In this episode of The MarTech Matrix, Sean Simon sits down with Daina Burnes, CEO & Co-Founder of Bold Metrics, to explore how AI-driven fit intelligence is transforming apparel commerce.

Daina shares the origin story of Bold Metrics, how the company predicts over 50 body measurements using simple customer inputs, and why fit uncertainty remains the biggest reason shoppers fail to convert — and the biggest driver of apparel returns.

We dive into the economics of returns, the limitations of static size charts, and why size confidence should be considered a performance lever, not a UX enhancement. Daina also looks ahead to the next 3–5 years, where fit technology evolves into a multimodal, context-aware personalization layer that blends body data, climate, lifestyle, and purchase behavior.

If you lead eCommerce, merchandising, or personalization for an apparel brand, this episode is essential listening.

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Takeaways

• 60–70% of apparel returns are caused by fit — the #1 margin leak in the industry.
• Bold Metrics predicts 50+ body measurements without photos, scanners, or measuring tapes.
• Fit intelligence is a conversion driver, not a UX enhancement.
• Static size charts underperform compared to intelligent size guidance.
• The next era of fit tech will merge personalization, digital identity, and predictive merchandising.
• Fit systems will become multimodal: climate, lifestyle, body data, and style preferences.
• Apparel brands can significantly reduce returns by arming shoppers with pre-purchase fit clarity.
• The industry’s shift will move from “What size?” to “What fits me?”

Chapters

00:00 — Intro & Who Is Bold Metrics?

02:15 — The Origin Story: FashionMetric

06:40 — Master Tailoring Meets Machine Learning

10:25 — How Bold Metrics Predicts Body Measurements

12:30 — Why Fit Is the #1 Conversion Killer in Apparel

14:15 — The Economics of Returns

17:50 — Size Confidence as a Performance Lever

21:05 — Why Static Size Charts Fail

25:35 — The Future of Fit Intelligence (Multimodal + Context Aware)

29:10 — Fit as a Core Layer of Personalized Commerce

32:00 — Advice for Apparel Leaders

35:00 — Closing Thoughts

Solving the Apparel Industry’s $100 Billion Fit Problem

For the past decade, apparel eCommerce has invested billions into acquisition, channel optimization, personalization engines, and loyalty programs. Yet one problem continues to drain margin and stall conversion: fit uncertainty.

It’s not just a UX frustration. It’s an economic leak.

Return rates in apparel consistently hover between 20–30%. A staggering 60–70% of those returns are caused by fit. Every industry leader knows this — yet most continue to rely on static size charts and guesswork, even as they invest in AI everywhere else.

This is where fit intelligence has been underestimated.

During our latest conversation on The MarTech Matrix, Bold Metrics CEO Daina Burnes made a powerful point: the future of apparel personalization doesn’t start with style preferences or behavioral cues. It starts with the body.

Because without foundational size and fit clarity, no personalization stack — no recommendation engine, no merchandising strategy, no lifecycle automation — can perform at its highest potential.

Fit as Infrastructure, Not Feature

The current generation of fit tools treat sizing as an accessory. But as Daina explains, the next generation will treat fit as infrastructure — a core layer that connects what customers buy with how they actually live.

Think of what becomes possible when brands combine:

• AI-generated body measurements
• Contextual data like climate, location, and lifestyle
• Style preferences
• Purchase history
• Predictive merchandising signals

This becomes the connective tissue of personalized commerce: a real-time, multimodal profile of the customer.

In other words, apparel companies can move from asking:

“What size are you?”

to a far more powerful question:

“What fits your life?”

The Strategic Shift for Apparel Leaders

Fit intelligence represents a shift from reactive decision-making (“We hope this fits”) to proactive customer empowerment (“Here’s how this will fit your body and lifestyle”).

Brands that embrace this shift will:

• Convert more first-time shoppers
• Reduce preventable returns
• Improve product design with better measurement data
• Deliver more meaningful personalization
• Build real loyalty through confidence and consistency

This isn’t theory — the operators who get this right are already outperforming the market.

The Bottom Line

Fit is about to become one of the most important levers in apparel commerce. As technology converges around body data, personalization, and predictive merchandising, brands that invest now will build a competitive moat that’s incredibly hard to replicate.

Daina puts it best:

“The next era of fit intelligence moves beyond size. It becomes context-aware, multimodal, and deeply personal.”

The future of apparel won’t be won by the brands with the most SKUs — but by the brands with the deepest understanding of how every product fits every customer.

Full Transcript of the conversation with Daina Burnes

Sean Simon (00:01.987)
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 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.

Daina Burnes (Bold Metrics) (00:51.0)
Thanks for having me, happy to be here.

Sean Simon (00:52.557)
That’s great to have you. So let’s start with your story. What inspired you to start Bold Metrics? When did you realize that sizing was a problem that had to be solved?

Daina Burnes (Bold Metrics) (01:02.786)
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. imagine like a multi-brand store, and it was called Fashionmetric. This is back in the day. And when we were running that site, we wanted to really have a good control of our return rate and as well as conversions.

we’ve 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, et cetera. And so as we thought about it more and largely inspired by my family’s craft in master tailoring, my family’s, I come from a family of master tailors 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 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 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 all your detailed body measurements. And then once you’re shopping the site, you actually, there was no notion of selecting a size 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 the correct size to your doorstep. And that process, that I guess was our aha moment, because our return rates were very low with FashionMetric, 1.8%, which was something to really be proud of. And in that process of getting it that low, we realized, we’ve built something potentially really powerful here for the broader apparel industry, not just to impact FashionMetric’s return rates, but we could have an impact on the apparel industry as a whole and have this technology

Daina Burnes (Bold Metrics) (03:26.634)
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 that they are making.

Sean Simon (03:38.787)
So how many years ago was this that you started, that this idea came to fruition?

Daina Burnes (Bold Metrics) (03:43.15)
This was several years ago, I guess a little bit over 10 years ago at point.

Sean Simon (03:46.499)
So long before AI, right? So before AI was commonplace.

Daina Burnes (Bold Metrics) (03:51.158)
I guess before AI was commonplace. mean, machine learning AI 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, lot of disbelief around capabilities that AI has. And I think nowadays a lot of that disbelief has been resolved in seeing the power that AI has. But certainly it was the early days for sure.

Sean Simon (04:17.081)
Has that changed your business in terms of how you do what you do with AI being more, not just accepting, but more readily available?

Daina Burnes (Bold Metrics) (04:29.134)
I think just like I said, so in the early days when we first, you know, so we eventually pivoted the business model, of course, we’re not an e-commerce store anymore. We are a SaaS company, changed into 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.

Sean Simon (04:29.315)
Thank you.

Daina Burnes (Bold Metrics) (04:58.818)
But the core of our tech is based on calculating a consumer’s detailed body measurements. So this is everything from like chest circumference, waist circumference, neck circumference, arm length, hip circumference, thigh, et cetera, from neck to ankle. Over 50 body measurements are produced. And we’re doing this all through AI. 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. They certainly 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-to-wear paradigm so that we can surface not only this is your best size for you, but this is how it’s gonna 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 closures and made to measure brands so that they can capture body measurements in a relatively easy way instead of having to measure their customers. So that’s…

surfacing of these detailed body measurements in our early days, there was a lot of disbelief around that. So we had to do lot of, I guess, proving that this is possible, that you can do this with machine learning, with AI. And I think with the advent of 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 disbelief that we’re dealing with within our sales cycle.

like we used to if you were to look back to prior to 2020.

Sean Simon (06:50.905)
Yeah, it just takes some take some time for people to believe that the technology will do what you’re promising right like anything else 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?

Daina Burnes (Bold Metrics) (07:07.63)
Well, we’re not in a native app, we are a, well, our most used solution is called the Smart Size Chart, which exists on the product display page. It’s mobile responsive, of course. So if you’re opening that on your phone, is mobile responsive, so you’ll have as good of an experience as if you’re looking at it on desktop.

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. 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 clients that do some interesting things around personalized product listing pages. So you can 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, et cetera.

And another sort of like interesting use cases within, know, implementation within the loyalty program, et cetera. But girl, this is the case, desktop or mobile, it’s responsive and looks good in either scenario.

Sean Simon (08:15.011)
So how do you engage with a manufacturer or a brand when maybe a medium in one brand we know fits one way and a medium in another brand fits very different? Do you take into consideration what the brand does, like the way their product fits, or how do you account for the different cuts in the same size?

Daina Burnes (Bold Metrics) (08:40.898)
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 there isn’t standardization when it comes to sizing. And by the way, 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 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 not having standardized sizing is a good thing. It allows for, you know,

Sean Simon (09:09.369)
you

Sean Simon (09:31.193)
you

Daina Burnes (Bold Metrics) (09:37.621)
individualization brands to you and for you to find brands that fit you well and inclusivity, cetera. 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. So of course, we ingest sizing information on a brand so that we are able to understand

you know, what the difference between the different, you know, categories or product IDs and what the sizing is associated with that. And then how that 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, your digital twin in the concept of your body measurements. 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. not only this is your best size, but because we captured your digital twin information, this is how it’s going to fit on you. And then you can make that decision on the purchase of what size you want based on your own subjective fit preference. Because an algorithm, AI, machine learning, isn’t necessarily going to capture your

fit preference if you’ve never interacted with this system before. 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 like something to be a little bit looser around your waist or your hips. So instead of predicting your fit preference, we will understand 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.

But if you’re in between a medium and large, for example, you can see the trade-offs. Let’s see, OK, well, if I buy the size medium, it’s going to be slightly snug around my waist. But maybe it’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 customer with that information.

Daina Burnes (Bold Metrics) (11:59.079)
so that they can make this informed FIT decision at time of purchase.

Sean Simon (12:02.721)
I like that because everybody likes to wear the same thing differently, right? So it gives them a choice, right? What’s the… I mean, you’re solving a big problem. What’s the cost to… Not for your product, but what’s the cost 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, there… Do have a sense of how big that opportunity is that you can solve?

Daina Burnes (Bold Metrics) (12:27.278)
Yeah, well, I guess two of our core KPIs that we impact are improving conversions and reducing returns. On the conversion side, mean, there’s really like lost opportunity. So a lot of marketing tech tools and a lot of budget, honestly, within the industry is focused on getting new customers to your site. And but once that new customers to the site, you know, you don’t want to fall short on giving them the information that they need to

understand their sizing and to make all that money you spent to bring that new customer to the site to actually convert them. And so a fit solution really plays within that camp, new customers, or if a branch changed their sizing detail, or perhaps the customer, they’ve lost or gained weight, and so they’re no longer familiar with their size. 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 to something like an intelligent solution that can give you that information. And then on the return side, now this is obviously tends to be a big line item on a lot of brands where they’re losing to returns. so…

You know, the average return rate within the apparel industry tends to be within the 20 to 30 % range and the majority of returns are due to fit. So we see usually around a 60 to 60 or actually even up to 70 % of returns are fit related. so fit is like unquestionably the dominant reason for returns. So if you want to have a 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, if you’re in between sizes, to help mitigate some of those 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.

Daina Burnes (Bold Metrics) (14:43.958)
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.

Sean Simon (14:51.705)
Yeah, and certain marketplaces have just made it too easy to return. And so I think, you know, for those, for other brands, their customers are just like, I’ll just return it because I can do it with that marketplace. But if it fits, then I don’t have to. I imagine there’s a loyalty component, like a repeat purchase. mentioned CAC earlier. 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 reduces the CAC, right? I imagine, I imagine the loyalty is a big piece of it.

Daina Burnes (Bold Metrics) (15:15.936)
yeah.

I mean, absolutely, overall just a better consumer experience so that you have a better chance of that customer coming back and buying more. So of course.

Sean Simon (15:27.481)
For sure just going back to how it works When 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 econ platform or CDPs personalization tools? Like where are their integrations? Like how do you hook into their experience?

Daina Burnes (Bold Metrics) (15:45.155)
Yeah, so we integrate, well, I think I touched on this earlier, but we do integrate within a couple of core options. So the smart size chart, is more of our flagship product, this is 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. And so you click on that link and that loads our widget that is pre-designed. There’s some customization options available for that.

And so just kind of like plug and play, very easy for a brand to implement. And 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. So if they wanted to completely replace the smart size chart flow or user experience and have their own flow and user experience, that’s certainly…

possible with the virtual Sizer API. But then there’s also some really interesting integrations, I think, that I touched upon earlier, you can have it create within the customer flow, have it create a personalized product listing page. So you can have a PLP that is specific to your body, specific to what’s in stock, that kind of thing.

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 in the future as well.

Sean Simon (17:20.397)
That’s very cool, like that landing page idea. So with all the data that you guys analyze, has there been anything that’s surprising that, like, wow, I never thought that. Has that ever popped up, any kind of unique data points?

Daina Burnes (Bold Metrics) (17:33.963)
Data points. Let me think about that for a sec. I mean, I guess that the PLP experience that I mentioned to create this personalized grid, some early studies with that with clients saw meaningful overall site-wide revenue lift. think one client that implemented within 30 days had a 7 % 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 really want to have that personalized experience and that can make a meaningful impact.

Sean Simon (18:13.421)
Yeah, for sure. think people are starting to really expect that. So let’s talk about some case studies or some use cases where 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 DTC brand, the channel brand, or even a luxury label or multiple ones, whichever you want to share.

Daina Burnes (Bold Metrics) (18:36.386)
Yeah, well, so I mentioned conversion. Conversion impact is a big one for us. So I can give you sort of like average stats. 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 go to that same PDPs and don’t interact with the sizing solution.

see a really positive lift there. Return rates, of course, that’s another one that we’ve been talking about that we impact. that’s quite a range. It depends, obviously, on what your initial return 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. But on average, I guess I could give a range, usually around 10 to 25 % return rate reduction for consumers that follow their size recommendations.

versus those that don’t. And then AOV, the average order value, we often have a good impact there. I think that average is around 22 % increase in average order value. again, getting confidence in what size someone is usually can lead to them adding more to cart because they have that confidence there.

Sean Simon (19:57.602)
Are brands thinking about the environmental impact as well? Does that come up a lot? Like in the fact that by reducing returns, you’re reducing emissions and all that.

Daina Burnes (Bold Metrics) (20:06.146)
You know, sadly, I don’t think a lot of consumers have much awareness for the fate of returns. And that’s something that 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 repackaged, reshelved, and at the end, so there’s no real impact. But of course, you as you’re probably aware, a lot of returns

end up being ascent rated, end up in a landfill and don’t make it back onto the shelf. For, because many brands find that it’s more expensive to, you know, read, you know, do the quality assessment and repackage, et cetera, and reshelve it. So that’s obviously got a huge impact as well as of course the carbon footprint of shipping the product to you and then back, et cetera. So it really can.

it’s actually quite a significant impact on the overall global footprint with shipping all this, well, e-commerce and shipping products back and forth from returns, et cetera, 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. So that’s it.

Sean Simon (21:19.917)
Yeah, mean, and e-commerce continues to increase or grow as a sector. then even returns, you probably just can’t keep up with the number of returns that you prevent. You 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, who’s involved from the organizational side from an implementation perspective and management?

Daina Burnes (Bold Metrics) (21:45.955)
Yeah, well, our typical decision maker is usually within e-comm. So usually a VP of e-comm, director of e-comm, something like that, e-commerce manager. Sometimes the innovation folks get involved. Every once in a while, someone from the finance team, because of the obvious impact that we make there on the bottom line, as well as top line with conversions. But yeah, typically the e-commerce team is who we’re speaking with.

We do have, I should mention, a product called Apparel Insights. anytime someone engages with our solutions and receives a science recommendation, makes a purchase, 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, because we have their digital twin details of all their detailed body measurements, over 50 body measurements, relative to what they pop, what they

what products they purchased, if they kept it, if they returned it, et cetera. And so our insights product, Apparel Insights, connects all this information so that actually for product designers can really, for the first time ever, have an understanding of how are their products fitting? 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 body shape and sizing better? Could they expand sizes? Are they missing out on the larger sizes or the smaller sizes? Is it time to add a 2XL or 3XL or an extra small or 2XL? So some of these questions. So this is all to answer your question on.

Sean Simon (23:33.442)
you

Daina Burnes (Bold Metrics) (23:34.274)
Who buys that is typically within the product design or also the innovations teams. within product design, being able to access that information is super interesting and valuable to those teams and typically would be interfacing with people in the product side.

Sean Simon (23:51.322)
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?

Daina Burnes (Bold Metrics) (24:04.11)
Usually, later on. So you get started, we get you in the door, so to speak, 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 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 data-driven view of how

products are fitting.

Sean Simon (24:34.787)
How do you manage the data privacy? You have measurements on people, right? Is there any issues around 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 it?

Daina Burnes (Bold Metrics) (24:47.82)
Yes, we do not have 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. The data is completely anonymized. We pair you with a unique ID for tracking purposes, just like a long set of numbers essentially, so we can track it through to purchase and returns. But yeah, at no point do we ask you any information that would be personally identifiable to you.

Sean Simon (25:18.103)
Okay, very cool. So just looking ahead into the future of the space, like, what do you see the future looking like for fit intelligence, especially with AI becoming more prevalent? Do you see big changes happening? Are there technologies that will or things that will allow your technology to be even better?

Daina Burnes (Bold Metrics) (25:36.301)
Yeah, so well, over the next three to five years, 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 commerce infrastructure. So converging several adjacent categories. So from AI-driven personalization to digital identity, which we’ve talked a lot about today.

predictive merchandising, of course, data-driven product design. And so likely we’ll see fit systems become multimodal and context aware. For example, what climate are you living in? It can help add to its full sort of multimodal awareness of your situation and how you’re living and blending the body data models with style preferences and your purchase behavior. And this convergence will allow brands to move beyond

what size fits towards what fits you, your lifestyle, your preferences, etc.

Sean Simon (26:37.965)
That’s very cool. 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 could be asking them questions that maybe helps you make their sizing more accurate, but I’m sure we’re we’re a ways off from that. All right. Let’s move forward to the final segment of the show, the rapid fire segment. So short answers. Just be real quick about it. Nothing, nothing crazy here. So right now, what is your favorite

Art-Tech tool. It can’t be bold metrics. Maybe something you use at bold metrics.

Daina Burnes (Bold Metrics) (27:11.054)
Well, other than the obvious, Bill Betrick.

Well, okay, well, I will say to plug our partners, we love our partners. Our website has our list of partners. If I had to pick, you know, we particularly enjoy working with loop returns, their returns logistics solution. We have a direct integration with their data feed. So if we have shared clients, if client’s integrated with loop, we can get the…

returns 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 a very complimentary way. So I’ll answer with that one.

Sean Simon (27:48.409)
That’s a good one. I that makes sense too. I love the explanation. right, biggest Mardic myth you’d love to bust.

Daina Burnes (Bold Metrics) (27:55.183)
biggest myth, let’s see, I guess, well, maybe it’s just like a, a startup myth, but like, if, 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, you know, founders often might believe that, you know, a brilliant product, will naturally attract customers and investors, et cetera. And in reality.

even the best technology could fail without the right distribution models, strong storytelling, and really like relentless iteration on customer feedback, iterating on your product, et cetera, and maintaining persistence.

Sean Simon (28:38.285)
Yeah, it’s definitely really hard to make people aware of you when there’s so many companies out there and help them understand what makes you better or different. All one piece of advice for women founders in tech.

Daina Burnes (Bold Metrics) (28:50.412)
You know, I think my advice for women founders in tech would be the same advice for any founder in tech, which is, guess, two pieces of advice if I could give two. One is, you know, create opportunities for yourself. This was a piece of advice that really inspired me when I was first starting out on my journey from one of the founders of Twitter who said, you know, create opportunities, put yourself in places to allow things to happen.

So get yourself out there and you never know what will happen if you create these opportunities for yourself. So, and then my second piece of advice would be, I talked about this word a little bit earlier with the Martech favorite, or I guess with the myth one. Is persistence. starting a company, growing company requires persistence. So stay persistent and keep going. There’s always ebbs and flows and as you start and run a company, but persistence is what gets you through the walk.

Sean Simon (29:48.132)
percent. can’t be shy. That’s perfect. Dana, thank you. This was fantastic. Thanks for joining us and for everything you do to make retail smarter and more personal. You can learn more about Bullmetrics at bullmetrics.com and check out their profile at trustblurbs.com. Thanks for listening to the Mark Tech Matrix presented by Blurbs, the platform where marketers discover vendors they can actually trust.

Daina Burnes (Bold Metrics) (29:50.125)
Yeah.