S4E4: Matt Christensen

Welcome to Commerce Famous, a podcast that celebrates the celebrities of our industry. Join us for a conversation with the taste makers in ecommerce. In their daily lives, these are just normal everyday people like you and me. But to us, they are commerce famous. Hello again, and welcome to season four, episode four of Commerce Famous.

This is a podcast dedicated to those people who contribute materially to the world of ecommerce, and there is no hall of fame. So this is our way of recognizing some of those key contributors. Joining me today is Matt Christiansen. Yes. The Matt Christiansen.

Probably the one and only. Matt, thanks for, joining me on the pod. Thank you for having me. Excited to be here. Yeah.

Let's, let's bust right into it a little bit. Why don't you introduce yourself? Obviously, you're, the founder and president or one of the founders of Distributor Data Solutions, DDS. Why don't you introduce us to both yourself and the company, and we'll, go deeper. Yeah.

Thank you. Yeah. Where do I start? Yeah. President, cofounder, we founded DDS, a little over eleven years ago.

We started in the electrical distribution industry. My business partner owned a electrical distributor. We were one of the first to launch an ecommerce site, and we saw firsthand the struggles of dealing with product data with large catalogs. So we had 350, 400,000 products that we wanted to sell. We were selling in in stores, branches around, this part of the country, and we just had ERP data.

We did not have any of the stuff we have today. That's probably fifteen, seventeen years ago. So trying to get images, trying to get descriptions was just the basic. And so we hired a guy, and his only job was to copy paste from manufacturer site into a spreadsheet and upload those into the the ecommerce site that we had. Super manual, very slow, error prone, and it took him a year to do 11,000 SKUs.

And so we still were short, you know, 300 and something thousand SKUs that we wanted to sell. And so, we just figured there's gotta be a better way to do this. So we started a company to do this. Fast forward, we are in, traditionally in any of the wholesale industrial, electrical plumbing, HVAC, industrial supply, gas and welding, any of these kinda related wholesale, b to b fields where large catalogs, scale's an issue, and we solve the the scale product data issue. So we sit in between manufacturers and the distributors, software companies as well.

We're starting to see a lot more software companies in these in these spaces. And, yeah, that's us. Fantastic. Well, so talk a little bit more about why distributors need a service like DDS. Like, what do you guys do that doesn't naturally come as part of having a PIM or having a commerce solution?

What is the mess you are cleaning up for them? Yeah. Yeah. I think the driver of medium, smaller distributors typically is ecommerce when they're coming to us. Typically, they've tried to do the product data themselves or they they've tried somebody, another third party or scraping company or something like that.

It it's really just the scale. It's the time. And so even a medium, small a small distributor probably sells 20 to a 100,000 SKUs from a hundred, two hundred manufacturers, that data from each of the individual manufacturers is different. It's stored different. We just I think the number one reason is in The US and Canada, we do not have data standards.

There's not a certain format that all the manufacturers have to adhere to, and so they are all a 100% different. So if you took a 100 manufacturers and got data from a 100 of them, a 100 of them would look different. So to to get that data, to categorize the data, to normalize the attributes and values, to enhance the short description so that they're search friendly, posting all the assets, making sure that could be a one time thing, but the data's always changing. So about 25 we have about thirteen, fourteen million SKUs in our catalog right now. About 25% of it changes every year.

So then there's the maintenance issue. And then on top of that, there's the integration issue. So okay. So you've got the data, you've got it in a spreadsheet or CSV or multiple, whatever you've got. Now you've gotta try to get it into your pin.

You've You've gotta try to get it into your ecommerce platform. You've gotta try to get the images in. You've gotta then categorize it. So it's all the it's it's all of those things that can be manual work, that we are doing on scale. But if you look at the bigger distributors, they need the data for the data for everything.

Typically, we're delivering into a PIM and that and also ERP. They wanna know what the new product introductions are. They want more normalized data in the ERP. And so the larger they're typically selling millions of SKUs from thousands or tens of thousands of manufacturers or brands. So the bigger they are, the bigger the problem.

So we see very large enterprise, multibillion dollar distributors that they've got a bottleneck of skews, millions of skews that are still not categorized. And so if they're not categorized, then they're not normalizing attributes and values. They're not enhancing the the short description or name, and bringing in some of those values for search. And so they're not even selling those online. They're just sitting there waiting to be sold, and so the lost revenue is is massive.

So I'd say overall, it's the scale and the speed. Commerce famous is proudly Commerce famous is proudly presented by Shopware, the leading open source e commerce platform for mid market and lower enterprise merchants. More than 50,000 clients already process over 25,000,000,000 in annual GMV through Shopware. Find out more about Shopware and the best value in e commerce at shopware.com. Got it.

Take me back to the in the way back machine to what life was like eleven years ago before you and your partner started DDS. You shared a little bit about your first experience and I love that, I want more of that. You put human middleware on a project, they got roughly 3% of the project done in a year, you knew that wasn't gonna scale. Yeah. Tell me about your second customer.

Tell me about what life started to look like when you realized you had a software solution here to solve a technical problem. Yeah. It's it's it's a funny question. I think that when you when you start a software company before you build anything, you have all kinds of ideas. Yep.

And and you realize over time that the ideas are are the easy part. Elon says it. Right? Anyone can have an idea. The hard part is building something.

So the same thing is in software. So when we pitched to early customers, we had, hopefully a a search product. We had we were gonna build quotes. We were we were gonna solve all these problems with product data, through the distribution side that was not built. It was a screenshot of here's a product and a, terrible name of what we were calling it back then.

And it was just vaporware, but we're trying to sell this whole kinda suite of things. And, you know, in software, you do, that you're always building a a year ahead, typically. I I think it's a lot faster these days. But when you're building something back then and you're just starting out, it takes so much time to actually build something before you have something to actually sell and deliver. So you're trying to see back then what's landing, what what what's what is the interest, and it really was just the data.

And so it was having more of a single closer to a single source of data for all of these major manufacturers, and that was, back then, we called the product eData. Everything had a e in front of it. It's how how terrible we were at marketing. So, yeah, it really just ended up being the data, and then it and then as as we grow, that's great. We have the data and we can deliver the data.

But then what are people doing after? What are what are the manual portions of the workflow that they're still doing? Well, they're still categorizing manually, which, you know, before AI was it took a lot of product knowledge or really good data within the product and they still would get it wrong. That's step one. Then they've got to typically, they will, try to standardize or normalize the names of the short description.

Then based on what bottom level category it's in, they will try to, do category attributes and normalize those attributes, have required, names and values. And and so so much of this was still very manual and happening with within the PIM, especially the the big guys. So we've tried to step into and take a look at all of the things that they were doing to slow down products from going live, products from being published and being sold ready. And that's what we've tried to do over the years is take more of that on. And, you know, thankfully, AI has we we can do some of that stuff in days that used to take years.

So that's the cool part. Fast forward today, probably have more early on stories, but it was pretty fun. Keep putting you back, so don't worry about it. Had, like I'll have to find one of the I know we have these. One of the PowerPoints that we originally shared, it probably had six or seven, Phantomware products on there.

The, only one we've ever done is the data. Only one that people ever really wanted. So Well, that's the key. The key I mean, so I obviously, we're in the shop Shopware is an is a software business, so we understand what you're talking about in terms of looking for product market fit. Yeah.

And maybe maybe you were just lucky that you found the best one first. We and we knew that one. Right? That's the idea of why we started the company because we knew that was the hard part. You couldn't do anything else without the data, and that's still true today.

I think you asked about our second customer. The second customer, we they were they were really good. They had development team, and so we we built API first because we thought, you know, a, we wanna be able to control the data. B, we want to give them we won't we don't want them to have to store all of this data, all of these assets. I mean, it's today, it's in the thousands of terabytes.

It it's pretty crazy, the size of of the digital stuff. But, anyway, we had a our second customer integrated to our API, so they didn't have to store, really any of the data. In fact, they used our API for search for their site search. That was seemingly a good idea. I think anyone who's doing search knows how difficult that is even with good technology, because you've got to have transparency and you've got to give the the customer an interface where they can tweak searches and, float things that they wanna float up to the top, and we just didn't have that.

So we were constantly getting requests on, hey. The search sucks, and, you know, why doesn't this show up when somebody types this? And I think we quickly realized that the search business is not at all anything we wanted or, wanted to be in. Now improve the data, enhance the data, normalize the day all the stuff that, you know, we're doing now greatly improves search, but trying to be in the search business was bad business. And so we quickly, as anybody does, any company, you you you try things, but you've gotta be really careful when you're young to not do too much.

Stick to your core, be great at your core competencies, and not try to get too diluted or else everything is just kind of mediocre. So over the years, we've done a really good job of learning from some of our early lessons, and, we had we just had some great customers that were that were really willing to, try things with us because we're really kind of the only one doing this at scale. So some interesting learnings. Well, I want to go back a little bit as stay in this theme of looking all the way back. I mean, it sounds like you made some really smart technical choices around being an API first company.

That's a really good decision. It found it sounds like you found your product market fit even back then when you were trying to develop solutions. Yep. Why distributors? What was their what was about what was the situation for a distributor versus a classic wholesaler that was different and unique enough that you dedicated the company towards that, at least in the name alone?

It's what we knew. I was I didn't mention this part, but I was head of IT for my business partner for ten years. So we knew that we knew the industry, we knew the need, we knew that nobody else was doing it, well, no one else was doing it at all, so there were some scraping companies back then, but, yeah, we knew the need was there. I think what we saw was we were early. We we knew the need, but the the demand, for the big guys was definitely there.

But for the medium smaller distributors, there's still thousands of them in our industries that are still not even online. They're not doing ecommerce still, which is still thing. Thankfully, companies like shock Shopware coming in to to help us out. Super excited about that. We just had a lot of I've told you this.

A lot of mom and pop kinda companies. No real proper software companies were paying attention just because it was just behind. But distributors, they they have a big problem because and still today, they just sell a lot of products. This isn't like retail where you can have an amazing catalog with a couple 100 products. Nobody is selling under I don't know, probably 10,000, 20,000 SKUs.

Nobody. Even a small, even a small distributor. They're just not. And so they they cannot do this themselves. Many of them try for a while, but it's just not sustainable.

So they need help. And, you know, we know they need help, and that's just that's just, what we've done. So Talk talk a little bit about one one of the things that I love about the distributors is they're commercially minded. They are they are very much in the business of moving high volumes of orders and units, dealing with massive catalogs, lots and lots of complexities. But the other thing that's really interesting is they live on pretty razor thin margin.

They're Yeah. Inherently a middleman. Yep. So talk a little bit about how you build a product to serve the distributors and also how do you price it because they live on such razor thin market. You don't have to get into, you know No.

No. Super soft. Yeah. Happy to talk about it. It's, we we price subscription, and we price based on the size.

And so we're trying to give the the small guys an advantage, trying to help the the massive guys scale, and and it's really based on kind of their overall size. Because what we know is that's really the same thing as the number of SKUs or the number of manufacturers, the amount of complexity, the amount of systems that we're gonna integrate into. So, it makes sense for us too. So, yeah, we're meeting them where they are. But, yeah, I think, the the medium smaller, especially, it's it's why they're still not online because they don't wanna waste money.

They don't know how to get online. They don't have partners that they can trust. There's a lot of mistakes that have been made in in some of these industries, some companies that aren't really here anymore. So, yeah, they they don't have a lot of extra. The big guys do for sure, obviously.

I mean, the the margins of the multibillion dollar guys are they've got plenty of money. But I think it's especially true with the smaller distributors, medium size who are still not online. They just they they don't have the people. They don't have maybe, even if there were good third party consultants, agencies. You guys have the right business model to recommend agencies and SIs and, that can really help not just get them online, but help them grow and turn that into a profit center instead of just, an expense, which has kinda been the the reputation, especially in terms of ecommerce, I think, is is I don't know.

What do you what do you think? What are you seeing? I see the same. I mean, ultimately, you know, one of the things I'm most excited about is it feels a little bit like distributors for the most part have been given a buffet of old technology to choose from. And and the and and the more modern technologies kinda came and originated in things like b to c that are used to charging a revenue share.

Well, that doesn't work in the world of a distributor. And so the the distributor's choices are old tech or modern rev share, and neither of those are great outcomes for that kinda distributor use case Yeah. Which is why Shopware is so excited about this space because we can price, package, and deliver differently, and it's why we're so excited about our partnership. Yeah. But you but you said something a little bit earlier around what's available to distributors of different sizes.

And, you know, the S and B ones are are largely ignored by the market. The mid market ones, have a lot of potential, but again, it's it's it's tough sledding and the biggest ones have as much capital as they need to do kind of whatever they need. We both share a handful of all those customers. One of the things I was you taught me a lot about the power of these buying groups and these consortiums that really level up some of the smaller players. Can you share a little bit about that?

I don't want you to give any, you know, g two up on your on your unique offering, but what can you share about that? I don't I don't think our offering's really that much different. We just try to package it and, try to give favor to the groups that are really trying to help the distributors and then also their manufacturer partners. But, yeah, I think I think one one thing is the the distributors are looking to the buying groups, the associations to make recommendations to do some of the vetting. And like you said, there it historically, has been industries of lower tech.

And, and so there there've been kind of this mindset of like eCommerce platforms coming in and just getting a distributor online and then just kind of bailing. And so how do you, how do you help them grow after they're online? That's not the goal. The goal is not just to have a site and, you know, show products that the goal is to make money. The goal is to be more efficient.

The goal is to give your customers what they're asking for because we all like buying online. So, the the buying groups can bring in vetted solutions and, negotiate some pricing discounts with the software vendors, to make it affordable. And then they know their distributor members better than anyone. They're they're working with them all the time. So, they're they're all a little bit different.

Like, we're we're we work with several groups in in The US and Canada. And, you know, we learn something different all the time with the buying groups. But I think the main thing they're trying to do is bring value to their members through whether it's software companies or or whatever, types of outside companies coming in and helping these guys. And I think the cool thing that we haven't been able to do is the data, the platform, the agency partner, whether an agency partner is an integrator or not. That's that's the solution, and that's what the buying groups need.

They need not just DDS. We're we're the data piece, but we're nothing if we don't have anywhere to deliver the data. And that's, you know, that's what the group needs. That's what the manufacturers that participate in the buying groups. That's what they need.

And and that's obviously what the distributors need. They need package solutions that will actually help them grow, not just be a a site and an expense. And and so I think that's what we see with the the the future of the buying groups. And and that's what we're bringing together. So I love it.

Well and if you think about, let's just call it David versus Goliath, Goliath gets unbelievable pricing and offerings and service levels and support, and then David gets virtually nothing because he doesn't have the economies of scale. So these buying groups in a way kinda level the playing field to give the smaller players access to world class, which they couldn't afford on their own. So, I I really like that that's DDS's core strategy, and and we're trying to copy as quick as we can and and be part of what you're trying to build. So, I thought it was worth stopping there just for a second. Alright, Matt.

Let's talk about the topic that every podcast related to technology hits, and that's AI and Agenca Commerce. One of the things that you said early on, is that kinda everything originates the how well your data is organized and how well you can use it. Does ZDS have a point of view on what's gonna happen with AI, and and how are your customers talking about it today? Yeah. Yeah.

I think for us in terms of of the data, it's it's what we were talking about earlier. It's using, and we've been doing this for a couple years, lot of lot of research and effort into this before we ever had a product. Right? It's kinda the same thing with software. You've gotta spend a lot of time.

And then as fast as AI is changing, and then which which models are better for certain things, are not actually better for data. So we we looked at it, like, where can we where do we start? Where can we have the biggest impact? And we just looked at the same thing. So we've got the data.

We've got the integrations into the platforms or PIMs or ecommerce or or wherever. Like, we have, like, 400 plus integrations into b two b distributors. So what what are they doing with the data? So the number one thing was categorizing the products into their category structure. Cool.

Let's use AI to solve that. So that was the first thing we did. And then what we saw so we could just do it in mass scale. So a distributor would upload their taxonomy. We would take their catalog, and we would categorize anything that was uncategorized.

And within our platform, within Acadia, they could approve or deny. So whether they like something, obviously, they they approve it, deny it if it was bad. And then we're taking that feedback obviously back, into the model on how do we how do we improve. So and then once they have confidence, what what we saw was they're just bulk approving. It's better they can do than they can do.

It's faster than they can do. And so I think our our approval rates on everything we're doing with AI, and we track this and go through this every month, like 99% plus approval rate. So you're we're doing something that would have taken, depending on the size, months, six months, three months, twelve months. Then what we saw, we saw distributors that did not like their category structure. So they're like, Hey, can you take our products and create a three level, two level, whatever level category structure and use AI to create that based on our products?

A lot of these distributors, their category structure has been, kinda pieced together over years. Some were done by product experts, some were done by data people. And so, we just did one that was it was like 1,200,000 products where we took their products, reversed that, and then created their taxonomy. And, it it would have taken them, in their words, years to do that. Brand new structure.

They they reviewed it. They made zero changes, and then we pushed it, to production with all their products categorized to that. So that was number one. Then we started looking at, you know, what do they do next? Next typically were the names, short descriptions, to some type of specification.

That's what we did next with AI. So that all our products, no matter which brand, no matter which manufacturer, all those that are primarily used for search, all look the same. Next, attributes. So specification attributes, specification values, normalizing those across all their products, for comparisons, for left hand refinement. You get into the bigger distributors that have category attributes.

So they're attributes based on the bottom level category, doing that with AI. Then the late then there's a lot of other things. There there could be search keywords, but what else are they doing that we can solve with AI when we deliver, into their platform? The latest is a problem that DDS had, is what do we do if we can't get the data from the manufacturer? So distributors paying us for their subscription.

In electrical, we'll typically have 95% of their SKUs, right away. But then there's always a certain percent, and in other industries, it's a lot higher. It can be up to 30% that we don't have. And so what do they do if if we don't have those? And we can't get those from the manufacturer, and the manufacturer can't get them to us.

Maybe it's buried in spec sheets or or whatever. So we just launched our AI product creation where our customers can create full SKUs to our specification with just a manufacturer name, catalog number, and a short description. So they can complete their catalog. We can deliver those products in days, where before, if we were waiting on manufacturers, those could take months or longer. And so, yeah, we're just looking at what what are our customers doing with the data after we give it to them that we can use AI to, eliminate their manual efforts or eliminate other third parties or, manual processes.

So that's where we're at. It's all around the usability of data and what can we do to improve the quality and improve the usability. I love it. Well, you're such a taking center. I think future, like, the agentic commerce and things that are being talked about, it's gotta be highly normalized.

That data's gotta be highly structured, highly normalized, and that's something we can do with AI so that it's agentic ready. Well, I really like how much of a pragmatic view you've taken to the the world of AI. And and I we took a very similar approach, and we say there's a lot of blue collar work being done by white collar people. And what I mean by that is a lot of cutting and pasting and editing and stuff. So what I heard you say is you've went from being kind of creators and and and the like into just approvers.

So you do the most of the work. They audit it. They approve it, which is, you know, 90% faster, which I love. I think it's a really pragmatic way. Yeah.

Yeah. We wanted to we we that's what we've tried to do over the last several years is really give give our customers control over the data, editing, modifying, enhancing, just the transparency because it's very difficult when you're you're you're delivering so many SKUs from so many manufacturers. Like, in the beginning, we were just kind of a black box where we're doing all the work behind the scenes, and then we're just delivering files. Those days are gone. And, so, yeah, trying to trying to give them what they want.

Yep. Well, so one of the things I love about this segment, these distributors, is, again, they live on thin margins, high volume. So it's really a supply chain issue around how do we become more and more efficient and find the efficient frontier. And everything you just described about what you're trying to do is kinda do more with less is ultimately what you're trying to do, which perfectly aligns to the segment you're you're you're serving. Yeah.

I think we all are. And I think we're all looking for, you know, how do we not only use AI within our companies to make us more efficient, but the harder part really is how do we use AI in our products to make our customers more efficient. And so that's we're we're trying to do both. It's easier to start with your your employees and, you know, use AI to, make your email sound better, however people usually start. But putting it in your product to make your customers, you know, months and months and months of, of work saved every year, that's pretty cool.

It's it's pretty rewarding, and it's I think it's cool that we're actually doing it where a lot of companies are still just talking about it. That's right. Well, so let's let's, let's end on something that I think is a really important point. It's not only do you have to deliver value, but the people who receive it have to be able to justify its value. It either made them money or save them money and it rarely anything in between.

So how do people look at the work that DDS does and the company you've created and how do they justify it in terms of making or saving money? Yeah. Good good question. It's it's easy to see and justify in the beginning because if you look at distributors, there's such a gap in let's let's say they're trying to go online and they've never been online and they have zero products in ecommerce format. So we can give them the products to sell online.

That's that's quick, quick value, a matter of months. You have bigger distributors that are coming to us where they're looking to do a lot of the the normalization, improving their data quality by adding pieces of data. They've got very strict specs that they're trying to fill, so we can we can fill that very quick value. The the harder part is the long term value, the value year one, year two, year three, year five. And so we've broken it down into using the data as less returns.

What what is that value based on, market research and the average market? There's there's stats out there. So based on how many products, based on how much revenue, here's what you're saving in returns because of accurate all the product data. Here's what you're saving in terms of, manpower in updating the products. Here's what you're saving, in terms of keeping customers coming back because you have all the products on your site and you have all this data normalized so that when somebody searches for a product, they're finding the right products.

So that's all in data. So we just broken it down into the top value drivers in looking at the savings every day. That's more of the longer term, what they're saving. If they're adding to their catalog, they're adding new segments of products, That's definitely revenue generation. So, yeah, definitely those.

There's there's kind of the third part. There's the the risk. What is the there's there's time, money risk. There's risk of, having your customers go to a competitor. Right?

If your customers are leaving you and they're shopping on, Grainger, they're shopping on Amazon for all the easy stuff. They probably didn't find it on your site or maybe the data wasn't there and they couldn't find it through the search. So that's the third piece of the the risk in keeping your current customers as well. Love it. Love it.

Well, Matt, we've hit the hit the time limit. Thank you so much for introducing our audience to DDS. It's a an amazing company that drives an unbelievable amount of value. And, man, you've dedicated your life to solving an issue that distributors kinda live every day. So thanks for joining us on the pod.

Thank you very much for having me. Really appreciate that.