Earley AI Podcast – Episode 77: Digital Transformation in Retail with Betsy Mello

Guest: Betsy Mello, SVP of E-Commerce Sales at Dorel Home

Host: Seth Earley, CEO at Earley Information Science

Published on: October 8, 2025

This episode of the Earley AI Podcast features Betsy Mello, a seasoned retail executive whose career includes leadership roles at Dorel Home, Levi’s, Old Navy, Sears, and several retail startups. With deep expertise in merchandising, inventory management, and eCommerce strategy, Betsy brings a pragmatic perspective shaped by years of leading digital transformation and managing major marketplace relationships with Amazon, Bed Bath & Beyond, and others.

Host Seth Earley talks with Betsy about how retail leaders can navigate the ongoing shift from traditional operations to AI-driven business models. Their discussion explores how structured data, process discipline, and organizational alignment form the foundation of successful digital and AI initiatives—and why the fundamentals still matter, even in the age of automation.

Key Takeaways:

• How the move from brick-and-mortar to digital commerce has transformed consumer expectations and the pace of retail innovation.

• Why marketplaces are data supply chains—and how brands must adapt content, taxonomy, and product positioning across diverse channels.

• The importance of clean data, standardized terminology, and clear use cases before adopting AI solutions.

• Strategies for breaking down silos, aligning KPIs, and ensuring cross-functional collaboration around data and insights.

• Leading through change: the value of transparency, experimentation, and learning from failure during AI-driven transformation.

• What leaders often overlook when preparing for AI—and how to make foundational data work visible and measurable.

• The building blocks for sustainable AI success: information architecture, governance, and accountable data ownership.

Insightful Quotes:

"You need to have everything standard. You need to have clean data and very clear workflows and accountabilities... The key is having the team set in place and very clear defined processes and roles and responsibilities. It’s incredibly critical to make sure your foundation is correct. You need to be always starting at the basics." - Betsy Mello

"Supply chain is an information supply chain. And every time you have a new way of distributing your product, you have to think about, how do I distribute the data with that product?" - Seth Earley

"Data is a big buzzword right now. Just like AI, everybody wants to say they're using it. And in reality, we've always been using it. It's just how you're using it now, and the volume of it, and whether it's good, clean data." - Betsy Mello

Tune in to hear how retail and eCommerce leaders can turn complexity into clarity—and build the cultural and data foundations that make AI work.

Links

LinkedIn: https://www.linkedin.com/in/betsymello/

Website: https://www.dorelhome.com

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Podcast Transcript: Digital Transformation and Data Foundations in Retail

Transcript introduction

This transcript captures a conversation between Seth Earley and Betsy Mello on leading digital transformation in retail, the critical role of data foundations, breaking down organizational silos, and preparing enterprises for AI adoption. Topics include marketplace complexity, change management, and the importance of standardization and governance.

Transcript

Seth Earley:
Welcome to today's Earley AI podcast. I'm your host, Seth Earley, and today we're going to be speaking with Betsy Mello, who is a very seasoned retail executive who has deep expertise in merchandising, inventory management, e-commerce strategy, and she recently served as SVP of e-commerce sales at Dorel Home. And she oversaw key accounts, including Amazon, Bed Bath & Beyond, and other marketplace partners. Marketplaces are so important these days. And she's also held other leadership roles at organizations like Levi's and Old Navy, Sears, and other retail startups. So in this episode, we're going to talk about the role of structured knowledge, data, content, and organizational change when we talk about AI initiatives. And these are very similar to the other initiatives and the other transformations we've had in the past. So, a lot of this stuff is kind of making sure that we're still doing the same blocking and tackling, and what, you know, what do leaders need to understand, and what do they need to prepare for? So, Betsy, welcome to the show. Thank you for being with us.

Betsy Mello:
Thank you for having me, Seth. I'm really happy to be here.

Seth Earley:
Perfect. So, let's go ahead and jump in. You know, you've had a very broad and diverse career experience, and as you've gone through this, kind of, how did you get to digital transformation, and how did your experiences inform your perspective on what digital transformation is in organizations today?

Betsy Mello:
Yeah, so when I started my career off, I started in brick and mortar, so I started in inventory management and merchandising at the Gap Bank, at Old Navy. And where I… and then from there, I moved over to Levi's, where we launched our outlet business from scratch. We acquired the licensee back from Anchor Blue, and really went overnight from 15 to 115 stores. So that was really my first, you know, step into transformation, but it was still at the brick-and-mortar level. However, you know, as the consumer has moved more digital, my career has followed us thus. So, I, you know, consulted for retail startups, I moved over to Sears, where I oversaw kids and baby apparel and hard lines.

And that included e-commerce, and then most recently, the last 8 years at Dorel Home, overseeing our Amazon account, as well as some other big accounts like Bed Bath & Beyond, Overstock, Zulily, and our Marketplace accounts on these retailers, so I have followed in that direction. So, it's great because I have the experience of being close to the consumer, and really building assortments around the consumer, watching their behavior, and building product around it, whereas I've now moved into the digital piece of it where, you know, you're really going into still what the consumer wants, but it's moving at such a quicker pace, right? And growth has been incredible in the past 5 years in particular.

Seth Earley:
Absolutely, and you know, when you think about marketplaces, that's a huge data play, right? How do you syndicate your data out? How are you not invisible? We can talk about that in a little bit, but I wanted to kind of ask you, you know, so what have the biggest shifts you've seen as e-commerce has grown, what, you know, what has really stood out for you in the past 5 or 10 years. I know there's lots of different changes in technology and the marketplace, and customer behaviors and so on, but what are the big things that have been kind of, you know, large shifts that you've witnessed?

Betsy Mello:
Yeah. Well, I think there's a lot more noise, right? The consumer has so many more places to shop than they did before, and, you know, it's increasing where it's not just with the retailer, right? You can also shop socials, you can shop so many places, you can shop on your phone. Like, the transition has been incredible, and it really means that everything is moving quicker, but there's also so many more data points, and it's just so much more complex, because if you think about it, you're probably selling the same product, but you're selling it through multiple channels. Each retailer has slightly different requirements, terms, things like that, so you're really just dealing with so many different channels. And that can make it incredibly complex, even if you have a very simple product.

Seth Earley:
Yeah, and I think that people don't realize that, you know, when you are a manufacturer and you're working through lots of different distribution channels, those are all channels for moving data, right? A physical supply chain is an information supply chain, and every time you have a new way of distributing your product, you have to think about, how do I distribute the data with that product, right? If you're selling off into a big box retailer, Amazon, they're going to have a different need than Target. Maybe they'll have different attributes, different values, different taxonomies, all of these different things.

And then how do you differentiate while you're in that marketplace, right? How do you expose yourself in that marketplace? And then, again, as you multiply these channels, there are all nuances, so it really does become a very, very complex information landscape, especially when you're trying to take those signals back from the consumer. And you're disintermediated with a distributor, right, when you're selling through a big box, or, you know, you're not… you don't have that direct customer data, but you still need to take the signals from the marketplace, and you still need to take the aggregate signals coming from your distribution channels, and somehow use those, right, to change a behavior, to change a presentation of product, to change the data, to change however you're showing up in that digital ecosystem.

Betsy Mello:
Exactly, and a lot of times you, you know, even if you're going back to selling a simplified product, because of the requirements of different retailers and how they want to speak to their consumers, you actually may have to develop different product, you know, at different pricing, and it just makes it so much more complex, and that's what's really hard, is you don't want to lose your brand values, and you don't want to lose what you are trying to sell the consumer, especially in a B2B marketplace, that it just becomes so much more challenging. The margin requirements being vastly different between several retailers can really affect how you develop products and how you show up to the consumer. And that's a big challenge.

Seth Earley:
Yeah, yeah, no, it sounds, it gets really complex. So, at a recent industry event, we talked about the idea of activating data, right? To get business value, so, you know, how are organizations and leadership teams, how are they thinking about data? And, you know, even if you take AI out of the mix, but the data is the foundation to all of this stuff, whether you're doing personalization, recommendation, search, you know, catalog operations, it doesn't matter what it is, right? AI is in there, but how are people looking at the data? Are they thinking more about the strategy, or are they getting in the weeds? Are they rolling their sleeves up? Are they still giving it lip service and saying, yeah, data's important? Yeah, nodding their heads, and then when they see a budget, they go, oh, it's not that important, right?

Betsy Mello:
Right.

Seth Earley:
What's your sense of what's happening in the leadership suite?

Betsy Mello:
That's a great question. I mean, I think it's interesting. Data is a big buzzword right now, right? Just like AI, you know, everybody wants to say they're using it. And in reality, we've always been using it, right? It's no different. It's just how you're using it now, and the volume of it, and whether it's good, clean data. That's the thing. You have to have a really clear view of what your end uses. What is your goal? What are you trying to solve? What is your use case?

You don't want to let the data, just raw data, drive how you run your business. You need to understand what you're looking for, and then be able to filter through and say, okay, what can I do in addition to what I'm doing now? What can I glean from this extra set of data, or what is this telling us that we didn't tell us before? I think that's the big difference. And additionally, having experts that can truly understand it and tell the story for you, because raw data doesn't help anybody, right? You need to have a team that's able to synthesize and analyze and tell the story from the data. That's truly what running a business is all about, right? So I think that… that is a big piece, that a lot of companies want to use the buzzword and say they're using it and use all these big fancy words, but the reality is, what are you trying to do? What is your use case?

Seth Earley:
Knowledge graphs are another big buzzword, but… and AI is a buzzword, and machine learning, and now agent technology.

Betsy Mello:
Later, yeah.

Seth Earley:
But I think what we've talked about before is the fact that many companies are still missing a very solid foundation, standards and… and so on.

Betsy Mello:
Well, and I think the other challenge is, in addition, how each team is using it. So, you want to have, at least from leadership, a common set of goals and data that you want to use. So, if you have different teams using different sets of data, and it's coming from different sources and they may not match up, that's a challenge. You want to make sure that you're not working in silos, and that everyone's working towards the same goal. I mean, in the end, right, I'm in retail, it's sales. We all want sales, but how do we get there? What do we learn about the consumer from one end with marketing? What can we learn from our retailers and our sales teams? From, you know, conversion and product development timelines and things like that. Like, it still all counts, even if it's not direct from the consumer.

Seth Earley:
Well, sure, and of course, we still have the problem with data silos in different departments, owning and curating and managing our own data, or being, you know, being territorial about it. What are you seeing in terms of your, you know, when you… you would have to work with teams that would work in different channels and have different sets of needs, and how were… how were you able to break down, or were you able to break down silos in terms of data, and how would you go about that type thing, that type of thing?

Betsy Mello:
Yeah, no, that's a, you know, that's a really interesting question, because the way my current company, last company was set up, was, sales teams were very siloed, which makes sense from a retailer perspective, but when you're all working towards the same goal, you want to be using the same KPIs and languages, right? So, obviously, with Amazon, it's very heavy marketing, traffic-driven, it's going to be different from a store account, right? But when, you know, leadership is hearing different terminologies and KPIs and goals. While the end goal is sales, you know, you want to be able to compare. What's conversion like on us versus Wayfair versus Target, right? Like, that makes sense. You want to understand across the channel where you're getting paid back for your marketing dollars.

Seth Earley:
Getting a win.

Betsy Mello:
That consistently, yeah, it doesn't help. So, you know, I was many times bringing to the table a lot of terminology that wasn't being used elsewhere, because maybe it wasn't applicable, or it just wasn't top of mind, which then just bounces around questions and causes a lot of, you know, could cause a lot of inadvertent spin, when in reality, we're still working towards the same goal.

Seth Earley:
Sure, and I think it's… it's getting alignment in terms of the goals.

Betsy Mello:
Right. The objective is to say, okay, how are we getting...

Seth Earley:
How are we aligning these goals? Are these the same goals? Are they similar goals? Are they adjacent? Are they dependent? And then, how are we measuring these? So, can we get aligned on the objective and the measurement? And look at the greater good, so to speak. I like to say...

Betsy Mello:
Absolutely.

Seth Earley:
There's no budget for the greater good, but there is, you know, an incentive for leadership to say, how do we work together? And if we can't get the terminology straight, that's actually a foundational problem. We should invest in that. So, what do you think, you know, organizations need to do in order to prepare themselves, or to set themselves up for AI? What are the, you know, what are the foundations?

Betsy Mello:
I mean, I think being open to it is the first thing. Like, I know that sounds so rudimentary, but you need to be open to it. You need to understand, like, if you haven't been doing something, that's okay. But I think you need to, you need to have everything standard. You need to have clean data and very clear workflows and accountabilities. You know, one of the things that we did at Dorel was we really cleaned up our product data management. We were in 4 different systems, and we consolidated them all into one. And it was a 3-year process.

But it was incredibly critical to make sure your foundation is correct. You need to be always starting at the basics, right? And so, you know, that's… I think that that is not something new. That is, quite frankly, what you should always be doing, which is making sure you have very clean data. Make sure you have clear workflows and accountabilities. You can't have people stepping on each other's toes, or not being clear of who's owning what in the process, and having documented workflows is really critical.

And then the other piece that I think is really important is having the right team that can understand it and use it, and tell the story. Not everyone's going to jump on it right away. And that's okay. But you know, you need to have, you know, that support mechanism in place to help people get there. The key is having the team set in place and very clear defined processes and roles and responsibilities.

Seth Earley:
That's great. And I think those fundamentals are, you know, they're unsexy. They're not sexy, but they're really, really critical. And I think, you know, a lot of the organizations that I work with, when I talk to them, they say, well, you know, we want to do this AI thing, and we want to do that. And I say, well, do you have a taxonomy? Do you have an ontology? Do you have standardized data? Do you have clear ownership? And they're like, well, no, but we want to do the AI thing.

Betsy Mello:
Right.

Seth Earley:
And it's like, well, you can't do the AI thing without the fundamentals. You can't build a house without a foundation. And I think that's the message that needs to get out there, is that, you know, the blocking and tackling, the fundamentals, those are what enable the AI initiatives. And if you don't have those, you're going to struggle.

Betsy Mello:
Absolutely. And I think the other piece that's really important, too, is understanding that it's going to be messy. Like, it's not going to be perfect. You're going to make mistakes. You're going to have to iterate. And that's okay. I think that's part of the process. And I think that, you know, being transparent about that and saying, hey, we're going to try this, and it might not work, and that's okay. We're going to learn from it.

And I think that's really important, especially when you're leading through change, right? Like, you need to be transparent. You need to be honest. You need to say, this is what we're trying to do. This is why we're doing it. And here's how we're going to measure success. And if it doesn't work, we're going to pivot. And I think that's really critical.

Seth Earley:
I totally agree. And I think that, you know, the transparency piece is huge. And I think the other piece is, you know, making sure that people understand the why. Why are we doing this? What's the benefit? What's the value? And I think that's where a lot of organizations struggle, is they don't articulate the why well enough. They just say, we're doing this AI thing because everyone else is doing it.

Betsy Mello:
Right.

Seth Earley:
And that's not a good reason. You need to say, we're doing this because it's going to help us improve conversion by X percent, or it's going to help us reduce costs by Y percent, or it's going to help us improve customer satisfaction by Z percent. And I think that articulation of the value is really important.

Betsy Mello:
Absolutely. And I think, you know, another piece that I think is really important is making sure that whatever you're doing is measurable, right? Like, you need to be able to show, here's where we were, here's where we are now, here's where we're going. And I think that's really critical, because if you can't measure it, you can't manage it. And I think that's something that a lot of organizations struggle with, is they don't have clear metrics. They don't have clear KPIs. And so they can't really tell if what they're doing is working or not. And I think that's a big miss.

And I think the other piece is making sure that you're sharing that information across the organization, right? Like, if you're doing something in one channel or one department, and it's working, share that with the rest of the organization so they can learn from it. And I think that cross-functional collaboration and knowledge sharing is really critical. Making sure it's measurable is really important.

Seth Earley:
Absolutely, couldn't agree with you more, and I think that is something that certainly the executives need to understand, is what's the baseline, and what are we improving? But then understand, well, what did it take to do that? Even if, again, you have to show them some scary diagrams, they may tune out, but they need to understand that there's a lot of work that needs to be done under the covers to do these things. So being able to see that, and then being able to, excuse me, see those results. Most executives don't look for their own information. They'll pick up the phone, or they'll send an email, or they'll ask their assistant, right? They don't necessarily search and retrieve.

Betsy Mello:
True.

Seth Earley:
You have to start getting them to use this stuff, and if you can make it conversational, you can build a conversational assistant for the executive to run a report, or to get certain things that they ask for over and over again. But again, that's kind of a tangible thing, it's visible progress, but then the metrics are super important, and making that invisible work more visible.

That's great. So, looking ahead, as you see the next step in your career, what are you excited about when it comes to the future of AI, or future of retail, or future of e-commerce, or the future of Betsy Mello?

Betsy Mello:
Yeah, oh, thank you. So I think, you know, obviously, I'm excited to see what… where AI continues to go, because if you just take a step back and say, where were we 5 years ago versus where were we one year ago and 6 months ago, it's moving so fast. It's… and, you know, and people are learning how to use it better. It's… I think it's… what I really love about it is it's so helpful for the consumer. Like, I'm using it all the time, personally, right? It's helping me do all kinds of things at home and in my business.

But what I really love about it is that it's just become a widely known way to prompt, and it's changing the way we think. And that's what I like, is it's not just, do this for me, but what do you need to give it context for? And I find that in business, context over my career has been so important, because if you're thinking about the consumer, you know what you're looking for, but having that context and sharing why you're doing this, or hey, we think this new idea would be really important for X, Y, and Z reasons. I really love what AI is doing in general for that, and I think that's super exciting.

I think the other piece I'm excited about is that, you know, we could… the world just continues to become more global, right? Like, as information grows and sharing, we have visibility to so many more things than we did a long time ago, and even recently, it's changing the way we source, and it's… I just think it's… it's interesting because you're… you're getting a lot more variety in what you can sell and what you can share in the United States and globally, and I'm hoping that there's just a lot more alignment around that going forward, and I just, you know, I think that at some point, the noise becomes too much, and you start to focus down, and where do you want to shop? Where do you want to look, where do you want to, you know, where does your life become?

And I think that that's going to be really interesting to see how people react to that. You know, I mean, obviously, people have been going back to stores a lot more since COVID. They're changing the way that you experience stores, and showrooming, and just so much more interactive based when you're in there. It's just… it's not just product anymore, it's about the entire experience and how you can make yourself different, and I love that. I just love the way that we're continuing to show up to consumers differently.

Seth Earley:
That is the exciting part, because it is, as you say, it's not just about the product, it's about that experience, right? Who are you? What do you sell? What's the product for me? How do I buy it? How do I use it? How do I get the most value from it? And what's that entire customer life cycle and lifetime value, and then what we're trying to do is be able to anticipate those needs, just like a personal shopper would be, right? So we didn't even get into, you know, agents, but there will be shopping agents, and shopping agents should own your metadata and know who you are, and not necessarily be freely shared, right? So, there'll be a lot of interesting things. We'll have to touch on that another time. Betsy, this has been great. Thank you so much for your time today. I appreciate it.

Betsy Mello:
Thank you.

Seth Earley:
Well, Betsy, thank you again for joining us and sharing your experience and your perspective, your insights on how enterprise leaders can deal with this change. It's very, very important. And to our listeners, stay with us, and come back, and we'll continue to explore what it takes to make AI work in the enterprise. Thank you again, Betsy. Appreciate your time.

Betsy Mello:
Loved being here, thank you so much, Seth.

Seth Earley:
And thanks to our listeners, and thank you, Carolyn, for your work behind the scenes. We'll see you next time.