Thomas Casciola pitches Naiz's virtual fitting room, which uses machine learning to estimate 21 body measurements and recommend sizes for specific items. The startup tackles online shopping's biggest friction point: 8 billion diverse bodies crammed into limited size ranges, causing high return rates. Naiz partners with brands like Moschino to cut returns and boost conversion.
Next up we have Thomas Casquiola, who's the Head of Sales at NICE Bespoke Technologies from Spain, and he'll be talking about how they're solving fit with the power of data. And we all know the size of that problem in the industry right now, so super excited to hear your pitch. Welcome.
Okay, does it work? Yeah. How you doing? So, well, here, I'm here today to talk about, like, how we try to solve size and fit issues, okay, based on the power of data. Because actually, I would like to start with a number that I got really impressed with, which is that we are now 8 billion people on this planet. So it's Quite huge number, isn't it? And so it's true also that we are like different body types, different people, different attitudes, behaviors. But the reality is that we are like 8 billion and we must fit all of us within limited size range. And this kind of size ranges might vary across country, across region, or within collections. Okay. But that's nothing new because Actually, it's everything that has been done so far because as consumers, we know that sizing might be inconsistent among brands, okay? Because that actually has its own logic because different fashion brands, they try to reach different target consumers. Therefore, they create different fitting models and they name them with different sizing, okay? However, the problem can also happen within the same brand. And that's what we found out with some partners, that also the reality is sad but true reality because you might have also that within the same collection and the same product category, you might have different sizes, okay? As you can see in this example with Denim, with a denim brand. So that's the great question. So when, what's my size? When you need to buy or need to go shopping, normally you struggle with understanding what's the perfect size for you. And here we come to the online challenge where the tangible part of the fashion industry is not playing a crucial game as it plays in the retail, right? Because you cannot try the item, you cannot touch it, You cannot see how it fits on you. And that's the solution that many e-commerces are providing. So, okay, we have the size guide, take your own measurements. But actually, nobody probably has a measuring tape. And also, it's quite hard to measure yourself, you know? Especially for the chest or for the leg length. So it's not as easy. And if you want, I have plenty of them, so for you. And so that's the reality that consumers are facing, that they need to measure themselves. And it's not easy because first, they don't have a tape. You will have afterwards, but they don't have a tape. And second, they don't know how to take the measurements, especially like chest, bust, or leg length, as I told you before. So we're facing now that the consumers have some frictions to complete the purchase. And that's how we help them with our Size and Fit Advisor, where we help e-commerces have a virtual fitting room or call to action in product page that help the customers fill in some basic information, like gender, age, height, weight, and also body shape, or to upload the photos in some cases. The brand is willing to interact more with the users and gather more data. And our algorithm, OK, with machine learning, they calculate up to 21 different measurements of the human body, OK, according to the gender type, according to the age. The weight distribution might be different. And so they will get a personalized size recommendation for them, OK? So now we understand that, OK, this is the first First part of the equation. So how to know what's Thomas's size, so my size, on a specific item? I must know two things. First, the human body. And we got it, okay? Second part is to know the specific SKU, which is the tricky part. Because, okay, that's another slide that you can see the journey, okay, that the user does. So they put their basic input, they upload a photo, or they can ask and fulfill like morphology questions such as waist, such as hip, belly, and they will get a personalized size recommendation on that specific item, as I was saying. So the next part is understanding that fashion is way more than a bunch of measurements or size chart, and What we do ask for brands to understand how the collections are is first, as an easy option to plug and play, let's say, we ask for their size charts or garment measurements or product specs. We have an in-house patternmaking team that analyze and collaborate with brands to understand the collections and their measurements requirements and so on. But the reality is that we noticed that measurements are not enough to understand how a textile product really fit on you. So that's why we offer additionally two options. One is Having fitting try-ons on focus group and our lab in La Coruña, and the other one is doing these fitting try-ons at the brand's place, either the warehouse or the stores or the headquarters. And so that's how our algorithm and technology can match the, the size recommendation and enhance the maximum accuracy by basing on the real fit of the final product and not just only measurements. So these are some examples of impact of e-commerce, especially you can see that the return is a quite a big challenge, and they were able to reduce the return rate but also to increase the confidence, such as in Moschino and in luxury, and also increase conversions. And I needed to put this slide because actually sizing at e-commerce level is just the peak of the iceberg. And I'm not the first one probably to put an iceberg slide in a presentation, but I thought that it was worth it. And we understood that we must go more in depth, okay? Because also product is involved in making this challenge solved. And so how can product team also benefit from this and from fitting try-ons to understand in depth how the collection fit on their customers? And I brought 3 challenges, okay? 3 different scenarios that we have with our partners. The first is that the challenge was to know if consumers have the same size in the entire collection. So we did, as you can see here on the side, like data from people participating in the fitting room try-on in the focus group. And the findings were that they had up to 4 different sizing in trousers and jeans. So the actions taken by the brand, thanks to this information, was to revise, revise pattern and correct them. Another one was, okay, are we offering the right size range to our target customers? And we noticed, as you can see here, that 25% was N/A, not available. So which means that they needed to offer larger sizes, okay, to adapt to real consumer demand based on data coming from e-commerce, okay, and from the fitting try-ons. And finally, also, if our consumers are morphologically similar across different markets. And of course, different markets might have different, different tastes and fit requirements. Okay, so we can provide BMI data and also minimum and height data. So that's just to end the presentation because I'm seeing I'm sticking to the time. And that's just to say that of course these are some of our numbers, okay, of testers we've been collaborating with. And fitting trials realized. And this is the smart catalog where you can now have access to this data. And that's more or less everything. Thank you for your attention.
