In today’s episode, my guest is Peter Szalontay, the founder and CEO of Data Milk. Data Milk is a no-code UX optimization tool that helps to build AI-driven UX optimization for Shopify brands. Their platform is a great starting point for growing revenue over a short time because it does not require any technical resources. Data Milk focuses more on navigational queues and optimizes one’s experience on the Shopify site.
For more information about Data Milk, tune in to today’s episode!
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- What is Data Milk and how do they help Shopify brands?
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This episode is brought to you by recharge the leading subscription management solution, helping e-commerce merchants of all sizes, launch and scale subscription offering. The subscription market is predicted to grow to nearly 500 billion by 2025. That’s a billion with a B, and as the fastest growing area in commerce, subscription holds tremendous opportunities to build a community of customers who share your values.
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Turn transactions into relationships and experience seamless subscription commerce with recharge. Get started today with a subscription payment solution trusted by over 45 million subscribers worldwide. You can check them [email protected]/fastlane.
Hey there, it’s Steve Hutt and welcome back to season five of e-commerce fast lane.
Now this is your first time listening. This is an e-commerce show where we have honest and transparent conversations about building and thriving with your store powered by Shopify or Shopify. Now if you’re an ambitious, lifelong learner, which you likely are since you’re here today, you’re definitely in the right place.
Now, new episodes are available twice weekly with your favorite podcast players, like apple podcast, Stitcher, Google play, Spotify, and many more. You can also stream current episodes, including a very relevant back catalog directly from eCommerce fast lane. Do. Now today’s episode. My guest is Peter. Who’s the founder and CEO of a company called data milk.
And they’re at data milk.ai. Now what they are is a no code UX optimization tool that helps to build well, I’m gonna call it AI driven UX optimization for Shopify brands. Other CMS is also, but since we’re so tight with the Shopify world, that’s what we’re gonna focus on today. Their platform really is a starting point to really help growing revenue really in less than an hour.
It’s no code. You add it on there’s no technical resources required. It’s so interesting because they actually focus more on the navigational cues, which we’re gonna dig into a minute versus maybe other on page personalization tools that are available out there. It really does optimize this kind of, I’m gonna call it the core.
Kind of user experience on a Shopify site, really with the number one goal, just to create the right experience for each user at the exact moment. Like ultimately we really wanna maximize revenue for each visitor or each shopping session. That’s in a Shopify store. This is what we’re gonna learn today about what a data mill can do. So, hi, Peter. Welcome to e-commerce fast.
Thank you and pleasure to meet you, Steve, like for having me here. And I’m very excited too. Kelly about the data mill can generally talk. E-commerce
Absolutely. This is great. Really excited to get this tool. I had a chance to play with it and I had a couple, uh, signature Shopify plus accounts.
And I understand we’re gonna chat about one for sure. A little later on in the episode and just see how the tool’s actually working. It’s quite interesting. I know we’re gonna unpack a lot about this today, but let’s talk a little bit about just the tool itself. Would like to understand a high level first because brands are getting incredible results from it. I mentioned a little bit about what you do, but I just said it best in your own words about the data milk platform and the problems just on a high level. First about the problems that you are solving today for Shopify brands.
Guess it’s best told from the perspective of a little bit, my personal story and what I used to do in my life and how I arrived at this. So I used to work at Google and at Google, my job was to optimize Google dot com’s user experience to make more money for Alphabet, which is the parent company of Google. The way that we did this at Google was actually with a lot of work and sophisticated software. So we used to run about 150 AB [email protected] to find the winning ones and then unlock about six to 12% extra revenue. On an annual basis for google.com. So Google used to do this for 15 years. I was there for five years doing it, but if you sum it up over the 15 years, that means close to a hundred percent extra revenue that go generated from updating its UX. Right? So that’s kinda the foundation of the story where I got started from, I looked at the department that we had at Google and I realized.
Well, it’s gonna be very difficult to scale this type of work to most eCommerce companies that have to deal with a lot more things in their business than just dealing with their UX. Right. And from here, sort of the key question that we wanted to answer was. How do we create an alternative path towards producing 150 AB tests, if you will, and unlocking that value. So that’s kind of the foundation of the idea and where we got started from, and the answer is always in life tends to be, you just have to go through the hard work. You know, you have to walk that hard road to arrive at the right answer. So we didn’t really understand eCommerce. When we started this business, we said, we’ll believe the numbers.
When we see them, we have to try things to learn what. You know, and so we went and we actually signed up some partners that are quite large, who on our words believed us that we used to do this at Google. And we did it very well and were nice enough to let us do this work for free on their websites in the beginning. And we started cranking away at AB tests on the science, and we must have run a couple a hundred. By this point from here, we were able to derive the type of patterns of things that actually work, that saves a lot of that work and effort and time for other C. That is thinking about what else should I do?
Well, we’ve already walked this path with a bunch of companies, so we can just sort of provide solutions for the particular UX changes that actually drive value and revenue for the next store and the next store that we work with. Right. So that’s kind of the beginning and how it got started. And the paradigm that you’re mentioning, which is basing the UX much more on what the person is actually doing on the side. Not even necessarily personalization in its classic way where you try to figure out, is this a certain demographic that’s on the site or is this someone who’s coming from a particular ad, but really focusing in, on understanding in real time, what is the intent of the user based on the behavior that they exhibit on the site, you know, and then try to build the site underneath that.
Sort of similar to how, when someone goes to Google, they type in the search result that is incredibly valuable information to then display the right user experience for that particular search. You can do the same on an eCommerce website, but taking the navigational cues. So the actual behavior of the as a queue into their intent and these kinds of changes work really, really well in making the store ultimately easier to use and perform better for the end user and for the store owner.
So let’s jump under the hood a bit. I want to get a little bit nerdy. I think there’s gonna be certainly a cohort of those that are listening today and say, okay, well, how does this work?
Like how specifically do these optimizations work in real time? I know you’re, I mean, you alluded to the fact that we understand maybe. The site visitor and kind of what they’re doing, maybe their scroll depth, what they’re clicking on, and maybe even understanding where the traffic came from, if it was a social ad or if it was a referral link or, I mean, it came from an influencer.
I mean, who knows, you know, and so you’re obviously understanding that or a type of product or collection of products. It’s interesting to me that you have this data available, but then in real time, you’re modifying the navigation. To tailor towards that specific site visitor known or unknown. We’ll talk about privacy policies in a minute too.
I think I’m gonna unpack that one too, but I’d like to understand a little bit about how these optimizations actually work. Like maybe walk us through that a little bit.
Yeah. Yeah. Great question. Fundamentally, it’s a bit of a question about how the browser and the web works in general, right? For those of you who are running marketing campaigns, you’re probably putting a lot of parameters in the URLs. Those parameters can immediately be taken us in to understand where the person is coming from. You know, if, if you’re sending an email to retarget someone to reignite their journey, that they stepped off of, you’re gonna put the particular parameter in the URL. So that’s already information. The browser also collects information about the refer of the traffic.
So Instagram, Facebook, Google will. Pass through information into the browser about what the previous page was, so that can immediately be used. But from the very first moment that they land on your site, they are going to be looking at either a collection page, a homepage or a product page, most likely. And from here, you can already infer a bit about the behavior of this person, right? If it’s someone who’s coming. To look at the same product page and they left about a week ago and it was the last product page that they looked at. Right. That’s probably a very high intent person that they actually are thinking about buying that particular product. So that’s kind of the depth of information, unlocking it and talking about this in a structured way, right? Because that’s how we humans understand it. Of course, the AI thinks about these problems in a much more unstructured way. It unlocks slices and behaviors that we can’t even fathom or in some way correlated.
But from that very first page that someone lands. Very interesting. How much information you already have and possess about the user to try to predict what their next step might be. And now take that sort of notion that one page is so much already and multiply it by 64, because we see that it takes about 64 clicks for someone to even get to the point of starting checkout on an average, fairly large store with quite a bit of excuse. Okay. So people are actually walking very complex journeys through these eCommerce websites to get to a point where they make a decision on what to do. There’s quite a bit of space and unused data that technically you as the store owner own, right? Because these are your users. You probably paid for that traffic to arrive there.
Right? This is data that you possess and you’re not leveraging and using it to then make the user experience of that person better. It’s just a loss sort of on both sides.
Let’s talk a bit about the effectiveness of it because, you know, you said prior to Google and the, and you know, at any one time, there’s, you know, 150, some experiments, A B experiments going on and, and you’re gonna pick the best ones. And the revenue lift is massive. And so applying that methodology and concepts now over to the eCommerce world, it just, I guess those listening today would say, okay, you know, you have a no code thing. We turn on data milk, and then it starts doing what it needs to do. How do we know? If it’s effective or not, if it’s actually, you know, cuz there’s always this kind of black box concept, what’s going on? And in fact, does it necessarily impact the bottom line? Was it a drop that was different? Did it add a new product or collection to the site that all of a sudden it’s hotter. There’s lots of variables and trends and things that are going on in an e-commerce store, seasonality, the things to talk about right. But from your tools perspective, like how can you with certainty say, okay. It was turned on and with these optimizations it worked. And here’s why I’d love to understand that part.
Yeah, that’s again, a very, very good question. We talked to a lot of e-commerce stone orders that still operate on this paradigm that I’m gonna make some change. And I’m gonna look at before and after conversion rate of my website, for example, you have to look at the oscillations of this conversion rate on a daily basis, but you’ll find if you look at your store closely, Is that these numbers can vary by plus minus 50% from every day and every week. You know? So when you’re trying to detect a change of a couple of percentage points of increase, you obviously cannot do that this way.
You cannot sort of do it in a separate time period. So you have to keep all other variables constant. And how do you do. Well, you do that actually with classical AB testing. So at least for our tool, how this works is every time a person arrives on the website, the first thing that happens before any changes made to them is they’re assigned to a group, a control or a treatment group.
So this could probably be familiar terms for you from pharmaceutical trials, right? Control the person. Steve is the original version of the side, because all the changes that we call smart components that are applied to the UX of the side are additive in nature. By removing all what’s left is the original side as it behaved before. Right? So in the control group, you can say the users will see the original website and in the treatment group, you will say, I’m gonna throw the whole kitchen sink of UX changes. Data milk predicts will be correct and good for performance added. And from here you have two sort of users using the side, two different websites, the original side for one user, and now the upgraded data milk smart website for the other user and you can compare these two groups of users to see which one actually makes more purchases ends up buying with a higher AOV, ends up generating a bigger gross profit margin. And so. That number is highly reliable. And the way that we prove that, that it is highly reliable internally for ourselves is we’ll frequently run tests where we actually have a control group and in a treatment group, we also won’t do anything. So we’re comparing the original site to the original site and we’ll see that the results of the two are correct. So that’s what gives us internally the confidence that the tool is correctly identifying that there’s an increase in case there’s an increase. And this is the number that we derive for the end customer, the store owner. We show them how much more the users of. Smart UX sides are buying compared to the original side.
Right. That’s amazing. What about speed now? Cuz that’s gonna be the next comment that people are gonna make. And a lot of brands are kind of in that migration right now of online store 2.0 kind of a new framework and then maybe further down the journey is hydrogen and oxygen kind of our take on headless and then some people going truly headless sell and, and others that are, you know, doing that sort of thing with web flow and Contentful and these sorts of sites. So. I’m just curious, like what happens if this is dynamically changing?
Cause I always think about just, you know, the navigational structure, not being hard coded, but kind of it statically stays where it needs to be based on setting up menu hierarchy. You’re suggesting that your tool kind of negates a lot of that in some cases, certainly in the AB test side of the business, maybe just walk us through kind of what you’ve seen. From Speed. Is there any speed issues at all by dynamically changing something versus what’s I guess, built into a theme maybe on, on the online store 2.0.
Yeah. So here again, we have to talk a little bit about the specifics of how the browser works to understand this. The end result is that of course, if the site was being slowed down, you would notice it on the performance of the site from a financial perspective, right?
The final metric that matters. It’s the side, making more money as a result of that change, even if it is a little bit slower, if it is making more money, that’s good for the end user as well. Right. But let’s set this aside and talk about the technology a little bit. So the way that the browser typically works is it can open a lot of connections at the same time to download different resources.
And so, as long as the connection doesn’t clog up, it opens to data. Any sort of other important resources that have to load before it, the site loads on it. And on the side, the data mill does its job really, really quick and sort of loads in addition to that something else. Right? So the job is to not block the existing behavior of the site.
And you do that in two ways. One is you tell the browser, Hey, this is not as important as the rest of the things that the site should load. Right. So why don’t you do those things first? And then when you have a little bit of a resource for me, load Datamill. Yeah. That’s what the asynchronous stag does in the browser world.
The second. That is a bit of our advantage is that we build a lot of our technology on the same stack that basically Google uses for search and in the search world, every single millisecond is worth so much money. The fact that we’re able to basically sidestep a lot of architectural problems that other stacks.
By basically building on the fastest possible cloud infrastructure allows us to be really, really quick. Therefore, the, the speed impact of data milk on the site is not at all noticeable and is majorly surpassed by the advantages that the user experience gets. And as a result that is reflected on the financial performance of the software.
Right. All right. Very cool. Thanks for sharing that. I was just curious about that. It was more of a side note. I wanna talk a little bit about maybe competitors in the space. I mean, I’d argue that maybe you’re getting a first mover advantage in this space, which is interesting to me. That’s kind of why I’m happy on the show today, but I think maybe unfortunately, this podcast is going to help that education component.
I don’t want you to get it. Into kind of like an on page personalization tool because there’s lots of legacy partners kind of in that space that are doing extremely well, but they don’t do what you are doing specifically. They’re using other signals, maybe from a merchandising perspective and a recommendation engine. These sorts of things, you know, they’ll pre and post purchase check out things that are interesting, that certainly affect the bottom line too. So those are, you know, I’m not saying they’re not useful tools, but I just, I guess when you do a quick search, you go to the Shopify app store or you go to Google and, you know, there are some notable peers there that have. Something to do with improving the conversion rate or, you know, lifetime value, average order value. There’s always some kind of like a hack or tool or process in place that people want to implement or are willing to try. I think data milk is uniquely siloed in its own little area, doing its own little thing a little bit uniquely different from everyone else. But I just would like to understand a little bit more specifically in your own words, I guess, about how you differentiate from others in the Shopify Ecosystem.
Yeah, it’s also a very, very good question. You know, we’ve been talking to the partnership team at Shopify and trying to figure out exactly which categories.
I bet, I know it would be–
but here’s how I think about this. I actually think in terms of a metaphor, which is a, a real estate one, you gotta think of your site. As a real estate game, you can basically break down the website by the value of each sort of UX feature. The burger menu gets this portion of your revenue uses it, right? Search bar. This portion of your revenue uses it. The product page, this portion of your revenue uses it. The checkout, this portion of your revenues is it.
And you can get quite. Right. And down to the minutiae of every single detail on the page, you should assume. And this is what practice shows from many, many years of optimizing Google is that every single little part can be somehow made better. And so we have the, I would say at this point hundred pound gorillas in the room, which are for search and product recommend, At this point, if you don’t have those, I think you’re not running a serious e-commerce shop in any sort of way.
I mean, this is like the basics: it’s been proven by the market that works. It works really, really. I don’t know which vendor you should use for it, but you should go and get that for yourself immediately. If you don’t have it, I’ve definitely interviewed most of them on this show. So just go to the back catalog, you’ll find all the ones on search and on page optimization, for sure.
But if you take this thought that, okay, search is worth, what, 15% for you, you know, product recommendations, another 15. But the total should be above a hundred percent, right? Where’s the rest of the 70%? And it’s scattered around these opportunities and the rest of the real estate of your site. You know?
So the truth is that you should be doing a lot of these things. You should be doing anything in everything that gets you closer to your revenue targets every year. And UX is probably gonna be one of the most powerful levers. So don’t stop at surge product recommendations. Go further. you know, that should be the main takeaway from our perspective as a company, we exactly wanna position ourselves that way we wanna deliver unique UX improvements that we call against smart components.
As long as we are making smart components that you cannot find anywhere else. We as a company are gonna be very powerful and useful to the world, you know? So, that’s kind of the strategy that we are taking.
All right. Perfect. So I wanna talk a little bit about maybe a strategy or tactic that you can share. I mean, you have a lot of, kind of real world experience from the AB testing side on Google, but now heavily involved in commerce. I know you have a couple, quite a few, not. Partners on Shopify that are using your tool right now with wild success. I’ll get into their story in a couple minutes here. I wanna, hopefully I can, don’t put you on the spot here.
I’d love to hear more specifics about this particular brand, but I just, from your 30,000 foot view, like, you know, let’s say a brand really wants, I’d say they have a pulse on retail and eCommerce and they understand direct to consumer strategies. And they’re listening to this episode right now and going, huh? Dynamic kind of UX improvements. That’s, you know, guaranteed to kind of, you know, based on. Size of the brand that you can make a measurable impact under the bottom line. That’s very interesting and compelling, honestly, you know, when I hear that I’m a merchant success team member, so I’m a trusted advisor. I’m trying to help brands to, you know, profitably grow their revenue or, you know, improve efficiencies and lifetime loyalty and these sorts of things. Right. When I hear about your tool, I’m like, wow, okay, this is so unique and different. And so I’d love to understand kind of how your brain ticks a bit about. Navigation and, and just if there’s any strategy or tactic that you could share, maybe with some of our listeners, these people are really eager to move the needle in their brands. They want to grow their businesses. They could already be at $10 million. It could be at 5 million. They could be an early stage who knows the reality is all brands want to continue moving the needle up into the right. And so I’d love to hear what’s on your mind today about what sort of things brands should consider to get that needle moved.
Ultimately, I’m. Believer in data, then that’s why the company is called data milk as well. There’s gotta be a lot of creative ways left out there to leverage the data. So one example that we see connecting to that, and then the world of navigation can be drawn from something in, in the fashion industry.
Actually that’s been propagating quite a lot, which is this concept of fast fashion, right? We’re basically, if you’re not familiar with this, brands are really producing sort of a limit. Of various products and then really closely observing what sells and connecting that trend all the way to even their manufacturing facilities so that they can push up the production and manufacturing of the items that suddenly start selling, because you can never tell what the sudden trend will be.
Some influencers may go on the red carpet and wear a particular color of. right. It’s an unexpected coincidence in the world that makes that color of things suddenly take off. And you cannot predict that. And, and actually big fashion houses have known this for a very long time. And what they do is they produce all kinds of fashion lines of different colors and different materials in advance and they sit and they wait until the right moment to release those.
Right? What this sort of paradigm means in today’s world is that we need to be very, very quickly reactive. To changes in consumer behavior, right? And we need to propagate that using the data of the consumer behavior, into all of the systems related to the e-commerce strategy. And if you can do that, you can run a really, really lean operation in terms of costs and at the same time produce maximum sales.
And I think that’s the angle that really works for us in UX today as well. We observe what people are doing. We are very, very quickly reacting and making the sites, UX reflect those trends. So when it comes to that burger menu, right, if suddenly something starts taking off in your site, you wanna start promoting the hell out of that category of problem, right?
Yeah. Because you know, it’s working now, you know, you know, and how is it gonna take you with a manual process to recognize that? And we talked to some C. That literally employs someone to sit on Google analytics and once a week pull these reports to then move the categories around. That is obviously not a scalable approach.
No, it’s an manually ever approach, but the idea is good, you know, but the execution not so much. So that’s why I’m a big believer in data because I believe that. You cannot predict in advance what will happen in the world? I mean, I certainly cannot predict what will happen to me in an hour from now, you know, so, so similarly we should just be very good. Listen. In life and for the company, that means listening to the behavior of their customers and reacting to it as quickly as possible.
Yeah. Love it. Love it. Love it. Love it. Okay. Thanks so much for sharing that one. This is the story part of the episode. Now I just feel that people resonate when they hear a story about a brand that uses a particular piece of software or technology, and, you know, like where were they before data milk?
Where, what happened when they implemented it and then looking at the other side, what was the, the end result and the ongoing relationship? So I have a couple in mind, but I’m, I’m gonna throw it back to you. I did some kind of internal research before our recording today, but I will throw it back to you. If you could maybe share. One notable brand that you know, that we’re both partners with or Shopify, you know, plus a brand with, you know, all the tech stacks that you know, that they have to run their store. They also have a data mill running too. So I’m just, maybe you can share just what specifically, what motivated them kind of in their current process, I guess, to now become a data milk customer.
One of our flagship customers is also one of Shopify pluses, flagship customer: culture kings. Very very interesting story of a company in general and how they brought a unique story and merchandising strategy to the eCommerce world. Very much aligned with this fast based moving internet paradigm, that random things take off like, uh, Netflix episodes that Netflix is, that no one would ever think would become big three, four years ago suddenly become the, the sensation of the world and who knows how that happens.
And, and you can ride those waves on the product merchandising strategy and, and they do a really, really good job at that. And it’s an excellent sort of business idea. I’m really fascinated by all of these businesses. It’s so cool. The types of depth that you can create and ideas. In making businesses. And then they were in a really, really lean technical operation, you know?
So just to highlight the benefit of Shopify here, I. You’re running a gigantic business. You know, I’m not gonna quote numbers, but a really, really large business with a couple of technical people that was impossible 10 years ago. And they gotta focus on the uniqueness of their business, right? Because the uniqueness of their business is identifying these consumer trends and really producing goods that ride that wave as quickly as possible.
So it’s awesome that they get to focus on what’s what they’re great at, you know, while Shopify is providing a lot of the technology. Yeah, but that also means they didn’t have time for UX. And so when we talked to them, they got interested in, okay, well that may be true that I have more money in my user experience than I thought.
So let’s give these guys a chance to try to unlock it, you know, and that’s kind of how the partnership started. And we worked right a bit on their side and we tried a lot of different things until we arrived at the developer set of things that are working really, really well. And now we’re producing amazing numbers for them.
And really, if you talk to their team to this date, what they like about it the most is how minimal of resources it requires for them to work with us because ultimately our solution is so easy to. And it’s so easy to make these UX changes that actually drive value that they end up sitting back and benefiting from it and focusing again on their core business, the same way that a lot of things Shopify is handling for them that they don’t have to ever think about.
You know, so it’s yet another similar solution, but with a very much UX focus. So I think we’re aligned with Shopify thinking here that everyone should specialize in the world on the thing that they’re best at. And for us, we’re UX and data nerd, you know, We’re having to come into your business and, and give you that expertise and provide our software to walk a similar result.
It’s interesting. When I went to data milk, I went to the website and looked up. I was hoping you were gonna bring up the culture Kings ones. I saw that there’s a few others on the website too, but you know, some of the public numbers that you’re able to share here was, you know, their ROI was, you know, an eight X and, you know, they had a, a 7% lift in gross profit, all because of your application running real time on their site. And I think that’s pretty impressive results, you know, for a very small amount of money for your platform in exchange for the incremental lift that happens like in real time. And so it’s pretty exciting that that’s the kind of numbers that they’re able to experience.
So, yeah, and, and furthermore, basically the way that we set up the billing for our tool, we wanna earn the money that you’ll pay. Before you have to pay us. You know, that’s really the most important thing here, that you’re basically paying data milk with extra profit that you earn, and we can statistically, a hundred percent prove to you that that money is in your bank account already by the time that you send a wire to the data mill.
So that obviously really, really helps them with their cash flow as well. And we’re happy to power amazing creative entrepreneurs like. A little bit of extra powder to build even more cool things. Love it. Love it, love it. Well, Peter, we are nearing the end of the show for today. I know we can probably chit chat a lot about this.
I just feel so blessed that I had the opportunity. Number one, able to kinda share this little further out in the Shopify world. I mean, I’m 15,000. Some people are likely listening to this episode today and it’s just like, I’m hoping it, it really just shed some light, put some light on this topic that, you know, there’s more, you know, you need to have the basics.
As you said, the table stakes of search and merchandising tools running. Man, this is like a, another part of that overall revenue lift opportunity is I think there’s money being left on the table. It’s very clear and culture Kings prove that fact with your tool. And so that’s pretty cool. Love to understand a little more about before we close for today, about what the future holds. You know, I, I don’t know if you’re able to share, maybe not sure your public roadmap, but maybe a north star, like. The rest of a 22 gonna unfold. Now we’re beyond four datamilk. And like, how are you gonna continue to offer value and assistance for brands that are really trying to unlock some of this missing UX? And is there any other, like, I don’t know, like partnership arrangements or other, I don’t know, innovation, I’m just curious about what you have planned that you can kind of publicly share.
Absolutely. Absolutely. So there’s two levels of thinking that I can share on this question. The first one is a little bit going back to the real estate paradigm that I mentioned, there’s much more than that 7% in the UX of even culture Kings, you know, so we are continuing. To work away, hack away as much as possible and unlock even more value. My estimate is that there’s 10 times more there. Okay. So we’re gonna keep working on getting that for every one level. But the second sort of level of thinking generally boils down to, we now live in an incredibly data rich world, and we don’t have enough use cases for this data.
And most people don’t know how to leverage this data. And not just leverage it to their benefit. But do it in a sustainable way, and we didn’t get to talk about privacy, but, but this is one of the points you have to do it responsibly. Right? You have to use data in a responsible way. We’ve seen a lot of companies in the world that have used it irresponsibly.
And ultimately that actually makes our lives as humans worse on this planet. Okay. So we are in the. Of leveraging data to make good out of it in the world. And in this case, we’re making the UX easier to use and generate more money for the store owner. But there’s many other ways that we can leverage AI and data to improve this world of commerce.
You know, so as a company, data milk will continue to focus on creating unique solutions for how to leverage the data with the use of AI to create benefits for both the store owner and the end user. So where should the listeners go? If they wanna learn more about datamilk is any kind of resource that you would like to share.
Yeah. Yeah. So I think we are gonna have a specific age that people can land on the listeners of this show. We are quite selective with the customers that we work with today, but with the customers that we feel certain that we can very quickly generate results for. If you just come and sign up. We’re actually happy to waive our typical $10,000 setup fee because we’re so certain in our results. And we’re very appreciative of this opportunity, Steve, and being in the show.
My pleasure. So I’ll guess what I’ll do in the show notes. So we’ll send people to. Couple different places. One data milk.ai is gonna be like that, guess the main URL, just to learn more, do like your blog, by the way. I’m gonna have to, I see there’s a few posts in there now. And I even read actually, just for recording today, I even read the article about its CEO world. We were talking about, you know, why is now the time to invest in e-commerce menu navigation? So I’ll make sure I put a link to that, which just came out recently too. I think March, March 9th, actually. So it’s actually less than a week ago.
It was a good little article and it kind of summarizes some of the things that we were talking about today, about how to use data to your advantage. The fact that you really do have an emerging tool. That’s very unique. Thank you for coming on the show. I do really appreciate that. I believe that there is some opportunity.
I’ll have a landing page set up and release this episode on the show notes page. It’ll be e-commerce fast lane forward slash data milk, and I’ll go to a landing page on data milk, and then we can talk about that opportunity, getting a demo, see if you’re a good fit. Thank you for, you know, potentially waving that, that onboarding fee. I think it’s very interesting for the right brand that really is at scale and really wants to maximize their. Return on investment. They wanna increase their revenue. This is because there’s a lot of stuff that’s just, I know, is missing with no one doing this. Hopefully, you know, I get the opportunity to share this out into the wild further, and you get first mover advantage culture Kings and others have proven the value of the tool. And hopefully others are listening today. They can also get value from what you’re building. I just wanted to thank you so much for coming on the show. You know this, but Shopify has a mission. And it really is to make commerce better for everybody. And you know, for me, from listening to this episode, it’s very clear that data milk; you and the whole team, you really are in tight alignment. You really do want to help Shopify brands to, you know, grow revenue through improving these UX experiences. So pretty exciting. So thank you for your, for giving back to the ecosystem and thanks for building an incredibly impactful product.
So thank you for having the opportunity to speak to you and present it.
All right. Have yourself a great day.
You as well.
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