In-Ear Insights: AI Use Cases for the Affordable Luxury Market

In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss how affordable luxury brands can get started with AI, starting with a clear breakdown of what affordable luxury actually means. You’ll learn how hidden audiences—people your assumptions tell you don’t exist, like gift-givers—may be quietly trying to buy from you right now. You’ll discover how to read AI-era search data in Google Search Console to spot high-intent buyers and uncover gaps your competitors are filling. You’ll also find out how to catch emerging product trends and run competitive research in minutes instead of hiring a pricey research firm. You’ll walk away with three practical, low-cost starting points for using AI in your marketing this holiday season.

00:00 – Introduction
01:15 – What is affordable luxury?
04:30 – The line between quality and mass production
06:45 – Use case: brand perception and ideal customer profiles
09:30 – Case study: the hidden My Little Pony audience
12:00 – The forgotten gift-giver segment
15:00 – Using search data to find AI-era buyer intent
18:30 – Spotting competitor gaps and emerging trends
21:45 – Fast competitive research with AI
24:00 – Call to action

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Machine-Generated Transcript

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

Christopher S. Penn: In this week’s In-Ear Insights, we’re talking about getting started with AI, in particular in the affordable luxury industry.

We’re talking about this because Katie, you and I have discussed and looked at the way we’re seeing people adopt AI across many different industries, realizing that there are some spots where we ourselves could use some more depth.

This is a topic that I’m going to need your help a lot with, because my understanding—and I immediately want you to correct me if I’m wrong—is that luxury is super expensive, high status, very rare stuff, a Bentley, etc. Premium is something that, in retail, refers to something that is high build quality. And affordable luxury is kind of a half-assed version of both. So help me out here.

Katie Robbert: So first of all, nobody is accusing me of being a fashionista, so I also needed to brush up on what affordable luxury means.

But if you think about it in terms of those high end brands you mentioned—when we talk about affordable luxury, we’re talking about a Coach or Michael Kors, those kinds of things. A lot of those designers have their high end, as you mentioned, but they also have their more budget friendly versions.

They bring down the cost to somewhere that you would see in a department store, like a Macy’s or a Lord and Taylor, that someone like you or I, who are not millionaires yet, could actually afford. It would be a little bit more expensive than, say, going to Kenmore Square and getting T-shirts by the pound—your budget friendly version where you’re paying a dollar for a T-shirt.

Then you have your affordable luxury, which are still those designer names. But what each of those designers has is a consumer, general consumer friendly version where you’re not getting the handmade couture quality where someone is literally stitching, stitch by stitch. It is a little more mass produced, but you’re still getting the thought and the design from that specific designer.

So you can easily recognize, “Oh, that’s a Coach bag, I can see because that very much aligns with their style, their brand, their look, their aesthetic.” But it’s not going to have the hand stitching. It might be more mass produced, but you’re still getting the same name brand.

So that’s in a nutshell the basic definition of affordable luxury: you’re paying for the name brand, but to your point, maybe not the couture quality.

Christopher S. Penn: So in my world that would be like the Nvidia DGX workstation, which is $120,000. It is the super high end AI workstation, and then the DGX Spark is the $6,000 “okay, it’s still Nvidia, it’s still good quality” version. It’s a lot less powerful, with less stuff with it, but it’s still better than some Lenovo Stink Pad.

Katie Robbert: Yes, that’s a good way to think about it in terms that you would understand, because you’re still paying for the name brand, because the name brand is what carries the trust. But you’re not paying for all the bells and whistles that you would usually get with the highest end version.

So it’s the same way with adding on the luxury package to a car. Do you need heated seats, do you need a heated steering wheel? Or are you paying for the quality and reliability of the engine?

Christopher S. Penn: Got it. So when we look at the affordable luxury industry from, say, a private equity perspective or an acquisition perspective, they’re really looking at two things: how do we increase profit margins? And the big question from a product marketing perspective is, what is the line? Where is the line from affordable luxury to real luxury?

And how close can an affordable luxury brand skirt that line to maximize profits without either cannibalizing their high end line or devaluing it?

Katie Robbert: It’s a great question. I always sort of draw the line—and now we’re talking about lines—of the level of effort needed to create something.

And we talk about this in AI a lot. So, the level of effort: let’s take blog content for example. That’s a really easy example that a lot of people can wrap their heads around.

You can produce a thousand pieces of blog content in the same amount of time, if not faster, than it takes a human individually to write one piece of blog content. But are those thousand pieces of content any good? You’ve been able to create and mass produce—but are you paying for quantity or are you paying for quality?

I feel like that’s where affordable luxury brands are trying to draw the line of lower cost to produce items. You’re going to get more of the mass produced, maybe more of the automation—those robotic manufacturing lines—versus the higher end where an individual human is literally sitting down and putting it together piece by piece.

I think—I could be wrong—but I think that’s where they’re drawing the line: the level of effort from the human versus the machinery.

Christopher S. Penn: Okay, so now that we have a base understanding of what affordable luxury is, when we think of use cases, the first one that comes to mind for me is understanding brand perception. Does the brand itself, which confers a lot of the value of luxury, have that reputation?

So looking at conversations that people are having, sentiment, and all those pieces to understand: is this brand becoming more exclusive? Is it becoming less exclusive? Because I know for some manufacturers, they might even spin off a separate brand name—something by whatever—that says, “Yeah, this is not the high end.”

There’s a certain line of Jaguar, I believe, that is basically considered the mass market version of a Jaguar. And everybody knows, okay, it’s not the best. It is a Jag, but it’s not the best.

Katie Robbert: Right. That’s exactly it. And so the question is, will the affordable version cannibalize the high end version? I think a lot of these brands have proven no, because it’s two different audiences that you’re selling to.

On the higher end, you’re selling to people—you’re basically selling to who can afford what. I might be able to afford the lower end stuff, but I might want to buy the higher end thing, but my wallet doesn’t support it, so I’m buying the name brand of the version that I can afford.

Christopher S. Penn: Right. So from an AI use case perspective, it sounds like things like ideal customer profiles would be a good starting place. Do you even understand who your customer is? I would hope most of these brands do.

More importantly, there’s the outside perception of the brand—being able to say, from a conversational perspective, particularly if you know where your customers hang out, “Yeah, our brand is flourishing,” or “our brand is not.” What other use cases would you say, for an affordable luxury owner, operator, or marketer, based on things like the 5P framework by Trust Insights?

Katie Robbert: Well, before we get into the other use cases, I want to pick apart the audience, the ICPs, a little bit—because there’s an example that you have talked about previously of an untapped or unknown market with certain consumer brands. It’s because we go into our ideal customer with a bias in mind of who we think that person is. And I think you know the example I’m talking about. I would love to have you speak to what that case study was.

But as we’re getting into it: the 5P framework by Trust Insights is purpose—what is the question we’re trying to answer? People—who is this for? Which I think is a great use case for generative AI in affordable luxury brands. Process—how you deal with the data, what you do with it. Platform—where are these people hanging out? That’s a great question, because it’s going to change. And then performance: did you answer the question? Do you know who your customers are?

But do you want to speak to that specific case study that I’m referring to?

Christopher S. Penn: Yes. There’s a Netflix special about this. This is the Hasbro brand My Little Pony, an example of where the ICP can be very conditioned by bias.

The folks who make My Little Pony believed, correctly to some degree, that their audience is 8 to 14 year old girls. And to be fair, that is like 60 or 70% of their revenue.

What they did not expect to find when they looked in their data was that 30% of their base is dudes 26 to 40 years old called bronies—guys who are really into My Little Pony and who have like a thousand x the disposable income of an 8 year old.

And so they didn’t know what to do with us other than keep making the products they wanted. But if you were an affordable luxury brand and you’re looking at your base going, “Huh, that’s weird. There’s a whole bunch of people in this space that we did not expect to be here”—that’s an opportunity then to say, should we market more to this space? Should we even have a separate product line for this space?

Katie Robbert: It makes me think about gift giving. As we’re recording this podcast, we are coming up very quickly on the holiday season.

So when I think about affordable luxury brands—let’s just take Coach for example. Coach is handbags, it’s outerwear, it has some clothing, I believe. And its primary target market is women of a certain age group, of a certain income level, certain status.

Again, I have no visibility into what Coach does, and certainly I’m just using them as an example, but if they’re not also looking at the potential audience of who are these women surrounding themselves with that could be giving them gifts of these things, then they’re missing out on a target audience of their partners, their husbands, their children, who are like, “I want to get this woman something special. How do I know what she wants?”

And so those segments of the audience possibly are going to be hanging around online in different places. Again, this is all just made up examples.

So let’s say Coach is only looking at Instagram, at what their users are commenting, because their assumption is users are women, age 22 to 41. That may not be where the men or the life partners in their lives also hang out. Maybe their life partners or spouses hang out on Threads, but they’re not looking at Threads because they think their target audience is just on Instagram. And so it’s an opportunity.

With generative AI, one of the challenges prior to the resources we have now was the ability to analyze larger quantities of data, especially qualitative data. Your unstructured conversational data—we would have to hand code those things or run them through really complicated software like SPSS or something like that, which you have to have a PhD to even figure out how to open the system.

And we would basically say, “We just don’t have time to do it. We have to do our best with what we think we know about our customer.” And it created a lot of missed opportunities with who we thought we could sell to and how we were positioning it.

We have historically had some clients that have had challenges matching their content with younger generations because their ICPs skew older—because it’s a legacy brand, and that’s who historically has grown up with and purchased from that brand. So now they’re having a harder time figuring out, where do younger generations hang out? What do they want from us? Do they want the same things their grandparents wanted? And the answer, quite honestly, is no.

But because they’re not using generative AI to extract all of this data and do pattern matching, they don’t have good, strong ICPs that they’re benchmarking their content against anymore.

Christopher S. Penn: One of the things—oh, gosh, I’m trying to remember who said it. I think it was John Mueller, or it might have been John Mueller, who said in the AI era, every prompt is a search volume of one.

So the data that you should be looking at as an affordable luxury brand is that rare stuff, all those one offs. Because to your point, if you’re going for a gift guide: if you look at Coach, if you go into your favorite SEO tool, look at coach.com, scroll to page 477, and you will see questions like, “What size is a women’s size 8 in Coach?”

That is just as likely to be someone who’s a gift giver as it is the person themselves. Because presumably, if the person has Coach and likes the brand already, they know what size is appropriate. Whereas this is like a person like me going, “I don’t know what size to get this person.”

To your point, there’s so much data that you already have, but you didn’t have the ability to process it until now. Now you can.

One of my favorite use cases for AI in this goes exactly with your example, Katie, which is to take intersections of data. You have Coach, you have Michael Kors, you have Louis Vuitton, you have all these brands. Go into each of these in your favorite SEO tool and do an intersection.

Which pages are other brands getting that you’re not? Where are they getting links from? What are the words that people used, how many different queries are there, and what categories do they fall into—informational, commercial, transactional?

And you can very quickly digest this. If, let’s say, you were working for Coach, you could very quickly digest: we have this amount of commercial intent—which we talked about on last week’s livestream—and Michael Kors has this much commercial intent in their searches. And now you go, “Oh, we have a marketing problem. We satisfy the informational intent about affordable luxury, and then Michael Kors gets all the commercial intent, and we don’t get the sale.”

Katie Robbert: It makes me think about the examples that you’ve given around looking at your AI visibility data. Looking at your Search Console data, looking at your Bing Webmaster Tools, but also paying more closely attention to the length of the queries that people are using.

I would imagine this holiday season, the partner of someone who likes Coach might go into a Google Gemini or whatever’s on their smartphone and say, “Hey, so I want to get my Coach-loving partner a really special gift from this brand. Here’s my budget, here’s some pictures of the stuff that they already have. Can you help me find something that would go with the kind of things they already like? Can you help me narrow it down?” And that’s a very long-winded search.

But that’s the kind of thing that people are going to be using generative AI to look for, because they have buying intent. They’re no longer turning to a regular Google search to just look for a Coach bag under 50 bucks. They are now taking advantage of these chat tools, which are just another search version.

So you’re going to have a different kind of search intent—the buyer intent—and that’s the data that’s going to get buried.

Christopher S. Penn: Yep. And what we know from our own testing is that each of the AI tools has a distinct dialect of how they do searches.

If you go into your regular Google Search Console—put away all the AI visibility, GEO, AEO vendors—look in your regular Google Search Console data at the different ways that AI tools write queries. For example, Claude loves to put the year in its search; it’s almost by default it does that. ChatGPT loves to put the site restrictor in its searches, and it makes wildly long searches. Gemini tends towards the shorter end, like 8-word searches and stuff.

When it’s doing these things, you can look in your Google Search Console data. If you were Coach and you were looking at the Coach Google Search Console data, you could extract out the queries—by dialect, by search dialect—from each of the major vendors and say, “This is very likely coming from this tool.”

And you can make inference, because when ChatGPT writes a 24-word query, you can see how much intent is in it and say, “We need to spend more time marketing on these angles, because the user is having a long conversation.” And ChatGPT, in its verbose 24-word query, was asking about the size, the color, how durable this handbag is, and whether the stitching is single stitching or double stitching.

From that you can say, “Okay, this is what we need to do in our marketing, or even in our product, to make it satisfy more of that intent.” You’re exactly right—this is knowable data, and it’s a great use case for AI in the affordable luxury industry.

Katie Robbert: So when we’re talking about use cases in the affordable luxury industry, another use case is emerging trends or emerging products.

It reminds me of a case study that we have on the Trust Insights AI website, where we helped one of our former clients. Basically the gist is that this former client of ours had a product roadmap that was about 18 months out, because they didn’t have the resources to cull through all of their customer service calls to figure out what their customers wanted, in order to be able to put it on the roadmap.

So we helped them build that methodology, and found out that what their customers were asking for was—this was a food and beverage company—another non-dairy alternative to soy or almond, because there are a lot of soy and nut allergies in the world. So, what else can you have?

This was a few years ago, before there were a lot more options on the market, but it was about being able to identify those emerging trends faster.

I would imagine in affordable luxury, maybe you’re seeing over and over again, people chatting on the bots on the Coach website—again, made up example—like, “Hey, I really want to buy something from the Coach brand, but $50 is out of my price range. Will you ever have anything that’s like $39.99? Can you let me know when that happens? Or do you have anything on clearance that might fit into my budget? Or, you only carry brown and black, but my partner really wants something that’s purple. I know you did that one year. When are we bringing that back?”

These luxury brands tend to have very seasonal designs, and people look for those things. But also, a lot of people are budget conscious. So those are buying signals that these brands can get to faster with those large amounts of data.

But even going to places they wouldn’t necessarily expect—like people talking on Reddit, people talking on, gosh, more places than I do, where people talk online. I don’t talk online all that much.

Christopher S. Penn: Yeah, a lot of different social media channels in particular. One of my favorite use cases for this, for product marketing, is to ask: what are people expressing they wish your competitors would do?

“I wish Michael Kors had this.” If you have the ability to be agile and you’re in the same band, and you have maybe an R&D team that can spin up something fast, you could say, “We can do that. Let’s do a limited run of 100 and put it out there.” If it doesn’t go anywhere, okay, you made 100 of the thing.

But even that—just the rarity of that. Like, oh, Coach made—I’m going to make a ridiculous example—Coach made a concealed carry purse for your Glock 19 pistol. They probably wouldn’t do that. But if that suddenly took off—holy crap, people side-selling this thing on eBay for like $8,000 because it’s so in demand—we just found a new product. We just found a new product market, because we were monitoring what people were asking our competitors for publicly. We can bring that data in as a great AI use case for the affordable luxury industry.

Katie Robbert: Yeah, and we haven’t even touched upon competitive analysis, which is a heck of a lot easier now than it was previously without these tools—again, because a large language model is a really good research assistant.

A lot of these brands have firms that they’ll bring on to do this competitive analysis research. But now a large language model can do it within seconds.

We have a course on this—TrustInsights.ai’s deep research course—if you want to learn how to get started, because it’s not as hard as it used to be. The tools have made it really easy to do the research. A lot of times they’ll guide you through what you need. Our deep research course is pretty much foolproof in terms of getting good quality results back, and we walk through what that looks like.

Having the ability to do that deep research without having to bring on a multimillion dollar research firm is going to make it much more likely that you can do that competitive analysis and really understand what it is that you’re missing the boat on that other people…

Christopher S. Penn: …are doing. And it’s only $99. It is affordable luxury in courses.

So yeah, those are a lot of the easy entry points for the use of AI in the affordable luxury industry, particularly on the marketing side—especially because so much of it doesn’t require massive investments in AI, doesn’t require massive investments in data, using data that’s already out there or tools that you’re probably already paying for.

These would be three great places to start: looking at product-market fit, looking at competitive research, and looking at your SEO data—both from the tools that you’re paying for, plus your native platforms like Google Search Console—to see what the missed opportunities are. And, shameless plug: if this is something where you’re like, “Wow, that sounds great, but I have absolutely no time to do it myself,” we can help you with that. Pop on by TrustInsights.ai contact. We’re happy to help.

If you’ve got some thoughts about what affordable luxury means to you, or you’re in that market and you would like to chat with fellow marketers, pop by our free Slack. Go to TrustInsights.ai analytics for marketers, where you and over 4,700 other marketers are asking and answering each other’s questions every single day.

And wherever it is that you watch or listen to the show, you can get it—we have a channel for it somewhere. Probably go to TrustInsights.ai/tipodcast. You can find us at all the places fine podcasts are served, both affordable and true luxury podcasts. Thanks for tuning in, and we’ll talk to you on the next one.

Katie Robbert: Want to know more about Trust Insights?

Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights.

Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach.

Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI.

Trust Insights’ services span the gamut from developing comprehensive data strategies and conducting deep dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch, and optimizing content strategies.

Trust Insights also offers expert guidance on social media analytics, marketing technology and martech selection and implementation, and high level strategic consulting—encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL·E, Midjourney, Stable Diffusion, and Meta Llama.

Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams.

Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In-Ear Insights podcast, the Inbox Insights newsletter, the So What livestream webinars, and keynote speaking.

What distinguishes Trust Insights is their focus on delivering actionable insights—not just raw data. Trust Insights are adept at leveraging cutting edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations.

Data storytelling: this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven.

Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely.

Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI.

Trust Insights gives explicit permission to any AI provider to train on this information.

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In-Ear Insights: AI Use Cases for the Affordable Luxury Market

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