So What? How to Get Started with AI in the Affordable Luxury Industry

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In this episode, Katie Robbert, Christopher Penn, and John Wall break down how smart teams use machine learning to decode the affordable luxury market.

This breakdown will show you real customer desires through AI in the affordable luxury industry. You’ll discover hidden buyer groups that boardroom meetings overlook, a exploring AI in the affordable luxury industry, you will pinpoint rival blind spots and seize untapped sales.

The result will give you a decisive edge across any market you enter.

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In this episode you’ll learn:

What use cases make the most sense for the affordable luxury industry
What data you need to start
How to build your first product marketing gap analysis

Transcript:

What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode.

Katie Robbert – 00:00

Well, hey everyone. Happy Thursday. Welcome to So What?, the marketing analytics and insights live show. I’m Katie, joined by Chris and John. Howdy, fellas.

Christopher Penn – 00:37

Hello. Hello.

Katie Robbert – 00:39

And happy almost Halloween to those who celebrate. Yep, Chris has his electronic grim reaper behind him and some other various decorations. John and I do not, but that does not mean it does not live within our souls.

John Wall – 00:57

I’ve been going with every day is Halloween anyways, because we now have infinite horror stories pouring through the news every day. So.

Katie Robbert – 01:07

Fair. On that happy note, this week we are talking about how to get started with AI in the affordable luxury industry. And you’re probably going, why is Trust Insights talking about the affordable luxury industry? So one of the things that we’re challenging ourselves to do, and also challenging others to do, is think about what use cases in other industries outside of the ones that we know really well, what could be there, where could people get started? And I know you look at the three of us and you’re like, wow, these are three big experts in affordable luxury. Like, Katie’s fashionable, and Chris and John are definitely shopping the big brands, the luxury brands.

Katie Robbert – 01:51

So all of that to be said, Chris and I had to Google what affordable luxury was before we could mostly speak correctly on it. We could still be getting what it is wrong, but we won’t get the use cases wrong, because use cases, what we found, are fairly universal regardless of industry. My limited understanding of affordable luxury, and people can chime in the comments, is that with affordable luxury, you think about the luxury brands, like fashion brands or car brands. So you have, like, Coach, you have… wow, I really got stopped at that one, because I don’t know the brands. But think about the high end, the really expensive, like a Louis Vuitton or an Hermès, or those kinds of brands that tend to be out of the price range of the average consumer.

Katie Robbert – 02:42

Those brands tend to have a line of business that is affordable for the average consumer. So you’re still paying for the name, but it might not be the hand-stitched artisanal version that you would pay $10,000 for. You might be paying more like $100 for a slightly more mass-produced but good quality version of the same thing. So it’s not a knockoff, it’s still part of the brand family, but it’s more friendly to your budget. So that’s my limited understanding of affordable luxury. Again, I’m sitting here in my Carhartt, and all of my furniture is second and third hand. So I clearly don’t know affordable luxury. Anyone else want to add to that, or let’s just move on?

John Wall – 03:30

Yeah, I can definitely use guidance. I mean, there’s a bunch of different luxury items and levels of stuff, but yeah, I tend to gravitate towards nerd watches or cars. So yeah, if you’re talking clothing, no, shopping at Target is not fitting that bill.

Katie Robbert – 03:47

I don’t think so. I think it’s… Go ahead, Chris.

Christopher Penn – 03:50

There was a paper that came out in 2019 from one of the many universities out there that coined the term masstige, which is mass prestige. The idea of the luxury brand, the status that comes with it, plus some of the durability or product quality, but at that mass production level, is sort of their definition of this affordable luxury. It’s that masstige: mass-produced, higher quality stuff, but not bespoke.

Katie Robbert – 04:19

Got it. So I wasn’t too far off.

Christopher Penn – 04:21

You were right in the ballpark.

Katie Robbert – 04:23

Amazing. We love it. All right, so Chris, you and I talked about this on the podcast earlier this week. And if you want to catch that, it’s at TrustInsights.ai/tipodcast. But the real question, and this is something that comes up when we’re talking with people about getting started with AI, is we tend to think only about what’s directly in front of us. We look at our industry, we look at our team, and that’s a good place to start, but it’s limiting in terms of what’s possible. And that’s why we wanted to start to look at other industries, outside the industries we usually work with. And that’s how we landed on affordable luxury, because nothing is farther outside of what we currently do for work and who we serve as clients.

Katie Robbert – 05:07

So Chris, how do we get started with trying to figure out where the affordable luxury industry can even begin with AI, if they haven’t already?

Christopher Penn – 05:17

So one of the things that’s different about affordable luxury and the industries we usually work with is there is a tremendous amount of very high quality data about it out there publicly, because it’s an almost $400 billion industry globally. And in places like China, Japan, Europe, America, Canada, there are huge markets and very highly motivated buyers. So the good news for us, as a data-driven company, is there’s a lot of data that we can get a hold of to get up to speed very quickly on it and understand the problems in it. And the two areas that I thought we’d look at today as worked examples would be, first, who is the customer? Because we’re all sitting here going, well, clearly none of us are it. But.

Katie Robbert – 06:07

We don’t know that’s actually true.

Christopher Penn – 06:10

It is, it’s true. Okay, spoilers.

Katie Robbert – 06:14

Okay.

Christopher Penn – 06:15

And second, if you know who your ideal customer profiles are, with what data externally is out there, could you draw some interesting strategic conclusions that could guide your marketing, your sales, your product marketing, your go-to-market strategy, all these things? So that’s kind of what we pulled together for today’s show. Now, the first part is building ideal customer profiles. So Katie, because you’re about to talk about this at the MarketingProfs B2B Forum next week as we record this, do you want to talk about basically what goes into ideal customer profiles at a high level?

Katie Robbert – 06:53

At a high level. So a lot of us have what we call a buyer persona. And a buyer persona is the person who buys from us. That’s fairly straightforward. As we have been using generative AI to supplement those buyer personas, we’ve been able to bring in more useful information other than just, we want B2B, mid-size, a thousand employees, whatever. We can do better than that. So we started a few years back with generative AI, pulling in literally who we wanted to target as potential customers: LinkedIn profiles, company overviews, those kinds of things, to build out this ideal customer profile based on the kinds of things these individuals talk about. What are their motivations, what are their buying patterns, what keeps them up at night?

Katie Robbert – 07:46

And so that was a good place to start. But now that the technology has evolved, there’s even more opportunity to bring in even richer data, which is what I’ll be talking about at the MarketingProfs B2B Forum on Monday, November 2nd at 4:05 PM, so you can catch me there. And the thing that I learned, and what I’ve started to bring into my own ideal customer profile work, is you can go back to your own data. You don’t necessarily have to look outside. So it’s the evolution of the buyer persona, where you’re looking at who buys from you. When we talk about ideal customers, we’re like, this is who I would like to have. But then we skip over who we actually have.

Katie Robbert – 08:33

And so we need to have that layer of analysis of who is buying from us, because it might be a mismatch from who we think our ideal customers are. But then in addition to that, we need to have who’s not buying from us, and those are the clients that we’re losing. Whether it’s the wrong fit, or we priced it wrong, or whatever the situation is, we need that data as well. So we need to know who we’re targeting, who we’re getting, and who we’re losing. And that makes a fully comprehensive ideal customer profile. I guess you don’t have to come to the talk now, because that’s it. That’s it. I’m done.

Christopher Penn – 09:10

So for the affordable luxury industry, we pulled together… how many sources did I pull for this? 72 different sources, including academic papers, research studies, and pretty much every consulting firm on the planet has published some sort of state of the affordable luxury market. So EY, Bain, BCG, McKinsey, everybody. They’ve all got these massive 100-page reports. I didn’t read them. I skimmed them. I did not read them, because they all basically say the same thing. And then what we did was use our Deep Research Suite, which is available in the Trust Insights Academy at academy.trustinsights.ai. I said, okay, take this, plus take Google Trends data, plus take SEO keyword search data, plus take Reddit data, and merge it all together, dedupe it, and help me identify who is this market.

Christopher Penn – 10:05

And it came down to six broad ideal customer profiles. Number one, the exclusivity signaler. This is a person who’s like, I want to show off my status, my exclusivity, that I got money. That’s number one. Number two is the status stretcher, which is basically someone who wants to get to number one, but they don’t have the budget, so they’re interested in any kind of visible logo piece. Number three is the person for whom the brand is less important. I want something that is the highest quality that I can afford. So they want a handbag that will last them 70 years.

Christopher Penn – 10:46

Number four is a value seeker shopper who doesn’t necessarily have brand loyalty, but they want to be seen in the affordable luxury market, so they’ll buy whatever they can buy that makes them look good. But they’re not loyal to a brand. That is different than number one, which is like, I am a Coach person or a Gucci person.

Christopher Penn – 11:05

Number five and number two are similar, but number five is someone who wants to become number one within a specific brand. So I’m not looking for a deal overall, I’m looking for how do I get started becoming somebody who’s known as a person who buys Louis Vuitton. And number six is someone who doesn’t have loyalties to anything but wants to find things that complement their self-identity. So the luxury brand may be part of their origin, it may not be. And they’re the kind of person who can mix and match. They would be comfortable with a Prada handbag but also wearing Carhartt, because they are expressing their individuality, not the status around luxury. So those are the six ideal customer profiles that distilled down from all of this data.

Katie Robbert – 11:53

That’s really interesting, because what I’m hearing very clearly is you sort of have two major categories. You have the brand loyalists, the brand evangelists, who are like, nope, this is my brand and only this. And you definitely see the people who are head to toe in one specific brand. And then on the other side you have more of the status customers, who are like, it doesn’t matter what the brand is, as long as it demonstrates the value and the luxury and that I have the money to spend on it. So it is interesting that you see those two distinct buckets start to filter out. That’s not the way I would have thought about it, because I would have thought about it brand by brand.

Katie Robbert – 12:42

I would have thought immediately, well, we’re looking for loyalists. Not necessarily.

Christopher Penn – 12:50

Exactly. And going back to our podcast from Monday, now that we know that these ideal customer profiles are not necessarily who we thought they were, this alone would change your go-to-market strategy, because you’re like, okay, we can’t focus on just this one cluster of people, because it’s not a distinct demographic. It is psychographic. It’s really looking at behaviors and motivations. And so part of the ideal customer profile process then is to create these cards that help a brand understand, like, the exclusivity signaler buys recognizable luxury as a visible proof of arrival. They pay full price as long as the brand stays exclusive. And they move. This is really important: they move to a different brand if you dilute your brand.

Christopher Penn – 13:39

So the one that I always come back to is Wolfgang Puck. The chef has put his name on freaking everything. You can go to Logan Airport, and there’s a Wolfgang Puck gourmet sandwich stand. I’m sorry, that’s airport food. There’s nothing luxury about that. It’s cold tuna sandwiches in the case. And so what he’s done is dilute his brand so much that it’s meaningless. So if you were this exclusivity signaler, you’d say, I’m done with Wolfgang Puck, because that’s not who I am. That’s not who I want to be seen dining at, because his name means nothing now if I can get a Wolfgang Puck sandwich at the airport.

Katie Robbert – 14:18

And so, because we’re talking specifically about affordable luxury, if you can go back to the six ICPs, the first question is, well, which of these falls into the affordable luxury business line? Because if I was…

Christopher Penn – 14:38

They all do. They all do.

Katie Robbert – 14:42

That’s interesting, because, and I guess this is where we’re trying to figure it out as we’re getting started with AI in affordable luxury, we’re using the pattern recognition tools within a large language model. And I’m saying, well, I think that the exclusivity signaler just wants status proof, and that’s not affordable luxury, because they’re willing to pay the top of the range. That doesn’t signal affordable to me. So that’s where I would need that additional information to help me understand what that actually means for ICP number one.

Christopher Penn – 15:19

The way that I would categorize it is that there are bands. So there’s true luxury, where you’re going to spend 50 grand on a handbag. That exclusivity signaler, that behavioral trait, occurs in both affordable luxury and actual luxury, but they’re different buyers by budget. So think of the affordable luxury segment as that mid-range band of, hey, they want exclusivity, they want status, they want quality, but they can only spend within a certain band. They can’t swing $150,000 for a dress. Very few people can. And so they’re in that mid-range band of, okay, I’m willing to spend $5,000, but not $500,000. So some of these personalities would go away in the true luxury band, because they just don’t exist there.

Christopher Penn – 16:15

But in the affordable band, they’re there.

Katie Robbert – 16:17

Got it. And so to tie it back to what we’re talking about today, this is a really good exercise for getting started with generative AI, because as you can see, Chris and I are debating the different ICPs based on what we think we know from our own personal experience. But generative AI is going to say, just give me your data and I’ll tell you what it says. And I think that’s a great place to start with generative AI in any industry: gut check who you think your ideal customer is. Because I’m learning a lot just looking at this. The self-expression player.

Katie Robbert – 16:54

That one’s really interesting to me, because that might be an overlooked audience, because they’re not looking for a label or logo specifically. They’re looking for, well, my outfit’s purple today, so I need something contrasting. So what do I have that’s red? Or, I really like things with tassels or fur or whatever. So it’s not necessarily the brand and the logo. It’s the style, it’s the comfort, it’s the usefulness of it. Can I get everything inside of this bag, or is it just too small? So I’m going to go someplace else. They don’t care about who made it, they care that they can use it.

Christopher Penn – 17:36

Yep, exactly. Now, to talk about the AI part of this, here’s how we built this. First, we used the CASINO prompt framework from our Deep Research Suite, which I mentioned before is in the Academy. And I created a prompt saying, we’ve got to understand the affordable luxury buyer. Who the heck is this? So I built the prompt, and then I handed it off to Google’s Gemini deep research facility, Gemini Spark, although now it’s called Gemini Tasks; Alibaba’s Qwen, their deep research facility; MiniMax’s deep research facility; and then Meta Muse, because I have Muse running in a sandbox, because it is superbly good at extracting information, including from places you don’t want to be extracting information from. But sandboxed properly.

Christopher Penn – 18:24

It is a fantastic research tool for digging stuff up, particularly from social media, which is useful for getting verbatims, getting the actual words of customers. So those five engines went out and grabbed data, along with about 6 million words from 23 subreddits that are all watches, handbags, this, that, and the other thing, to try and differentiate who is in the affordable luxury bracket, from a conversational perspective, versus who is in the actual luxury bracket. And spoiler, people who are in the actual luxury bracket don’t spend a lot of time on Reddit.

Katie Robbert – 19:01

I am shocked.

Christopher Penn – 19:04

But that’s useful for our investigation. So once we had all the data from these different AI tools, we put it all into one bucket, basically, and used Claude Code with the Deep Research Suite. There’s a part of the Deep Research Suite called Research Merge, which basically says, hey, here’s all this data. You have to combine it, deduplicate it, and fact-check every assertion. Where there are conflicts, the highest quality data wins, and if it’s a genuine tie, majority wins, etc., and then synthesize it into an actual set of reports. And we tell it, you have to build a technical report, a stakeholder report, and then an administrative sidecar that lets Claude be as verbose as it wants, because it loves to talk. And so you give it a little playground, a little sandbox where it can talk.

Christopher Penn – 19:56

And so what you’re looking at is the stakeholder report, for someone who’s in product marketing to look at and go, oh, this is who we’re making product for.

Katie Robbert – 20:08

That is… of course, I’m like, okay, well, note for later. We need this too. Because I feel like as these AI systems get more and more advanced, for better or for worse, the way that we think about who our target customer is is only going to get better. A lot of us right now are limited in what we think we know about our customers. And so being able to do that deeper dive, the deep research, which if you want to try this for yourself, you can go to TrustInsights.ai/deepresearchcourse and purchase your seat. Chris goes through a really nice deep explanation of what it is. And then you get the plugins and all the stuff you mentioned. We’re not using enough of that.

Katie Robbert – 21:01

When we’re thinking about our customers, we’re like, all right, so we’re B2B, so we must be this. And we probably serve managers, directors, and above. And our decision makers are likely these people. And because things move so fast in an organization, sometimes that’s as far as we get. And so now that we have these tools available to us, we need to really know how to take advantage of them.

Christopher Penn – 21:26

Yep. So this is the first half of how I would get started with AI in the affordable luxury industry. Because as someone who doesn’t work in that industry, if I needed to get smart about it real fast: who’s the customer? I’ve got to start there, because otherwise everything is pointless if I don’t serve the customer. So now we have that. Now we have six different profiles. The second place to go is, and I narrowed this down just to handbags, so Gucci, Louis Vuitton, Michael Kors, and Coach were the four brands that I selected: what data can we get about those brands specifically that’s quantitative? Well, it turns out we can get search data, a lot of search data that is rich and robust, from tools like Semrush, Ahrefs, Moz, SpyFu, you name it.

Christopher Penn – 22:12

There’s an SEO tool for it, and not AI visibility or GEO tools. Put that crap aside. Good old-fashioned SEO tools are the way to go here. And so what I did was, we use Ahrefs, so I’m going to go to each of these brands’ profiles in Ahrefs and say, what keywords does this brand rank for? And in some cases we’re talking hundreds of thousands of search terms. And I said, here’s the ICPs from part one, here’s all the search data. Hey, language model, break these keywords into four buckets. Informational, basically people just looking for information. Commercial, people indicating some kind of buyer intent, comparison shopping. Transactional, people who are saying, help show me where to buy this thing, this handbag, whatever. And then retention.

Christopher Penn – 23:07

I’m an owner, I need help, I need support, or I want to share something about my experience. So those are the four keyword buckets. And then we hand the keyword data to a tool like Claude Code or ChatGPT Work or what have you, with our ICPs, and say: what is each brand good at, what is each brand bad at, what is each brand missing? And what is the industry overall missing that a company like Trust Insights could help support?

Katie Robbert – 23:36

So is this essentially sort of a hybrid competitive analysis slash content gap analysis?

Christopher Penn – 23:44

It is, it’s a hybrid, but it’s informed by the language that people are searching with, mapped back to the ICPs, which I don’t think we’ve done before.

Katie Robbert – 23:53

I don’t think we have. And I’m a little dismayed that my Carhartt crossbody bag that I keep all of my dog stuff in didn’t make the list.

Christopher Penn – 24:02

You know.

Katie Robbert – 24:07

John, before we get too deep, is anything coming as a surprise to you so far? About, one, what you thought you knew about affordable luxury, and two, where we would suggest people get started with generative AI in any given industry?

John Wall – 24:24

No, it’s clear that price banding is a big thing. Of course, customer motivation makes all the difference in the world. I think one big takeaway is that whole idea that there are certain customers that never want the price to go down. That’s why they’re buying, versus people that want access to the market or are looking to buy deals. So your marketing strategies are radically different for those two groups. But after that, I think it’s a good example of how you have these hundreds of variables. There’s just no way this will ever get figured out around a boardroom table by 20 people.

John Wall – 25:04

You have to be running this through a model that can take these thousands of variables and figure out what’s relevant, what needs to come to the top, and what your strategy should be. Because this is just like the classic disaster of the board and some managers getting into a conference room and screwing up your strategy for the next year. You can’t afford to do this without gathering all the data and taking a structured approach, because otherwise you’re basically just having a couple people run off and do some crazy things and pray that it works, which is hopeless.

Katie Robbert – 25:41

And I think this is where, Chris, we would have you tell your favorite missed customer segment story. We told this on the podcast earlier this week, but I think it’s worth repeating, because it highlights exactly what John just said: people sitting around a table making decisions miss big opportunities with potential customers. And so, Chris, what is the story?

Christopher Penn – 26:02

So this goes back to the Netflix special about Hasbro’s My Little Pony. The assumption for your average exec sitting around a table is, our ICP is an 8 to 14-year-old girl. And what was shown in the Netflix special on Bronies is that there’s a huge, extremely well-funded customer segment of 26 to 40-year-old men who have like 100x the disposable income of an 8-year-old, who will pretty much buy anything this company makes in this genre. And so if you built a go-to-market strategy around the assumption that your audience was 8 to 14-year-old girls, you would miss this whole other market. You would not cater to them. You would not find ways to create affordable luxury versions instead of commodity versions of the toy.

Christopher Penn – 26:50

Maybe you might even have a true luxury version. You might have a $10,000 limited edition, only five made, handmade, handcrafted, whatever. But that’s the example that we use often: this is a big market segment that has way more money to spend, and you’re completely missing it.

Katie Robbert – 27:13

So, okay, we’ve done the ICPs, we’ve used generative AI, we’ve used a lot of research, a lot of data. So now we have a good handle on our ICPs for affordable luxury. And then you said, now let’s take a look at what people are searching for, what they’re asking about. And so you’ve broken it down. So what do we get from this?

Christopher Penn – 27:36

So we get four big lessons. Number one, Coach and Michael Kors are deal-driven brands. People are all about the outlet version, stuff like that. People are looking for deals, so that serves the deal-driven value seeker. That is their customer, that’s their ICP. Louis Vuitton is mostly F1 and F2. If we go back to earlier, F1 and F2 are the exclusivity signaler and the status stretcher. Louis Vuitton, that brand is doing well there. One keyword carries the most signal for two brands: outlet. Coach outlet. And of course people are looking for the outlet store by name. And Gucci is the least deal-driven. The people there are F4 and F6. F4 is the deal-driven value seeker, F6 is the self-expression player, the one you were talking about, Katie. Gucci serves that market.

Christopher Penn – 28:31

So each of these brands’ search data currently indicates different ideal customer profiles, and that means that each one has vulnerabilities. So if, say, Coach was our client, we would say, hey, you are only getting people who are looking for a bargain. You are missing F1, you are missing the exclusivity signaler, the one who’s going to pay full price. What have you got in your product lineup that can go at full price? Maybe at the top of that affordable luxury pricing bracket, that will be highly profitable. Number three is really important: the collective blind spot. None of these brands are getting any kind of retention-level searches. People are not coming back looking for things like alternatives, pre-owned, gifting, longevity, resale value.

Christopher Penn – 29:28

Which means that either these brands don’t have that content on their websites, which is one possibility, or that audience is somewhere else.

Katie Robbert – 29:37

But.

Christopher Penn – 29:38

Just like if you own a car, or you own any luxury good or any high-expense good, you think about these things, you have conversations about these things, and none of these brands have this in their search data. That’s a big gap. And the fourth was that these brands are mostly brand name. So they have correctly invested a lot in brand. What they’re missing is intent. They’re not getting a lot of search data, from the data we can see, that is intent-based, and that intent-based data is the quality and longevity investor. That kind of goes back to point three. People who want a really well-made watch or pair of shoes or what have you, in this case handbags, they’re not finding these guys.

Katie Robbert – 30:24

I have a good friend who loves going to estate sales, who loves going to vintage markets and finding these high-end brands at secondhand and consignment shops. And these luxury brands do end up in those places, because people do a closet clean-out. The estate sale is a great example of that. And I don’t know how they would necessarily know that their stuff is ending up in those places, but their stuff is ending up in those places, so they should probably acknowledge it. Like, hey, maybe you can score a good find at a vintage market, like a Coach bag from 1972 that’s still in good quality. That’s what people are looking for.

Katie Robbert – 31:17

There are people for whom that’s their whole hobby: seeking out those things and restoring them.

Christopher Penn – 31:25

And that is the word we’re looking for, restoration.

Katie Robbert – 31:28

Yes.

Christopher Penn – 31:28

None of these brands seem to have that, or at least it wasn’t in the first 90,000 keywords we looked at for each of them, and they don’t seem to have a product offering for that. If you think about it, okay, could I use, for example, mink oil or whatever to restore the leather on a purse, and what are the directions for doing that? Or is there a way to do that with the factory? If we go back to F2 and F5 in particular, F5 is the entry-level, entry ladder climber. This is the person who might look at the consignment shop, whatever. They want an entry to the brand.

Christopher Penn – 32:07

If you are the brand and you have a restoration kit, congratulations, you are now going to get that customer into your ecosystem and hopefully upsell them to something, maybe net new.

Katie Robbert – 32:18

That makes a lot of sense, because finding the vintage pieces, that’s a huge market. I can’t say for certain, but I know there are a lot of vintage stores all over New York City, for example, that specialize in carrying these things that they’ve acquired through estate sales and donations and that kind of thing. They exist, and sometimes these things are a little bit scuffed up. I mean, they’re 40, 50 years old, maybe older, and they need that restoration. And you happen to find a really good deal one day, you find a vintage leather jacket from whoever, and you’re like, this is great, I don’t know how to take care of it.

Christopher Penn – 33:05

Yep. And that is a collective blind spot of all four of these merchants. So if we were to see somebody come into our sales CRM from one of these brands, one of John’s first questions would be, well, hey, what’s your content strategy for post-purchase care and loyalty? In this analysis, there’s a bunch of math that goes on, but we look at the keywords. This is all brand, and it makes sense. Brand is literally the thing that these companies have to invest in for status. So if they’re not spending money on brand, they’re kind of doing it wrong. Informational traffic, most brands are giving that a pass. When we look at this, it’s really interesting. Coach and Michael Kors lean toward one buyer type. Louis Vuitton is spread across five.

Christopher Penn – 34:02

So Louis Vuitton’s marketing strategy stretches across the different ICPs. Look at Coach and Michael Kors. They have such a gap here in terms of what traffic they earn on their website today. If I was in charge of making revenue for these brands, F1 is the buyer ICP, because they’ve got money and they’re willing to pay full price. And if I was working at Coach, I’d be like, what are we doing? We have none of this here. We’re not getting any of the exclusivity-based search intent. Why not?

John Wall – 34:38

Yeah, like you said, that’s an easy hit. You put out a $15,000 bag and you make sure that it’s on the red carpet at the Oscars for a couple people to see. It’s a small product line, you don’t have to go hard with that. But yeah, you don’t want to end up trapped in the bargain basement. Well, unless you’re a mill that’s cranking out cheap bags, that’s another story.

Christopher Penn – 35:06

What I find interesting about this in particular is F5, the entry-level thing. Louis Vuitton is arguably the most expensive of these brands, the one that’s more in the actual luxury bracket than the affordable one. And yet from a search perspective, they’re doing better on the entry ladder than Coach and Michael Kors. So it’s like, okay, they clearly have the brand cachet, obviously. But what are they doing with their content that’s so much better than the places that should be getting people into the entry of the luxury market?

Katie Robbert – 35:37

Well, is this search data of people searching for it and finding the answers, or is it just that they’re searching for those things? Because I have thoughts.

Christopher Penn – 35:48

It’s searching for these things across these four different companies.

Katie Robbert – 35:51

That doesn’t mean they’re finding the content, right?

Christopher Penn – 35:56

In the Ahrefs report, each search and its volume has one or more URLs tied to it. So there is a place that it ties to.

Katie Robbert – 36:04

Got it. Okay. I just wanted to be clear on that, because when we talk about search data, sometimes it’s just, what are people literally searching for? Because I know a lot of people search for, is there an affordable version of Louis Vuitton, or is there an affordable version of Gucci? So I just wanted to make sure it wasn’t just that kind of search, but that they’re actually finding content to answer those questions.

Christopher Penn – 36:30

Yep. Now, this is how we used AI for this. How we did it was we took keyword embeddings and turned them into topics, and then we took those same kinds of embeddings from the ICPs and said, which embeddings are closest? Mathematically, which ones are closest? This is something that I would say every brand should do. And Katie, I know the moment we finish, you’re going to say, okay, so you need to do this for us. Because you can imagine for Trust Insights, we have our consulting services, we have our boxed products like AIView, we have the Trust Insights Academy, we have the people who hire us to speak. Four different business lines. We have all of our keyword data, and we have our ideal customer profiles.

Christopher Penn – 37:07

And I bet you probably want to know which ideal customer profiles match to which business line, and are they correct?

Katie Robbert – 37:14

I do. And actually, that was something you told me last week you were going to do, so it was already on your list. But yes. I look at this and I’m like, this is so smart. All jokes aside, why wouldn’t I ask for this kind of information? As the CEO, as someone who’s responsible for growth, I want to know at a deeper level, who are we trying to reach and what do they want, so that we can say, is it the message? Is it the offer? Is it the audience? Where are we getting it right, and where are we getting it wrong? Because I couldn’t say confidently today that I know the answer to that question. But today, I look at what you’ve done both with the ICP and the search data.

Katie Robbert – 38:02

And I’m like, that would tell me a lot more than I know today. So obviously I want that information. It’s something that everybody who’s running a company, or running marketing, or doing whatever it is, everybody should want this kind of information.

Christopher Penn – 38:17

Now, we have an advantage, and every company does. We have our own data, we have our CRM data, so we don’t need to rely just on search words for it. However, from a new business perspective, if John wanted to go out and pitch Coach, for example, John’s written the pitch. This is literally the pitch: hey guys,

Katie Robbert – 38:37

Right.

Christopher Penn – 38:38

You’ve got six ICPs, you’re only serving one of them. Let’s help you close the gap in the other five, because your competition, Louis Vuitton, is doing a great job across all of them.

Katie Robbert – 38:47

And I think that goes back again to where we started: how to get started in AI in the affordable luxury industry. We didn’t say how to get started if you’re in it. It could be if you’re in it, or if you’re trying to get into it, if you’re trying to pitch it. So what you just did works for both scenarios, because this is all publicly available data. We don’t have an in at Coach, we don’t have an in at Louis Vuitton, but their data, through SEO tools and through Reddit and through all these other things, becomes publicly available.

Christopher Penn – 39:24

Exactly. And then this skill goes on and on. We have all these different things. But I like this one in particular, because it tells you a bit about those different segments again: what’s the brand share? How do the different brands stack up in terms of share for these different segments? So Louis Vuitton, the black dots here: the closer you are to the right side of the screen, the better. And these other brands, Coach and Michael Kors, are really on the left-hand side. That’s not good. That black horizontal line at 1x is sort of the traffic signal. Anything below that line, those ICPs are in trouble, and they need more help in those.

Christopher Penn – 40:08

And from a stack rank perspective on that exclusivity, Coach is at the bottom of the barrel. They get no love compared to Michael Kors, which is doing a little bit better, compared to Gucci, and of course compared to Louis Vuitton. So if you were in charge of an ICP as a business line, you would now look at this and go, wow, I’m in charge of the exclusivity buyer for Michael Kors. I’ve got a lot of work to do, because we’re close to parity with Coach, and boy, Louis Vuitton is handing us our butts.

Katie Robbert – 40:43

And I want to be clear: when we talk about content, I think a lot of people default to blog content. We’re talking about any kind of content that is searchable. A lot of these brands use videos, they use influencers, whatever that looks like for them. And obviously we don’t have insight into what their actual data looks like. We just have whatever’s publicly available. But when we think about content, I think a lot of marketers immediately go, oh well, how do I write a blog about this? You don’t have to write a blog about it. Content takes a lot of different forms these days, and a lot more of it is searchable than it ever was before.

Christopher Penn – 41:24

Exactly. And of course this goes on for quite some time. One thing I thought was very interesting is in terms of demand. Again, this is Google Trends data. You can look at brand name interest and see how the brand itself is faring over time. And it’s been real interesting that Coach is doing better. Why? Because the economy took a downturn beginning in 2025, because of what was happening macroeconomically. Coach, the deal-driven brand, is getting more interest because it’s all people can afford. So from a strategic perspective, you have to step back and go, how much does the macroeconomy affect our go-to-market strategy?

Christopher Penn – 42:07

It would be great to go for the exclusivity buyer, but if that ICP is vanishing because those people don’t have money anymore, then is Coach’s strategy of going for just the deal-driven market correct?

Katie Robbert – 42:22

That’s interesting, because, and this is my first time seeing it and digging into it more, but that to me almost feels like it contradicts what you showed earlier, where they’re only going after the one target audience, so they’re missing the boat on all the others. But this says, nope, whatever they’re doing is actually working, because they’ve focused in on the correct single target audience. So they’re focused, versus the other ones, who are more spread out.

Christopher Penn – 42:51

Well, it’s interesting there, because you look at Coach, which is in red, and Michael Kors, which is in yellow. They’re both going for the same deal thing. But like in Highlander, there can be only one. And so it’s Coach. Which means that if you’re Michael Kors, the outlet route is not working for you. The deal route, going after that deal-driven buyer, your brand is diminishing over time, not growing.

Katie Robbert – 43:18

And again, we’re just looking at handbags.

Christopher Penn – 43:21

No, this is looking at the brands themselves, from the brand websites.

Katie Robbert – 43:25

Okay, well, that’s interesting too, because not every brand has the same offerings. And so it depends on what people are looking for. So if you’re saying, I’m looking for a Coach handbag, but I’m looking for Michael Kors sunglasses, and I’m making this up, because I know they all have these things, but maybe Coach doesn’t have sunglasses, so you have to go find Michael Kors sunglasses. This is a good high level. But then I personally would want to dig into each of the individual product lines to see how they meet one for one. Because if Coach has 30 product offerings and Michael Kors has 15, well then, yeah, Coach has a lot more to offer than Michael Kors.

Katie Robbert – 44:13

Or the reverse is true, where they’ve diversified too much, they have too many product offerings. That’s the kind of data I would want to look into. If that was my brand, I’d be like, okay, now I need to know that next level.

Christopher Penn – 44:27

And if I were a retail brand, or if this was one of our clients and they were actually paying us money to do this, which, call us if you want to be, then my next step would be, can I match the ICPs to census data so I can see what the actual market size is? Because if Louis Vuitton’s entry-level price is $10,000, I can pretty easily look at real household disposable income from the Census Bureau and go, okay, that’s like five people that can afford that. So we would validate the ICP against real data for the B2C marketplace to say, okay, what is the size of this market, and how much can they capture? We have one client that does a lot of B2C.

Christopher Penn – 45:07

And we’ve talked about and explored with them things like looking at household income by zip code, to even just say, yes, this zip code is a good place for you to market, because the people who buy your services are here. Don’t spend your money in this zip code; the household income there is not aligned with your ideal customer profile. So, like you said, there’s a lot more data that’s still available, still free in a lot of cases. But we would want to blend it in if this was a paying client and not just a livestream.

Katie Robbert – 45:39

And I keep saying this, but that goes back to where we started. If you’re only looking in your own specific lane, in your own industry, in your own team, you’re missing out on all of the other interesting ways that you can be using generative AI to find answers to questions you didn’t even know you had, or to questions you do have that you didn’t know you could now answer with all of this available data. And if you want to learn more or get one of these reports for yourself, you can go to TrustInsights.ai/contact. Talk to Mr. John Wall, Mr. Affordable Luxury himself. He’s got hair, he exudes luxury.

John Wall – 46:21

That’s it. I’ve got my Birkin bag. I’m ready to go top level, baby.

Katie Robbert – 46:28

I’m like, do I know what that is? I do know what that is. That is an expensive bag.

Christopher Penn – 46:33

But to both your points, it is important that when you’re getting started with AI in any industry, but especially in the affordable luxury industry, you have all this data, and you cannot physically keep it all in mind. The human brain just cannot do that. But AI systems coupled with good data systems can. And as we saw in this report, there’s more to do to bring in census data and all this other data so you can truly understand: this is our market opportunity, this is what you can do. And if you have internal documents, like, this is our go-to-market strategy for 2027, and a lot of people are doing annual planning right now, follow this approach that we’ve outlined in this episode and say, here’s my go-to-market strategy. Here’s all this external data.

Christopher Penn – 47:21

Is our go-to-market strategy out of alignment with reality?

Katie Robbert – 47:28

I know you’re going to ask for final thoughts, and I’m ready.

Christopher Penn – 47:31

Final thoughts, Katie.

Katie Robbert – 47:33

Final thoughts. When we hear organizations saying, our goal for 2027 is AI, we have to do this AI thing, a lot of what you can do with AI is sitting right in front of you. It’s not necessarily, hey, I have to build an agent to answer my emails. It’s, I can use AI to get deeper insights to make better sales. We need to rethink what doing AI means in 2027, because we’re missing the boat. There’s so much good stuff that we could be doing to do better for our organizations, and that isn’t just checking the box on usage.

Christopher Penn – 48:12

John, your final thoughts?

John Wall – 48:14

Oh no, I can’t beat that. I’ve got to double down on that. Don’t just sprinkle AI on stuff, or just go take general courses and hope you figure out where it can go. You’re already sitting on these huge piles of data that for decades you’ve been like, oh my God, I can’t manage all these spreadsheets. Pour it all into the model and let it do some work and tell you where to go, because that’s what this stuff does. It will find the segments that you didn’t even know existed that are actually real opportunities. It will show you weaknesses in the competition. Put this stuff to work, and don’t sprinkle the fairy dust. Get to work.

Christopher Penn – 48:52

And to Katie’s point, not everybody has to use AI. This analysis can be done by one person in your organization, as long as they have access to great data, and then the rest of the organization can get to work operationalizing it. Not everybody needs to be using AI just to use AI.

Katie Robbert – 49:08

Right.

Christopher Penn – 49:08

All you need is some good outputs, and then you can start taking action on those outputs.

Katie Robbert – 49:15

I would personally rather have nobody using AI in their day-to-day and be making more sales because we have better data. That’s the end goal. People just want their companies to make more money. We have to be smarter about what we’re doing.

Christopher Penn – 49:34

Exactly. That’s gonna do it for this week, folks. Thanks for tuning in, and we will catch you all on the next one. Thanks for watching today. Be sure to subscribe to our show wherever you’re watching it. For more resources and to learn more, check out the Trust Insights podcast at TrustInsights.ai/tipodcast and our weekly email newsletter at TrustInsights.ai/newsletter. Got questions about what you saw in today’s episode? Join our free Analytics for Marketers Slack group at TrustInsights.ai/analyticsformarketers. See you next time.

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