talked about earlier around the product data being at the core. And if that's inconsistent, incomplete or contradicts itself across sources. And again, remember that 14% consistency number we talked about earlier? The model's going to get you wrong or just leave you out entirely. So I think GEO ultimately starts with data hygiene, right? Not keyword search. So that's the keyword research. That's the fundamental difference.

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Speaker C: Yeah, that's a great question because we're seeing, you know, conversational commerce as we call it, right? An assistant or just a transformation at least in the front end, how consumers are interacting from a shopping perspective really taking hold now. And I think it's going to accelerate over the next few months, uh, for sure. And I think three things really need to be in place before you kind of hand over. Handover is maybe not the right term but strong. But in a lot of cases you are right, but sort of enable this agent, this, this conversational system to be on your website or wherever it may be. The first thing is that back to the data, that underlying data has to be trustworthy in real time. So uh, the inventory, the sizing, the policy, the product information. Because an assistant that confidently tells a customer something is in stock, uh, when it isn't or can do more brand damage than a slow website ever would, for instance. So I think secondly you need clear guardrails and what the assistants allow to decide versus escalate a return exception, ah for example should have a defined boundary and not be improvised in the moment uh, for fairly obvious reasons. Right. Um, and then I think third, someone in the organization has to actually own the assistance answers the way you'd own a policy document. So reviewing auditing, correcting drift. Right. Really taking a look at um, what's been said because once it's live, it's Your brand's voice and your brand's commitment, uh, at scale, without a human in the loop on every conversation. So you want to have some level of control over that. I think we're ways away from the perfect conversational assistant being there and providing the right answer all the right time. I think there always needs to be this human element, um, that ultimately is doing that review. So I think it really comes down to those three things.

Speaker B: Yeah, well, and I want to talk about measurement, uh, here as well and maybe going back to the new or the most recent iteration of the funnel. A customer starts on an AI platform, they go to social media and then they end up in a direct purchase. Obviously the traditional brand funnel as well as traditional attribution models break, frankly. Uh, so rather than trying to rebuild attribution for channels that are difficult to observe and sometimes not able to be observed directly, what's the first credible signal that a leader can use to tell whether a brand is even showing up in some of those machine mediated consideration steps?

Speaker C: Yeah, I think, uh, we're also used to saying, okay, what converted? Right, right. It's, I think with these uh, these AI platforms like do we even exist? Right, right. Do we even exist in the answer? So I think the most credible first signal is systematically prompting the major AI platforms with queries your real customers would ask, like category comparisons, you know, or specific product questions, you know, what's the best product for X or Y or whatever it might be in tracking whether and how accurately your brand shows up. Uh, that's a visibility audit. Right. Not attribution, but it's the leading indicator of everything else depends upon in this new world of thinking about how your brand is coming across on AI and never mind your products, are they being surfaced. I think, uh, the second signal is directional but pretty useful. I think it's pairing that with direct branded search traffic trends. So if a machine mediated consideration is happening, for instance, you'll um, you'll often see people arrive already knowing your product name, arriving to your site or SKU rather than arriving from a generic category search. So it won't give you a clean attribution path necessarily, but it tells you whether you're in the conversation at all, which frankly is the uh, more urgent question right now.

Speaker B: Yeah, yeah, well, and you know, talking about operationalizing this as well, certainly, you know, we've talked about it from the customer perspective, but you know, managing product fees feeds merchandising for dozens of channels. There's an operational strain here and the governing consensus seems to be to Apply AI to it. Which AI can mean many m. Many things. But where's the honest line between a system that helps a uh, merchandiser work faster and one that's actually making the merchandising call itself? And who's accountable when it's that second scenario?

Speaker C: Yeah, for sure. And this is a problem that we're dealing with every day at Athos. We get some pretty cool uh, technologies and agents that we recently released to make the merchandiser work faster. Right. But with, with uh, with human intervention of course. So I'll touch on that a little bit too. I um, think the honest line at the end of the day is whether a human can explain and defend the decision after the fact. So if AI is the is like flagging feed errors or surfacing which products are underperforming on a channel, auto generating attribute fixes for a merchandiser approve, that's assistance. Like that's really the right way to do it. It's compressing hours of manual audits into minutes. Really it's that efficiency gain, not to mention accuracy. Quite honestly, uh, a lot of um, the machine models could do it a little bit more accurately than a human. So however, contrast that if AI is like silently like working behind the scenes, repricing, re ranking or suppressing products across live channels with no review step, that's uh, the system making the call. Uh, whether anyone officially decided that or not. That's exactly the model that we don't necessarily want to have in play. Right. I think recommendations of that and making sure there's that human element. Our customers at Athos, like Early Settler for instance, uh, has done very well with the former. As I described, uh, around the time savings they've saved 10 plus hours a week through automated merchandising. We've seen others like uh, Top Tiles where they've roughly halved the time it takes for them to spend that they've spent on manual search related tasks, for instance. So in all of these cases a person still approving what actually ships live, um, and accountability doesn't move because a machine made the decision no matter where it is, um, it stays with whoever owns the channel's commercial outcome. So typically that's the m. Merchandiser or E Commerce leader, not the AI vendor or the algorithm. Right. Uh, our view at Athos, as I heard me say before, is that technology should um, should make the merchandiser faster uh, and better informed and more accurate. Right. With visible, with a visible audit trail and not quietly replace their judgment or actually what they're doing their job for. Instance completely the um, moment a team can't really explain why a product got to a certain, got a certain treatment, they've crossed the line, um, without really realizing it. So that's something that we work hard to avoid.

Speaker B: Yeah, yeah. Well, and um, I want to talk a little bit about some present day, but moving to the future as well, where the buyer is an agent. Right. And we're seeing this already in small ways, but certainly um, Athos uses the term agentic commerce to describe this future. If the first evaluator of your product is an agent. Comparing structured attributes, price, availability, specs, return policy, as opposed to what humans may evaluate on where does brand preference live in this scenario and where does differentiation actually live in this exchange?

Speaker C: Yeah, for sure. And that's a great question as um, somebody who thinks about the brand. I'm always wondering in this new world are we in control or just like we talked about the funnel breaking brands to a certain degree breaks. But I don't think brand preference doesn't disappear in the new world. It really moves kind of upstream and downstream of that exchange, uh, upstream it lives whether the agent is even considering you, as we talked about before, are you being found? Right, that's the geo and structured data work we talked about earlier. You can't be preferred if you're not in the candidate set, for instance. So just that piece of your concept of branding still needs to exist. Uh, downstream it lives I think in the moment the human. Right. Because ultimately the shopper is still making a decision, looks at what the agent surfaced and decides whether to trust it. Brand recognition, price, memory, past experience still shape whether someone accepts the agent's top pick or asks it to look again. Right. So eventually, you know, maybe we reach this agent to agent world and what happens there? Um, different question, but I think largely it's you know, agent to human at the end of the day making the actual shopping decision. So I think those two elements of a brand are important. But within this exchange I think it's differentiation shifts from uh, persuasion to precision. So if an agent isn't uh, swayed by uh, you know, agents aren't necessarily swayed by like big beautiful hero images or clever headlines. It's swayed by more accurate or complete structured attributes. And we've seen this precision over persuasion effect show up concretely with some of our customers, like David Jones, one of our department store customers saw uh, uh, 66% performance uplift simply by tightening up how their product data was structured and represented with no creative changes necessarily involved, no quote unquote classic Branding changes. So paradoxically, I think in the agentic world, uh, being scrupulously honest and complete about your product data, uh, becomes a competitive advantage in its own right. And the brands that are going to win are the ones that ones an agent can trust enough to recommend confidently.

Speaker A: Yeah.

Speaker B: Well, Gary, thanks so much for joining today. I, uh, got one last question for you as we wrap up here. What do you do to stay agile in your role and how do you find a way to do it consistently?

Speaker C: Yeah, that's a great question. I think, uh, for all of us marketers out there, a lot of us are like, hey, how do we stay relevant in this, this AI world? And I think marketing, in my opinion, is more important than ever in this AI world. And for me, you know, staying agile is really, uh, making sure that I am talking to our customers, thinking like our customers actually, uh, shopping, right. Uh, through the AI platforms. You know, what products are showing up through social, not just the AI platforms necessarily thinking like the end consumer, uh, because the market's really moving faster than ever. So it's acting like customers talking to our customers, really hearing what they're going through firsthand. And I think operationally too, in our team that I run, it's like building out a, uh, standing rhythm, um, of small experiments rather than waiting for a big annual plan to be right. So testing a channel, test the results, adjust, et cetera. So agility is really something that I think is at the core of it. So, and of course, like everyone else too, I'm in my fingertips and trying to get into AI and figure out how we automate some of our marketing processes and do things smarter internally. Which, um, you know, when you get a small team and limited resources, uh, it's critical to do that and also a challenge to do it at the same time. So those are things I'm constantly thinking about to, uh, stay agile in my role.

Speaker B: Yeah. Love it. Well, again I'd like to thank Gary Lombardo, CMO at Athos Commerce, for joining the show. You can learn more about Gary and Athos Commerce by following the links in the show. Not this episode is brought to you by Tech Systems. They're leaders in full stack tech services, talent solutions and helping companies put it all in action. You can learn more@, uh, techsystems.com that's teksystems.com and thanks again for listening to the Agile brand podcast. If you like the episode, hit subscribe and drop a rating so others can find the show too. And if you're interested in consulting advisory work, or if you need a speaker for your next event, feel free to reach out. Just visit GregKilstrom.com that's G R E G K I H L S T r o m m.com the Agile brand is produced by Missing Link, a Latino owned, strategy driven, creatively fueled production co. Op. From ideation to creation, they craft human connections through intelligent, engaging and informative content. Until next time, stay curious and stay agile.

Speaker C: M

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