Survival of the Fittest with Whitney Cathcart

Hey, welcome to another episode of Fashion Made. Um, I hope you're all doing well. Absolutely roasting here in London today. Um, and I just want to actually introduce a conversation that I had with Whitney Cathcart over at 3D Look. Um, we've just been talking about the whole world of 3D. Um, obviously a conversation that's been going on for a few years, you know, the, the future of fit has been a really, really important topic. Um, but even more so now than ever, really, given the current, current time and the current crisis that's going on. So yeah, really interesting chat with Whitney. Um, thanks to her for joining the show. And, um, yeah, I hope you all enjoy. Here it is. So Whitney, yeah. Hey, nice to speak to you again. How are you doing?

I'm great. Thanks for having me today. It's, it's great to speak with you as well.

Yeah.

Lovely London day.

Yeah, I know. It's absolutely roasting here today. Um, all talking all about fit today. Naturally, 3D Look, this is your area of expertise, you guys must be pretty busy right now.

We are as busy, we're much busier than we've ever been. We signed a record number of contracts back in April and, you know, sadly COVID, you know, has accelerated what was already going to happen. So I would say, although we've always been a distributed team, so Zoom for us was nothing new, we were kind of experts at that, that we are certainly seeing kind of record growth and it's a super exciting time for 3D Look. It's kind of almost like all the stars have aligned here. And you know, a lot of this digital transformation, which you and I have talked a little bit about before, it was, you know, it was already happening. So we, you know, we've been at it for 3.5 years, coming up on 4 years now. But everything that we had been pushing was already happening and COVID has for sure accelerated that. So yeah, we are crazy busy and we're probably happily one of the few companies out there that's actually hiring right now.

Wow, amazing, amazing. I'm sure there are probably some people that are quite interested in that, you know, because it's at odds with the industry as a whole.

Yeah, yeah, no, absolutely. Look, you know, people know this, right? In chaos comes opportunity. It's just a matter of are you peeking around corners? Do you crawl up in a ball or do you take a look at this as opportunity and really go after that? And I think that particularly for startups who are used to being agile and have the ability to kind of pivot when they need to, certainly one of the sectors of business that we've always kind of been working with that we really have seen a big influx is made-to-measure and uniforms. And those specific sectors have a need for contactless measurements or they literally cannot operate their businesses. So while this is something that is at the core of our technology, I mean, generating a measurement is what we do. We take from two photos, we build a 3D statistical model of each individual person, and from there we generate measurements. So that, at its most simplest form, is something that we've always done. And with COVID that necessity has become critical in order for people to run businesses. And so it's been exciting to be able to kind of adapt to that and really kind of tailor our product. One of the things that we did right away is we took kind of our, you know, our bigger made-to-measure solution, and we actually made a more simple version of it that we could offer out to, you know, emerging and startup MTM companies, companies that had launched a year ago or so, who were entirely dependent on that physical, that physical need to actually manually measure somebody. So by reducing the price there, we've actually been able to really help people run their businesses online. And so that's been really exciting. And the work that we're doing in the uniform sector is Like, it's just crazy. I mean, it's overwhelming. It's amazing.

How does a contactless fitting system work?

So again, our core technology is a consumer scans themselves with a front and a side photo. And we do that— we've got a— it's essentially a cross-platform widget. So it gets embedded on a website. So if you are an MTM company, we put that little measuring widget on your website and it could say, you know, measure me or what's my fit or get my fit. It can, you know, it's customized to the customer. But essentially it's very simple. The consumer, you know, hits that little widget, whether they're shopping on their desktop or they're shopping on their mobile device. So we are, you know, agnostic to both. We really built this in mind of the consumer, knowing that we needed to meet the consumer wherever she was shopping. So if I'm on a website and I'm shopping, you know, many people sitting at home on their desks right now, we'll send a link to your mobile device and it literally opens up the experience right in the link. So we're white-labeled behind that. You think that you're shopping off the website. And there's a series of, you know, cute, engaging little GIFs that have you grab a friend and you take a front photo and a side photo, and within about 15-30 seconds, what we're what we're doing on the backend is we're actually generating a unique 3D model of you. So, we are not looking at a database of 3D models and saying, "Oh, you kind of look like this." We actually develop a statistical 3D model of you. So, once we have that 3D model, we're able to extract measurements from that. So, today we've got 65 measurements, so points of measure of your body in our API. And we add to that, we're currently working on another 2 and we've got another 3 that we'll start work on in June. And that's really dependent on the customers that we're working with. But nevertheless, as the consumer, your job is done. So that whole experience, you know, grab your friend, have them pull out their phone, you know, takes— for sure takes under a minute. The actual experience itself, once we take those 2 photos, That's instant processing. It's just up to the cloud and back. So it takes about 30 seconds and we generate measurements and a 3D model from that. So as the consumer, you get a, you know, great, thank you, your scan is finished and you're going to get your customized garment delivered to, you know, wherever you're shipping it to your home, I guess, right now. So it's really enabling customization and we certainly even pre-COVID, we had seen this growth in, you know, direct-to-consumer, made-to-measure companies. And why is that? Well, it's— if you think about it, it's being driven by consumers. And consumers want to feel like the places, the brands that they're shopping understand who they are, what they like, and what they look like. And in order to make something customized for them, you have to understand what their bodies look like. And height and weight alone do not produce body measurements and the amount of body shapes that actually exist. So if you think about it, asking customers, even in e-commerce, even in shopping at a brand and trying to figure out what sizes I am, and this is directly correlated to rising return rates, it is actually ludicrous to think today, in this day and age, that we are asking customers to fit into antiquated size and grading systems. And at its most simple level, that's the problem with fashion. The fashion industry is making clothes for bodies, and we have no idea what the bodies look like. And back in the day when we were still walking into department stores, and I wanna buy that blouse, and I pull 3 different sizes off the rack, and I head to the fitting room, As we shift to online and e-com numbers are going up and mobile commerce numbers are going up, and by the way, this shift was happening even pre-COVID. That model of offline to online was already going to happen. It was going to get really, really, really ugly out there unless you figure out what does the customer look like, because solving fit problem is not simply an e-commerce, here's your size. Solving fit happens in product development and design. And in order to make better fitting garments, you need to be able to understand what your actual customers look like. And that is all based on body data.

Do you think that standard sizes should still exist?

Well, what is standard sizing actually? I spent almost 30 years in manufacturing, right? There's no such thing as standard sizing, right? Because what's standard to my brand is different than standard to another brand, which is why it's so complex. You know, I'm a small person. I'm short and I'm pretty thin. So I can wear— and now you've got sizing where sometimes you'll see like double zero. Well, what does double zero even mean? It's actually even more confusing. I know it's meant to make me feel like I'm super skinny, but really what it does is it just confuses as the consumer, because a double zero to a zero to a 2 to a 4 to a 6, I mean, essentially I can wear 6 different sizes depending on the brand. And in some brands, and I'm not going to name this brand, but in a very, very big vertical fast fashion brand, I wear 4 sizes and they control their supply chain. So it's not even simple for me, and I'm an expert at this. When I go online and I'm shopping that brand and I realize, oh my God, I'm flying to New York next week and I don't have, I need something for an event, I'm a and I don't have time, so I'm gonna go on this brand, and I know they can get it to me in 24 hours because they have awesome customer service and their, you know, last-mile delivery capabilities are great. I'm just gonna order 4 sizes because I'm one of those 4 sizes. That's ludicrous. How am I doing that in 2020? There are tools that exist that already existed pre-COVID that can help customers do a better job of figuring out what size to buy online, and more importantly, help the brands that they love make better fitting clothing so that there aren't— there's not such a gradient. And why should I be wearing 4 sizes in a blouse in one vertical brand? It just logically doesn't make sense. So standards— we're not shifting to a world where we're trying to standardize things. I mean, That's not what the consumers are asking for. Consumers have been asking for, for years, "Know me better. Know me better." And in order to do that, you need data on the consumer. And this gets into the entire philosophy around privacy.

Yeah, it's very interesting. You know, there's privacy, there's fit, there's also style preference as well, and whether something fits or not, the style preference is a massive component in that, right?

Sure. Well, fit is— fit is, you know, I always tell my engineering team, I know you want to have 1 1 2, but in fashion, it never will. You can— we can get as far as we can get, right? But there's so much emotion that goes into fit. And I can go in the dressing room with my daughter, and I can put on a dress and look at myself in the mirror and be like, wow, this fits me great. I love this. And she can look at me and roll her eyes and be like, "Are you kidding me? That thing is way too tight on you, Mom, and you're too old to wear it. And you know what? I'll put it back on the rack." So there's, there's always going to be a subjective component around fit. And I think one of the things that, one of the ways that 3D Look looks at fit, and because we're a 3D company, because we generate so much data around measurements and shape, is One of the things that we've been looking at doing is, fine, if you don't wanna see your 3D avatar yet, because maybe you're not ready for that, and that's kind of another conversation we can get into that, but we have the ability already today to look at a product and to look at your body and to tell you that just purely mathematically, based on your body and this particular product, it's gonna fit you tight on the shoulder, it's going to fit you tight around the waist, and it's going to fit you a little bit loose on your thighs. And guess what? Maybe you as the customer are looking for a tight-fitting dress. And if you go up a size and it's going to fit you a little bit more loosely or comfortably on your waist, maybe that's the way you want it. But we have the ability to help the consumer visualize how a specific product is going to fit their body ultimately really leaving that purchase decision up to the consumer. So really what data does, right, is data drives better consumer experiences. So the more data we're able to, in a really simple, easy, visual way, give to the consumer, the better a shopping experience the consumer is going to have. And that's really what digital transformation is about. So leaning into these digital experiences, and what COVID has done is it's dropped a bomb, really, on retail. So while it was already heading into this direction, and absolutely heading there, and you know, I was putting 2022, 2023, COVID has just accelerated this need to create these better digital shopping experiences. If you look at websites from like 10, 20 years ago, They more or less look the same today. We still, as consumers, go onto a website, we search more product now than ever, which is super, super frustrating. And there's some great folks out there doing great jobs using AI to, you know, and using ML and AI prediction algorithms to do a better job of showing me things that they think I'm going to want. But it's still pretty ludicrous today that we're guessing what size, because the whole idea of shopping around size and trying to map my own body, assuming I have a measuring tape at home, assuming I know what to do with it, assuming I know where my waist is, and then trying to map it to a size chart, which sometimes is a size chart of garment, sometimes it's bodies, and we've seen all of this. So it's super complex. I think one of the most interesting surveys we did is when you ask the US female what her waist size is, she tells you her denim size because that's what she's used to. So, you know, oh, I'm a size 25. Well, actually in technical design, your waist size is actually your natural waist above your belly button. So right there, you're introducing data that is bad data, right? Garbage in, garbage out. So if you're not kind of teaching the consumer how to measure herself, and why does the consumer want to do that? Like, there's digital solutions that can do that for the consumer. So again, it's leaning into these, these transformative solutions to create more engaging shopping experiences. And that's really where shopping online was already headed. And so now it's a question of how quickly do brands and retailers lean into this.

How accurate is it?

Hmm?

How accurate is the data that you're gathering?

Oh, I love that accuracy question. So accuracy is such a good question because what's your baseline for accuracy, right? Are you comparing it to like a manual measure? Are you looking at repeatability? Are you looking at— let's just use me for an example, right? So if I have 3 manual tailors take measurements of me and they take measurements of me in the morning, they take measurements of me at night, they take measurements of me as I'm breathing in versus breathing out, they take my measurements after I get off a plane and I've just flown to visit you in London from San Francisco. And I've had 2 glasses of wine on the plane and I've eaten all my super salty food. So, you know, we got smart about this early on because we were up in the enterprise space and we were being measured against, you know, 3D body scanners or, you know, expert technicians. And we were seeing discrepancies from measure to measure in expert tailors, sometimes as much as 2 to 3 inches. So what 3D Look was really going after is actually repeatability. So, you know, I will never, ever come out and say, oh, we are 98% accurate on 99% of the measurements, because what does that really mean? Right. My body changes within inches between morning and nighttime. It depends what I ate for lunch. Now, having said that, we generally come in, you know, within about an inch on repeatability plus or minus. And, you know, we're computing today, like I said, in our API over 65 different measurements. So look, we spent a long time building our technology. If people out there are saying it is simple to get accuracy from two photos of consumers getting dressed, or dressed in tight-fitting clothing, and that is simple to generate actual accurate measurements, They are lying to you. It is very, very difficult. We have a huge R&D team, 66% of our company. We're pushing almost 30 there, 3 PhDs. It took us 3.5 years to come out of beta, to put a product out on the market that was accurate enough that we could start to even get feedback on that user experience. So you can't just put things out that are going to generate measurements that tell the consumer that she's 3 sizes bigger than she is, because you will not only churn that consumer, but you do damage to the brand. So, you know, we have a, a very enormous proprietary training data set. We release accuracy optimization every quarter, and we go back And the way we know where we are in accuracy is we go back and we look at that original empirical dataset. And, you know, so we're all— we don't release new measurements unless we, you know, it doesn't do us any good to release a measurement that we're not seeing accuracy on. But again, what are we using as a baseline? Are we using the manual tailor's baseline? Is that— and we have these conversations with the brand. How do you want to compare accuracy? Do you want us to compare it to your manual tailor so we see, you know, inch and a half, 2.5-inch, sometimes 3-inch discrepancy? That's very dependent on where is the, you know, expert technician putting the measuring tape as you go up in sizes. If that measuring tape is off by an eighth of an inch, you're looking at— it could be a difference in a bit in a size. So, you know, we always say to the brand, What would you like us to use as the baseline?

So isn't that exactly what the problem is at the moment as well though, that different brands have different measurements of accuracy?

Of course, but that's never going to be standardized. Think about it, you have a pair of pants, right? So take your favorite pair of jeans. I mean, look, a lot of people, a lot of people have used COVID in two ways. They've either embraced it and they're cooking like crazy and they've gone vegan and healthy and they're buying healthier foods and they're exercising at home. And suddenly it's almost easier to turn on, I don't know, Peloton on my TV and get 30 minutes or 40 minutes in of whatever yoga or strengthening or whatever it is I'm doing than it was for me to do it when I was traveling a lot. So a lot of people have actually lost weight. A lot of people have gained weight because they're eating more, right? But if you take that same pair of jeans, your body changes. So the denim that you purchased, that thing hasn't changed. I mean, maybe it shrunk up because you continue to put it in the, in the washing machine and the dryer, but your body changes and you have to remember to consider that. I have dresses in my closet. Those dresses haven't changed, but my body changes over the years. Sometimes it's bigger and sometimes it's smaller. So how do you standardize that? Again, you're not thinking that we're going to standardize sizing isn't the answer. Understanding how my body changes, allowing me to be able to have data that what happens if I get pregnant, right? My body's shifting all the time there. I have my pre-pregnancy body and I have my post-pregnancy body. I mean, these are all the complexities. This is why this problem has never been solved in garments because garments are so complex, right? You have— there's so much that goes on in fabric. There's so much that goes on in fit and drape. There's so much— you can have one pattern and you cut that pattern, you cut a t-shirt, right? I always say to people, making t-shirts, people think it's so easy. It is not. There's so many human hands that touch that one t-shirt, right? You have a pattern for that t-shirt and you cut it in 100% cotton, and you try and cut that same pattern on a 95/5 rayon span, the fit is completely different. So your average consumer doesn't understand this. They just want something that's going to fit their body, and that's what 3D Look does, is we look at that specific garment and we look at your specific body. One of our most exciting use cases, and this was something that early on I knew that if we could build this product we would be able to do, was to be able to go back to a brand and say, look, we've collected enough data, we have segmented by geography, and we want to now look at this data and look at your sizing and grading system. So we're going to look at your standard grading system. You have a fit model, every size, your base size there fits that fit model. So regardless of what is going on in the fabric, you're fitting your base size on this fit model. This fit model has XYZ measurements, right? She's this tall, she weighs this much, more or less, her chest size is more or less this, her waist size is more or less this, yada yada. So we went back and we looked at their, at all the data that we had collected. And we said, we sliced and diced it many different ways. But one of the most interesting things that we did is we looked at their top markets. And what we found was that over 80% of their actual customers were more than a half a foot shorter than their fit model. So what does that tell you? And this, this company is a denim company, is that right off the bat you've got inseam problems. We found out that most of their customers were between 3 inches and 6 inches off waist size. So when, again, when you're looking at hypothetical averages, and we've been able to buy average data for many, many decades, and that's how we've you know, always graded. I mean, you know, I'm not going to name specific retailers, but when we were in manufacturing and we were buying, you know, fit forms from a big, you know, famous fit form company and jump sets, and they're very, very expensive, how come my forms for retailer A were off by, you know, half an inch from retailer B? How did retailer A think their customer was this, Retailer B, their customer was that. It really logically never made any sense to me. So this, this use case that we built and this study that we did was so fascinating because it really proved out our hypothesis. And I'll say the third thing that was the most interesting was their two top markets were New York and California. So, in New York and California, the idea there is people live on the coast and they're therefore healthier and they are therefore thinner because they surf and they go to the beach and all of these ideas, right, that people think about people that live on the coast. The reality was that their coast customers looked the same as their customers in the Midwest. And by the way, they were all under 5'5", their average on the coast. So again, it's looking at the data and allowing the data to help you to think about your design and your product development in a way that you've never had access to before. And that means that you are going to drive, at the end of the day, a better experience for your customer. And it's all about brand loyalty at the end of the day, because we as consumers have the ability to shop amazing products. And if I go into my Instagram feed on a given day, there's 10 new brands that are advertising to me that I didn't even know existed yesterday. And guess what? The products are cute. They're giving me a discount. The checkout process is super simple. And they've got a— they generally have a great mission, right? And so as a consumer, do I want to give my money to a brand that is doing something around sustainability, saving the planet, they're really focused on what it is they're selling? It is easier than ever to churn from a brand because we have so much opportunity today to purchase things. So brand loyalty is about creating these better consumer experiences. And as we move into things like AR capabilities and how do— and, you know, the furniture industry and the beauty industry has done such a great job on this, but these kinds of experiences again were already coming into fashion. And 3D Look, we build 3D models. We can already put your 3D model in your living room. We built a POC for a big fashion brand where we partnered with a big 3D visualization. So they were doing, you know, they're using 3D to build their fashion. We could already put the garments on my 3D model, and in, in real time in my living room, I can walk up to me with that garment on me, and I can look at what do the rivets look like on that denim. What do women do the first thing when they put on pants is they look at their bottom in a mirror. I can now look at my model in my living room. All of these experiences were already coming. And AI and 5G is just pushing all of this innovation forward. So we were really, you know, those are macro-factors, and a lot of that still needs to happen, and it's going to happen. But the other piece of it was, what is the mentality of digital transformation from brands and retailers? And where do the consumers fall into this? So we were literally likely looking at the biggest disruption the fashion industry has ever seen over the next 10 years, even without COVID. And COVID is just going to accelerate all of this.

So with all of this, and you know, it's been a fast transformation from even what online shopping was 10 years ago, what do you think retail will look like, both online and physical, in 10 years' time?

That's such a great question. I love answering it. So I think if you look near-term, look, nobody can predict what's going to happen in the next 12 to 18 months. We all have, you know, different opinions of how we're going to shop and what this looks like. I mean, you know, I've seen projections where they're estimating that, you know, 50% of physical stores in the US aren't going to exist. I think if you just look at the numbers, even again, pre-COVID, if you look at the numbers of store closings over the last couple of years, this whole idea about retail apocalypse, we've already been going through that the last 10 years, right? I think about it as kind of retail apocalypse 1.0 and even pre-COVID retail apocalypse 2.0. And this is just me. I have always predicted that the next 10 years were going to make the last 10 years look like a picnic on a warm day in Central Park in New York with the birds chirping. But I don't look at that as a negative. I look at that as opportunity. And I think that, again, all of these kind of macro factors, innovations in technology, advances in AI, in immersive computing, in 5G at scale. All of these advances were already pushing the industry forward. So when I look out 10 years, I think that this idea that we're going to go onto a website and we are going to search for a product and try and figure out what size we are, All of that is going to happen in 3D and in much more immersive ways. And whether, you know, if you think 10 years out, you know, the idea of moving our heads down from our phones up because we're going to have at scale, you know, augmented reality glasses, MR, right? So however you want to call mixed reality, but this idea of a consumer-facing AR isn't going to look as weird as it does today when somebody's walking down the street with AR glasses on. Again, these things are going to happen. And in 10 years' time, we're going to be sitting around in our living rooms, and we're going to have these truly amazingly real immersive experiences. And whether they're VR, whether they're AR, whether they're a combination of that— and I think that they will be combinations of this, of this putting the physical— the digital world and our physical world is going to be so realistic, and it's going to create these immersive experiences that are so entirely engaging. That, by the way, we will be pushing out into social selling as well, and there will be platforms of social selling and social engagement built on top of AR, just like Back in 2000, 2008, nobody had ever heard of Instagram. Nobody had ever heard of TikTok. TikTok is relatively new. So if you think about these billion-dollar platforms that were literally built on top of iOS, right? Those same types of platforms are going to be built on top of spatial computing platforms. And so, this idea of walking down the street and being fully connected, right? It's IoT also, but fully connected to the weather, to my calendar, life is going to get easier. And a lot of that is collecting data, right? And I, again, this goes back to the kind of the privacy thing. And I think you really look to, you know, I'll bring COVID back into it too, because there are two schools of thought here. I'm going to give you just my personal. If you've looked at China and where they are with privacy and how they were able to really lockdown COVID because there essentially is no privacy over there. And you look to kind of, you know, take for example what Apple and Google are doing together. I think that how many people— ask yourself, and I look to my kids, and my, you know, I have a 21-year-old and a 24-year-old, and me included, when I am downloading a new app, am I reading the privacy policy or am I just clicking yes? I'm clicking yes and so are they. So, you know, I think we really do a disservice to ourselves in a lot of ways around privacy. Great data, authentically, if you're giving access to it, makes our lives actually safer and it makes our lives easier. And that's what we're all eventually, I think, looking towards. Certainly me. I want to be able to spend more time with my family. So when it comes to e-commerce, if I can have to spend less time searching and less time trying to look at Excel spreadsheet of sizing and just buy things that are going to make me feel good, that from brands that are doing things great for the planet where I'm giving back and I have more time, isn't that ultimately what we're going for? And I think that's what all of us working in 3D, and it's not just 3D, 3D look. There's lots of amazing folks building amazing products right now. Some of them have been, some of them really, really, really early on, just kind of out-raising money, really doing transformative work in 3D. All of these technologies coming together are going to create this world of immersive commerce, really, of spatial commerce, that is going to truly look like nothing like it does today. And I think That is probably one of the most exciting things. And I think certainly, if you look at those opportunities, that the next 10 years of shopping— granted, a lot of people are going to go out of business. It was already going to happen even without COVID. These next 10 years of shopping are actually going to be the most exciting and most engaging, certainly of my lifetime and 30 years in this industry. And I'm super excited about it, actually.

Yeah, I mean, we are too. And I just want to say thanks for joining us today because it's been a really in-depth conversation about fit and retail and sort of what's going to happen over the next few years and actually, more importantly, what's happening right now. So yeah, thank you so much.

Yeah, thank you. It was great to chat with you today and stay healthy and stay safe.

Yeah, that was Whitney Younghoe. Thanks to her for joining us again. Really, really interesting chat. I guess that's all we've got time for this week. Just a few notes from me, we've got some live webinars coming up which you can all attend from Pi Apparel, so go on to apparel.pi.tv to check out when those are and what you can do to sign up to them. And yeah, also on next week's show we have Nidhi Puri from PVH who is going to be talking to us really all about digital transformation, um, from, from a general perspective, um, not specifically what PVH are doing, you know, those stories are well told, um, but from a more general perspective of what it's like to sort of lead teams and who should champion these things. So yeah, really interesting chat with Nidhi coming up. I hope you can all join us then, um, but until then, yeah, stay safe. I hope you're all well and Yeah, all the best. Bye-bye.

Everybody get up! Woo! Woo!