The Apparel Industry’s $100 Billion Fit Problem

Speaker A: Marketers love to talk about personalization, but what if the product doesn't fit? Nothing else matters. Every year, billions in apparel are returned for one reason, sizing. My guest today is using AI to fix that. Proving that when brands nail fit, conversions climb and returns drop. What inspired you to start Bold metrics? Like what? When did you realize that sizing was a problem that had to be solved?

Speaker B: We were an e commerce store that sold ready to wear and custom clothing products. When we were running that site, we wanted to really have a control of our return rate and as well as conversions. And so we really thought about how can we mitigate returns in a powerful way that can prevent people from potentially buying the wrong size and of course returning it. We thought, well, why don't we build a solution that could essentially capture with machine learning methods our customers, detailed body measurements and that process that I guess was our aha moment because our return rates were very low in fashion metric 1.8%, uh, which was something to really be proud of. And in that process of getting it that low, we realized, well, we've built something potentially really powerful here for the broader apparel industry.

Speaker A: What's the cost to the ecosystem, to the brands and the retailers when it comes to not having a solution like this?

Speaker B: There's really like lost opportunities that new customers to the site, you don't want us fall short on giving them the information that they need to understand their sizing. We see, uh, usually around maybe 60 or actually even up to 70% returns are fit related. And so fit is unquestionably the dominant reason for returns. In the absence of that, the static size charts don't convert customers very well compared to something like an intelligent solution.

Speaker A: Today we're exploring how AI powered fit intelligence is reshaping retail. Joining me is Dana Burns, co founder and CEO of BoldMetrics, helping apparel brands predict perfect fit without a measuring tape.

Speaker B: Welcome to the Martech Matrix a blurbs production. Blurbs is a community platform built by brands designed around how they want to search for solutions, not how vendors want to sell them. It's where marketers and e commerce leaders find the right tech faster with clear answers and no sales fluff. Each week we bring that mission to life, interviewing Martech founders and industry pros to explain what they do, the problems they solve, and how the space is evolving, all without the buzzwords. And now your host, Sean Simon.

Speaker A: Marketers love to talk about personalization, but what if the product doesn't fit? Nothing else matters. Every year, billions in apparel are returned for one reason, Sizing My guest today is using AI to fix that. Proving that when brands nail fit, conversions, climb and returns drop. Welcome to the Martech Matrix, where we connect the dots between data tech and the human side of marketing. I'm Sean Simon and today we're exploring how AI powered fit intelligence is reshaping retail. Joining me is Dana Burns, co founder and CEO of Bold Metrics, helping apparel brands predict perfect fit without a measuring tape. Here's how we describe Bold metrics on, um, blurbs. Bold Metrics uses AI driven digital twins to help apparel brands predict perfect fit, improving conversions, cutting returns and elevating the customer experience. Dana, welcome into the matrix.

Speaker B: Thanks for having me. Happy to be here.

Speaker A: It's great to have you. So let's start with your story. What inspired you to start Bold Metrics? Like what? When did you realize that sizing was a problem that had to be solved?

Speaker B: Yeah, so we started Bold Metrics actually as a different concept entirely. We were an e commerce store that sold both ready to wear and custom clothing products. Sort of like imagine like a multi brand store and it was called Fashion Metric. This is back in the day and when we were running that site, uh, we, we wanted to really um, have a good control of our return rate, uh, and as well as conversions. And so we've really thought about how can we mitigate returns, um, in a powerful way that can prevent people from potentially buying the wrong size and of course returning it, et cetera. And so as we thought about it more and um, largely inspired by my family's craft and master tailoring, my family's, um, I come from a family of master tailors. Uh, and thinking about how a master tailor approaches sizing is really first capturing, measuring a customer and so getting all their critical measurements and then from there you can understand what size that they would be and of course create custom clothing and um, all the rest of that. So in running Fashion Metric, our e commerce site, to mitigate returns, we thought, well, why don't we build a solution that could essentially capture with machine learning methods our customers detailed body measurements. And we uh, took a pretty aggressive approach in the store. Where to shop the store you have to answer our survey questions. And then in the back end we would calculate, uh, all your detailed body measurements. And then once you're shopping the site, you actually, there was no notion of selecting a size, uh, when you are checking out, but instead you just shop based on style. And we will figure out the sizing for you because you've already gone through our process where we've Captured your body measurements. And so then we would ship uh, the correct size to your doorstep. And that process that I guess was our aha moment, uh, because our return rates were very low with fashion metric 1.8%, uh, which was something to really be proud of. And in that process of getting it that low, we realized, well, we've built something potentially really powerful here for the broader apparel industry. Not just to impact fashion metrics, uh, return rates, but we could have an impact on the apparel industry as a whole and have this technology accessible to other brands and retailers to really help mitigate returns, increase conversions and just overall improve the consumer experience by giving customers that confidence in the purchase, um, that they are making.

Speaker A: So how many years ago was this that you started that this idea came to fruition?

Speaker B: This was several years ago about I guess a little bit over 10 years ago.

Speaker A: So long before A.I. right. So before A.I. was commonplace.

Speaker B: I guess before A.I. was commonplace. I mean, uh, machine learning. A.I. has been, you know, in different forms throughout the decades, but of course not in a time where it was as commonplace. And so there was a, you know, back in the day, a lot of disbelief around capabilities that AI has. And I think nowadays a lot of that disbelief has been resolved and seeing the power that AI has. Um, but certainly it was the early days for sure.

Speaker A: Has that changed your business in terms of how you do what you do? Um, with AI being more not just accepting, but readily available.

Speaker B: I think just like I said so in the early days when we first, we eventually pivoted the business model. Of course we're not an E commerce store anymore. We are a SaaS company, changed the name to Bold Metrics. And in the early days selling our technology and particularly, and I guess it would be worth describing the core of our tech and how that works before getting into this point on how AI has or I guess the broader adoption of AI and how that's impacted the business. But the core of our tech, uh, is based on calculating uh, a consumer's detailed body measurements. Uh, this is everything from like chest circumference, waist circumference, neck circumference, arm length, um, hip circumference, thigh, uh, etc. From neck to ankle, uh, over 50 body measurements are produced. And we're doing this all through AI uh, and from the consumer's vantage point, they're just answering some simple survey questions about themselves. They don't have to take a measuring tape out and measure themselves. Uh, they certainly ah, don't need to take a photograph of themselves. All of These things really hinder converting the customer to actually adopt a solution like ours. So it's all just answering questions that people readily know about themselves. And then we produce all these detailed body measurements in the background and then those measurements can then be connected to the garment sizing details within the ready uh, to wear paradigm so that we can surface not only this is your best size, uh, for you, but this is how it's going to fit across your body for the critical points of measure for fit for that garment. And then of course since we're producing body measurements, we also work with custom clothiers and made to measure um, brands uh, so that they can capture body measurements in a relatively easy way instead of having to measure measure their customers. So that surfacing of these detailed body measurements in our early days uh, was there was a lot of disbelief around that. So we had to do a lot of um, I guess uh, proving that this is possible, that you can do this um, with machine learning, with AI and I think with the advent of uh, the broader adoption of AI and seeing all of the amazing things that it can do in more recent years has really helped in that process. We don't have as much of that, you know, disbelief that we're dealing with within our sales cycle like we used to. If you were to look back to you know, prior to 2020.

Speaker A: Yeah, it just takes takes some time for people to believe that the technology will do what you're promising. Right. Like with anything else, um, how does the experience differ from desktop to mobile? Is it the same experience? Is it different? Do you use the phone technology to help or is it exactly the same?

Speaker B: Well it's, it's um, we don't, we're on a native app, so we are a uh. Well our most used solution is called the Smart size chart which is, exists on the product display page. Um, it's mobile responsive of course. So if you're opening that on your phone it is mobile responsive. So you'll have as good of an experience as if you're looking at it on desktop. Um, and then of course we have our virtual Sizer API which is essentially the same underlying technology as the smart size chart that exists on that product display page. Um, but instead since it's an API, this allows our clients to access the technology and create their own front end experience. So we've got um, clients that do some interesting things around um, personalized product listing pages so you can you know, have a grid of products that are personalized to you for your fit and your body and how it's going to fit you, what's in stock, etc. Um, and uh, and other sort of like interesting use cases within, you know, implementation within the loyalty program, et cetera. But regardless of the case, desktop or mobile, it's responsive, uh, and looks good in either, in either scenario.

Speaker A: So how do you, how do you engage with a, with a manufacturer or a brand when maybe a medium in one brand we know fits one way and a medium in another brand that's very different? Um, do you take into consideration what the brand does, like the way their product fits or how do you account for the different cuts that uh, in the same size?

Speaker B: Well, that exact issue is the reason why fit intelligence systems should exist and be on a brand or retailer's site, which is that uh, there, you know, there isn't standardization when it comes to sizing. And by the way, that's a, uh, that's a positive thing. I don't think that we should be in a world where, imagine a world where all sizes are exactly the same across every brand and it's completely standardized, obviously. Maybe it's not obvious, but it's worth mentioning if it's not obvious to people. You would have like, for sure, inclusivity issues where imagine if everything is exactly the same across every brand and you don't fit into that paradigm. Right. So therefore you uh, are I guess, not included in the spectrum of standardized sizings in society and I suppose you would have to be relegated to custom clothing exclusively. So, you know, not having standardized sizing is a good thing. It allows for, you know, um, individualization, uh, brands to, to you and for you to find brands that fit you well and you know, inclusivity et. Um, so yeah, so that's why essentially you need these systems to help a consumer understand their sizing. And so of course for a fit intelligence system to work effectively, the fit intelligence system should have an understanding of that brand's actual sizing. So that's a critical piece. Um, so of course we ingest sizing information on a brand so that we are able to understand, you know, what the difference, um, between the different categories or product IDs and what the sizing is associated with that and then how that uh, relates to the underlying body that's ideal to fit within those sizes. And of course we capture your detailed body measurement information. We call that your digital twin, uh, your digital twin in the concept of your body measurements. Uh, so we can, you know, surface your best size and then we know about that, that garment that you're looking at and the sizing associated with it. So not only this is your best size, but because we've captured your digital twin information, this is how it's going to fit on you. Um, and then you can make that decision on the purchase of what size you want based on your own subjective fit preference. Uh, because an algorithm, AI machine learning, um, isn't necessarily going to capture your fit preference if you've never interacted with this system before. Uh, and instead we take sort of like this agnostic approach of like we're not going to be able to predict what your preference is. Maybe you let like something to be a little bit looser, um, around your waist or your hips. So instead of predicting your fit preference, we will understand uh, how you're going to fit across the sizes and then you can browse your best size and the adjacent sizes to understand how that will fit differently. Most people tend to be in between two sizes. It's really common. So uh, you know, if you're in between a medium and large, for example, you can see the trade offs and see, okay, well if I buy the size medium it's going to be slightly snug around my waist, but maybe they'll just be just right at the chest. But if I go up a size to the large, it's going to be just right around the waist, maybe a little looser in the chest. But I would rather that fit on my body because I don't like the feeling of things being snug around my waist. So we essentially empower the uh, customer with that information so that they can make this informed fit decision at time of purchase.

Speaker A: I like that because everybody likes to wear the same thing differently, right? So it gives them a choice. Right?

Speaker B: Yeah.

Speaker A: Um, what's the, I mean you're solving a big problem. Um, what's the, what's the cost to, not for your product, but what's the cost to the, to the ecosystem, to the brands and the retailers when it comes to not having a solution like this, like when it comes to returns, is there you have a sense of how big that opportunity is that you can solve?

Speaker B: Yeah, Well, I guess two of our core KPIs that we impact are uh, improving conversions and reducing returns on the conversion side. I mean there's really like lost opportunity. So m, a lot of marketing tech tools and a lot of budget. Honestly, uh, within uh, the industry is focused on getting new customers to your site. Um, but once that new customer is to the site, you know, you don't want to fall Short on giving them the information that they need to understand their sizing, um, and to make that, that all that money you spent to bring that new customer to the site to actually convert them. And so uh, a fit solution really plays within that camp. New customers or if a brand's changed their sizing detail or perhaps the customer if they've lost or gained weight, um, and so they're no longer familiar with their size. And so giving the customer the information to make that informed decision to help improve those conversions. So in the absence of that, the static size charts don't tend to convert customers very well compared uh, to something like an intelligent solution that can give you that information. And then on the return uh, side, uh, now this is obviously some uh, tends to be a big line item on a lot of brands where they're losing um, to returns. And so uh, you know the average return rate within the apparel industry tends to be within the 20 to 30% range. And the majority of uh, returns are due to fit. So we see usually uh, around a 60 to 60 or actually even up to 70% of returns are fit related. And so fit is unquestionably the dominant reason for returns. Um, so if you want to have some way of tackling that, having it on the front end of that purchase to inform the customer this is how it's going to fit, this is your best size. These are some other considerations if you wanted to uh, if you're in between sizes to uh, help mitigate some of those uh, returns and giving the customers the best probability, if you will, of preventing a return by arming them with this information. Because at the end of the day consumers, I mean it's not a great experience to have to return. Like it's, you know, it's another thing on your to do list. So giving that customer the information that they need so that they don't have to go through that experience is obviously a plus.

Speaker A: Yeah. And certain marketplaces have just made it too easy to return. And so I think you know, for those, for other brands they're, the customers are just like I'll just return it because I can do it with that marketplace. Um, but if, if it fits then I don't have to. I imagine there's a loyalty component like a repeat purchase. You mentioned CAC earlier. Um, I have to imagine that if people are happy with the purchase that they're coming back to that brand or that retailer buying more, which then further further reduces the cac.

Speaker B: Right.

Speaker A: I imagine, I imagine the loyalty is

Speaker B: a big piece of it absolutely overall just a better consumer experience um, so that you have a better chance of that customer coming back and buying more.

Speaker A: So of course for sure just going back to how it works, um, if there's a brand that wants to work with Bold metrics, what does that integration look like in terms of do you integrate with the E comm platform? There's CDP's personalization tools, like where are their integrations? How do you hook into their experience?

Speaker B: Yes, so we integrate. Well I think I touched on this earlier but we do integrate uh within a couple of core options. So the Smart size chart which is our more of our flagship product, this is a uh, it's integrated on the product display page often as like what is my size or calculate size button entry around usually where the size chart link exists. Um and so you click on that link and that loads um our uh widget that uh is pre designed, there's some customization options available for that. Um and so it's just kind of like plug and play, very easy for a brand to implement. And um, oftentimes though brands and retailers will want to create their own customized experience with fit and sizing. And so that's where our Virtual Sizer API comes into play. So the API can be integrated however they like. Uh, so if they wanted to completely replace the smart size chart, um flow or user experience and have their own flow and user experience that's certainly um possible with the Virtual Sizer API. Uh but then there's also some really interesting integrations I think that I touched upon earlier where you can um, have it create uh, uh within uh the customer flow, have it create a personalized product listing page so you can have a ah PLP that is specific to your body, specific to what's in stock, that kind of thing. Um, and also integrations within the loyalty program itself. So if you create an account or if you sign up for the loyalty program, answer the questions there so that you have your sizing information stored uh, in the future.

Speaker A: That's very cool, like the landing page idea. Um, so with all the data that you guys analyze, has there been anything that's surprising that you're like wow, I never thought that um, has that ever popped up any kind of unique data points?

Speaker B: Unique data points. Let me think about that for a sec. I mean I guess that plp, um experience that I mentioned uh to create this personalized grid, um some, some early studies with that with clients, meaningful um overall site wide revenue lift. I think uh one client that implemented within um 30 days had a 7% ah measured site wide revenue lift. So that was certainly I think surprising to us that it would have such a huge impact. This sort of speaks to how consumers are really um, you know, really want to have that personalized experience and that can, that can make a meaningful impact.

Speaker A: Yeah, for sure, for sure. I think people are starting to really expect that. So let's, let's about some like um, case studies or some use cases where um, you know a brand really saw an impact to their business like you just mentioned but maybe get a little more detailed about it. Maybe like a D2C brand, omnichannel brand or even a luxury label or multiple ones, whichever you want to share.

Speaker B: Yeah, well, so uh, yeah I mentioned conversion. Conversion impact is uh, a big one for us so. Well I can give you sort of like average stats. Um, when a, when a customer comes to the pdp, if they interact with our smart size chart solution, they're usually around 3 to 4x more likely to convert than consumers that you know go to that same PDP's uh and don't interact with the sizing solution. So see a really positive lift there. Return uh, rates of course that's another uh, one that we've been talking about that we impact and uh, that's quite a range. It depends obviously on what your initial return uh rate problem is. If you have a big problem you're over 30% returns, we're usually going to make a pretty big impact there. Um, but on average, or I guess I could give a range is usually around 10 to 25% return rate reduction for consumers that follow uh, their size recommendations versus those that don't. Um, and then aov the average order value. We often have a good impact there. I think that that um, average is around 22% increase in average order value. So again getting confidence in what size someone is usually can lead to them adding more to cart because they have that confidence.

Speaker A: There are brands thinking about the environmental impact as well. Does that come up a lot like the fact that by reducing returns you're reducing emissions and all that?

Speaker B: Sadly I don't think a lot of consumers have much awareness for the fate of returns. Uh and that's something, something that we, we try to get out there more in our narrative to give consumers more awareness around that. But of course you know often people think that their return is going to just get you know, repackaged, reshelved, uh, and the end so there's no real impact. But of course you know as, as you're probably aware a Lot of returns end uh, up being incinerated, end up in a landfill, um, and don't make it back onto the shelf, um, for. Because many brands find that it's more expensive to you know, re, you know, do the quality assessment and repackage etcetera and reshelve it. So yeah, that's obviously got a huge impact as well as of course the carbon footprint of shipping the product uh, to you and then back etc. So it really can, it's actually quite a significant impact on the, on the overall global footprint with shipping all this. Well E Commerce and shipping products back and forth from returns, etc. And of course the landfill issue. So it's a big thing. But I think a lot of consumers don't actually have much awareness for that.

Speaker A: So yeah, I mean E Commerce, E Commerce continues to increase, grow as, as a, as a sector and then returns. You probably just can't keep up with uh, the number of returns that you prevent. It probably just can't keep up with the growth. And so the environmental impact is really, really strong. When you work with a brand or a retailer, um, who's involved from the organizational side, from an implementation perspective and management.

Speaker B: Yeah, well our typical decision maker is usually within E Comm, um, so usually a VP of E Comm. Director of E Comm, something like that, E Commerce Manager, um, sometimes the innovation, uh, folks get involved um every once in a while someone from the finance team because of the obvious um, impact that we make there on the bottom line as well as top line with conversions. Uh, but yeah Typ, the, the E Commerce team is who we're speaking with. We do have, I should mention a product called Apparel Insights and this anytime someone uh, engages with our solutions and receives a size recommendation, makes a purchase, uh through to whether they keep it, return it, all that information's tracked. And anytime of course that someone engages with the solution in the back end where it's like we've body scanned them um because we have their digital twin details of all their detailed body measurements, over 50 body measurements relative to what they, what they, you know, what products they purchased, if they kept it, if they returned it, et cetera. And so our Insights product, Apparel Insights connects uh, all this information, uh, so that actually for product designers, uh, can really for the first time ever have an understanding of how are their products fitting, uh, you know, and is there certain areas of improvement within the technical design specifications that could allow them to fit a broader market, to fit their customer demographic when it comes to um, body shape and Sizing better? Are they, could they expand sizes? Are they missing out on, you know, ah, the, on the larger sizes or the smaller sizes, Is it time to add a 2xL or 3xL or, or an extra small or 2 extra small? Um, so some of these questions. So um, this is all to answer your question on uh, who buys that is typically within the product design or also the innovations teams. But um, within product design being able to access that information, super interesting and valuable to those teams and typically uh, would be interfacing with people in the product side.

Speaker A: I'm curious, does that usually happen after you're already in the door? Like you're working with the E Comm team on a fit solution and then product gets involved around manufacturing or does it work both ways? Sometimes.

Speaker B: Usually yeah. Uh, later on. So you get started, we get you in the door, so to speak, with, with the fit and sizing solutions and then we're collecting all this really interesting information. And so can you connect us with the product teams? We'd love them to take a look. And then typically uh, when they see that type of data that we're surfacing, it's really quite interesting to them because it's like they body scanned their customers and so they can really start to see these tangible insights and have a really like data driven view of how their products are fitting.

Speaker A: How do you manage like data privacy? Like you have measurements on people, right. Is there any issues around, you know, what you can and can't do with that data and how you manage that with the consumer, given that the consumer belongs to the brand and you're getting.

Speaker B: Yes, we do not any, it's called pii, Personally Identifiable information. So we very much don't collect any of that. So you don't need to give us your name or your identity. Uh, the data is completely anonymized. We pair you with a unique ID for tracking purposes. Just like a long um, set of numbers essentially so we can track it through to purchase and returns. But uh, yeah, at no point do we um, ask you any information that would be personally identifiable to you.

Speaker A: Okay, very cool. So just looking ahead into the future of the space, like what do you see, uh, the future looking like for fit Intelligence, especially with, you know, AI becoming more prevalent, do you see big changes happening? Um, are there technologies that will, or things that will allow your technology even better?

Speaker B: Yeah. So, well, over the next three to five years I think Fit Intelligence is really poised to evolve from a sizing tool which right now it's kind of like within that Paradigm into really a core layer of personalized, uh, commerce infrastructure. Uh, so converging several adjacent categories. So from AI driven personalization to digital identity, which we've talked a lot about today, um, predictive merchandising and of course data driven product design. And so likely we'll see fit systems, um, become multimodal and context aware. For example, where, what climate are you living in? Uh, can help add to these, to its full sort of, um, multimodal awareness of your situation and how you're living and blending the body data models with style preferences and your purchase behavior. Um, and this convergence will allow brands to move beyond, you know, what size fits towards what fits you, your lifestyle, your preferences, etc. Etc.

Speaker A: That's very cool. I can see some kind of merge happening with like those AI sales agents that are getting smarter and smarter where they're just having conversations with shoppers. They um, could be asking them questions that uh, maybe helps you make their sizing more accurate. But I'm sure we're, we're a ways off from that. All right, um, let's move forward to the uh, the final segment of the show, the rapid fire segment. Um, so short answers. Just be real quick about it. Nothing, nothing crazy here. Um, so right now, what is your favorite martech tool? It can't be Bold Metrics.

Speaker B: Well, other than the obvious, maybe that,

Speaker A: maybe something you use at Bold Metrics.

Speaker B: Well, okay, well, I will say to plug our partners. We love our partners. Our website has our list of partners. Um, if I had to pick, you know, we particularly enjoy working with Loop returns, their returns logistics solution. Uh, we have a direct integration with their data feed. So if uh, we have shared clients. If a client's um, integrated with Loop, we can get the, the returns, uh, data feed directly sent and integrated within our systems. Makes it a lot easier for the client as well. So the two work together in very complementary way. So I'll answer with that one.

Speaker A: That makes sense too. I love the explanation. All right, biggest martech myth you'd love to bust.

Speaker B: Um, biggest myth. Uh, but let's see. I guess, well, maybe it's just like a startup myth, but like if you build it, they will come concept. If you build it, they won't come. You have to really work at that and be persistent. And so founders often might believe that a brilliant product will naturally attract customers and investors, etc. And in reality, uh, even the best technology could fail without the right distribution models, strong storytelling and really like relentless iteration on customer, uh, feedback iterating on your product, etc. And maintaining persistent.

Speaker A: Yeah, it's definitely really hard to make people aware of you when there's so many companies out there, and, um, help them understand what makes you better or different. All right, one piece of advice for women founders in tech.

Speaker B: You know, I, uh, think my advice for women founders in tech would be the same advice for any founder in tech, which is, I guess, two pieces of advice. If I could give two. One is, uh, you know, create opportunities for yourself. This was a piece of, uh, advice that really inspired me when I was first starting out on my journey, um, from one of the founders of Twitter who said, you know, create opportunities, put yourself in places to allow things to happen. Uh, so get yourself out there, and you never know what will happen if you create these opportunities for yourself. Um, so. And then my second piece of advice would be, and I talked about this word a little bit earlier with the Martech favorite, or I guess with the myth one. Yeah. Is, uh, persistence. So starting a company, growing a company requires persistence. So stay persistent and keep going. There's always ebbs and flows and as. As you, you know, start and run a company. But persistence is what gets you through the long haul.

Speaker A: 100. You can't be shy.

Speaker B: Yeah, that's.

Speaker A: That's perfect. Dana, thank you. This was fantastic. Um, thanks for joining us and for everything you do to make retail smarter and more personal. Um, you can learn more about bold metrics@boldmetrics.com and check out their profile at trustblurbs.com thanks for listening to the Martech Matrix, presented by Blurbs, the platform where marketers discover vendors they can actually trust.

Speaker B: That's it for today's episode of the Martech Matrix, a blurbs production. To see what's coming up next, visit themartechmatrix.com and if you're looking for your next Martech solution, start your search@trustblurbs.com where marketers discover vendors faster.