Chase Zieman is the Co-founder, Chief Data Officer, and General Manager of Data Products at Cart.com, an e-commerce software and service provider helping brands scale and streamline delivery processes. Chase is an expert in the data sciences industry and has experience leading high-performance teams for growth. He began his career in AI within Deloitte’s counter-terrorism and fraud detection spaces before leading data science at companies, including Home Depot and Vail.
Here’s a glimpse of what you’ll learn:
- Chase Zieman shares his journey to founding Cart.com
- How to resolve complications associated with attribution resources
- The impact of first-party data limitations on attribution’s growth
- Creating campaigns to increase cash flow
- Rectifying common industry issues to improve e-commerce brands
- How the fourth dimension can influence our three-dimensional world
- A glimpse into Chase’s personal life
In this episode…
Tracking attribution helps you understand your customers’ online habits and the marketing strategies influencing their purchases. While the benefits of attribution are invaluable, the model is not perfect — and it may create more questions than answers. What are the practical methods for resolving frequent attribution issues?
As data analytics rapidly evolves, traditional attribution models are being discontinued. With rumors of third-party cookies dissolving, your company must adapt to these new developments to continue tracking your customers’ digital behavior. Recognizing the future of data analytics, Chase Zieman found a solution that doesn’t rely on third-party cookies.
On this episode of the Up Arrow Podcast, William Harris welcomes Chase Zieman, Co-founder, Chief Data Officer, and General Manager of Data Products for Cart.com, to discuss how to solve issues associated with attribution tools. Chase also shares the limits placed around attribution models, how limited attribution will affect the marketing industry, the value of campaigns, and how utilizing the fourth dimension will impact our world.
Resources Mentioned in this episode
Sponsor for this episode...
This episode is brought to you by Elumynt. Eluymnt is a performance driven e-commerce marketing agency focused on finding the best opportunities for you to grow and scale your business.
Our paid search, social, and programmatic services have proven to increase traffic and ROAS, allowing you to make more money efficiently.
To learn more, visit www.elumynt.com
Welcome to the Up Arrow Podcast with William Harris, featuring top business leaders sharing strategies and resources to get to the next level. Now, let's get started with the Show.
William Harris 0:15
Hey everybody, William Harris here I am the founder and CEO of Elumynt and the host of this podcast where I feature experts in the e-commerce Industry, sharing strategies on how to scale your business and achieve your goals and your dreams. With me, I have a really exciting guest today Chase Zieman Chase. I'm gonna see if I get to do this in one breath. Here we go. Chase is the co-founder and chief data officer at Cart.com, the leading provider of comprehensive e-commerce solutions that enable retail brands to easily sell and fulfill across every channel with nearly 15 years at the forefront of Data Science and Artificial Intelligence. He launched the company in 2020 and is responsible for building innovative solutions at the intersection of commerce platform software and AI Zieman began his career in AI within the counterterrorism and fraud detection space at Deloitte before leading data science engineering digital. I didn't make it industry solutions that companies including Home Depot NRG Energy in Alteryx, that is absolutely a massive lead up. I'm really excited to have you here.
Chase Zieman 1:10
Yeah, appreciate it. Appreciate it. You almost made it through.
William Harris 1:14
I did, man I tried. I think I did it in practice a couple of times. But it's a little different when you're actually on the on the camera and
Chase Zieman 1:21
breathing exercises. Before it
William Harris 1:23
reads a little bit more. I do want to go before we dig too deep into the actual questions here. Sponsorship method message here on this episode is brought to you by Elumynt. Elumynt is an award winning advertising agency optimizing e-commerce campaigns around profit. In fact, we've helped 13 of our customers get acquired with the largest one selling for nearly 800 million. And we were ranked as the 12 fastest growing agency in the world by Adweek. That said, we're here to talk to Chase. Chase, one of the things I'm the most excited to talk to you about is the backstory Cart.com. What got you into car.com? What's the backstory for even founding it in the first place?
Chase Zieman 2:03
Yeah, great question. Great intro, William, happy to be here. Funny story around the beginnings of Cart.com. There are four founders before the original founders. And three of us had worked together back at Home Depot, and were heavily focused on digital transformation and growth of the e-commerce business on depot. And then on the fourth one was a connection through the CEO Umair. And so we had all kind of split up and we had planned on starting a company one day, we didn't quite know what that company was going to be. But we assumed that we would boomerang back together. And my wife and I had left Houston. I was, you know, sitting pretty in Colorado, snowboarding on the weekends. That time we had been together for probably about 14 years. Never had kids before. And it was kind of a the first time in life to where things were just kind of moving slower. Right. And so we were like, hey, you know, let's let's have kids and so my wife is actually pregnant. We just found out she was pregnant one week before. And oh, Mayor Tareekh, um, the CEO, and my ex boss from Home Depot sends me a text message. And he says, Hey, you're quitting your job. And we're starting a company. And so I was like, Whoa, you know, I'm like, Oh, Mary doesn't have high high alcohol tolerance. He's probably had two glasses of Prosecco at this point. Sure. Maybe he's a little tipsy. And, you know, I'm loved my job. At this time. I'm sitting at Head of data science for Alteryx reporting into the chief data officer. And I told him, I said, Hey, so what's the idea? Right, like, oh, man, what's the idea? Don't worry about it's going to be great. I'll call you next week. So of course, I do not tell my wife. I am like, I'm not even going to stress her out tweet about any of this. You didn't tell her at all? Not yet. Not yet. Okay. Because I was, you know, I America sometimes a sporadic person, he has a lot of ideas and so I was gonna wait until we had the the actual conversation about the idea. And, and mind you, by the way, we had just built and moved into a brand new house in Colorado in probably about 90 days after like living in an apartment for a while as his home was being built and delayed and whatnot. And so fast forward about a week later, I talked to him he says that, you know, Henry is involved as well and this other guy Remington that I had met and tells me about Remington and you know, basically sends me a shell of a pitch deck. And you know, he had linked up with this guy named Jim Jacobson who was trying to poach him from Home Depot. to basically start this brand incubator, and, you know, a mayor and Henry had always known that I wanted to create a company around an analytics platform. So like one of the things that we do inside of Cart.com, which we'll get into in a bit, I'm sure is this product called Unified analytics. And I would say for the last, like six years or so even before Cart.com, I had been thinking about how we could build this product. And maybe that was going to be the original company. And so he he's basically pitching like, hey, we can have this e-commerce platform, we do all the things for e-commerce, because all these brands that Jim wanted to have a brand incubator, they need to have all these needs, right? And so rather than stringing together all the capabilities of e-commerce, what if we could start consolidating all these capabilities under one roof, and then from a marriage perspective, he's like, Hey, and by the way, you get to build what you've always wanted to build is just now inside of this much, much bigger thing. So at that point, you know, I'm like, Okay, this is real. And I mentioned it to the wife, like, and, of course, a marijuana system moved back to Houston, if we do this, oh, no. So you know, it all just kind of snowballed from there. And, you know, and four of us left our jobs. You know, we had at that time, we had like, small severances, if things went up in flames, like Jim had promised to give us some runway for walking wave our jobs. And then the four founders got together and walked around with a PowerPoint deck and pitched and pitched and pitched and pitched in pitch to rally hundreds of VCs. And probably, I don't know, maybe 45 to 60 days later, we closed our Series A. And since then, we have, we've raised over $400 million. We've grown to 1600 employees, I have 11 fulfillment centers across that region, we
William Harris 7:03
massively fast Hang on a second that that's like Manwani 20 months or something now, isn't it? How long has it been?
Chase Zieman 7:09
20? Yeah, 20 months? About 20 months, 20 months? And yeah, so you know, across the ecosystem of cars, we have great third party logistics, pre PL once again, like one to two days shipping to 9095 96% of the country. We have some major brands in that area. We have feed marketing to give you access to over 2000 sales channels across the world, just
William Harris 7:37
like you guys are doing like every, like you said, it's like all of everything. Commerce stuff. Yeah. I gotta come
Chase Zieman 7:43
funny, though. We? Oh, yeah, go ahead. Yeah, I was just gonna say it. It's funny. We early on, we were leaning into this concept of Indian River intern commerce. And, you know, that was just us. We're not none of us for awhile. I believe I'm an expert in digital marketing. I'm not really a brand marketing type of person. As we were beginning the conversations with like the foresters of the world, we actually got some feedback, like, you know, you should lean out of in the end, because you're not really in the end, because in the end, is a fictitious asymptote. And you can't actually be into and, and so we, we leaned out of that message a little bit, but to your point, we do a lot of stuff, right. And I think we could be, let's say 80% of your needs as as a brand, which is a ton.
William Harris 8:34
Yeah, especially in something as robust as e-commerce. The the one though, that I wanted to come back to is, you know, the unified analytics thing, that's one of the biggest ones I know you and I kind of both geek out about, and I'm gonna call you a nerd right now, because you just said that you wanted to treat an analyst company. And that's a very nerdy thing to say, I'm a nerd as well. So I'm right there with you. But, you know, I have to imagine that's not what you said, when you were a kid. You didn't say, hey, when I get older, I want to build an analytics company, or was it did you say that even as a kid? Yeah,
Chase Zieman 9:06
I mean, it's interesting, I think early on as a child, I was always gifted in mathematics. I think originally, I wanted to go to medical school. And when I was really small kid, I wanted to be a pilot when I was like a toddler. And then once I got to grade school, I wanted to be a doctor, but I have some doctors and nurses in the family. And so I think I was getting steered a little bit toward medicine. And then growing up, I was always really, really good at math. And you know, there's a program in Louisiana, called Louisiana school for math, science and the arts. It's a boarding school. They basically go around and handpick some of the most gifted talent and students in the in the state. They have about 150 students per class, and he basically move away from home in high school, you go live on campus and you're able to take college level classes like differential equations, abstract math, linear algebra, all of the calculus is organic chemistry, like all those types of things, you're able to take them in high school. And that was really when I started to realize that my math work my mind worked in a, in a mathematical manner that was unlike others, but I stayed on the buyer route. And when I came into college, I basically tested out of enough classes to have a minor in Mathematics. And day one walk on walk on campus, I had a minor in math, and I didn't want all this credits to go to waste. And I was still planning to go to medical school. And so there was a program called biomedical engineering, okay, I can get, I can use all my math credits, and then I could get all my prerequisites for med school, if it is biomedical engineering route, which I did. And then, you know, ended up studying for the MCAT did pretty well on the MCAT ended up teaching Princeton Review classes for MCAT, and GMAT. But studying for the MCAT was when I realized I was like, my brain is not wired to memorize biology, I just can't do. And I was just like cruising through all my people like failing these math classes, and not studying these math classes. And then LSU. So that was at LSU, the PE in there. And LSU was, I believe, the third masters of analytics program in the country. They were a super early adopter to Data Science and Artificial Intelligence analytics. And there was actually a pilot program when I had graduated undergrad, in a master's in analytics that I could combine with an MBA. And I had a began looking at this. And it was like, super interesting, because, like, all all, like all I have to do, and I was actually surprised taking a step back, that there was a job to her, all I had to do is solve math problems. Sure, this was a real, this is a real job, you just throw problems at me that are unsolvable. And I can solve these math problems, right? So like, where do I sign up? Right, and I signed up, did my grad school research LSU and fraud detection, and then the rest of its history from from that point on. So a kind of a weird way to get there. But But then again, in our, in the data science and AI space, I think some of the most talented people that have hired and or worked with and or met, they come from all kinds of different backgrounds. So right, and I think it's just figuring out that your mind is oriented to be successful in these types of situations.
William Harris 12:47
Well, and I'm right there with you, fellow nerd, the nerd. Just driving to work the one day, I decided to memorize pi out to 59 digits, because why not? And I just needed something to do and enjoyed that. And I think I remember taking my daughter into kindergarten class. And, you know, kindergarten, what is she six years old, five years old or something and I kind of leaned over to when I go, Hey, give me a high 4.9 repeating. And she knew what it meant, because I had taught her what that meant kind of thing. But it's like, I know her teacher has no clue that a 4.9 is the same thing as five. All right. So yeah, math nerds and just appreciate like the way that yeah,
Chase Zieman 13:25
like, we'll never be in the end. Right?
William Harris 13:27
It will never be an end, right? Yes, exactly. So when I think about, you know, what you're doing, especially the unified analytics and math, like this is a complex thing. And I feel like there's a lot of issues that I take with a lot of other analytics platforms out there that I feel like they were written by marketers, who then tried to apply math to whatever they wanted to do versus written almost from a mathematic standpoint from the from the get go. When, you know, we're talking attribution, there's that's a hot button topic out there. There's a lot of people that are very hotly contesting this, you'll have one platform that gives a very different attribution, another that gives a completely, you know, opposite one, they both have their own algorithms for how they're coming up with this, then you'll have something like Google Analytics that says something completely different. And then we'll run into businesses that are like, oh, yeah, we actually use NetSuite and we use last click only. Okay, well, that's a whole nother issue of problems, not NetSuite. NetSuite is great, but I just mean, the idea of, of, you know, getting to the we're using last click only And so, there's a lot of issues that are out there with attribution, and I feel like what I've seen is a lot of these attribution tools are creating a lot more questions and they are solving answers for a lot of people. How are you getting around that what's making this something that people can actually take action on?
Chase Zieman 14:56
Yeah, so great question and couldn't agree more attribution is probably One of my favorite topics in the in the space. And there's a couple things that I think, put us in a unique position to win in this space. And then also pulling on the thread that you were talking about and the struggles that the brand is dealing with. So, first and foremost, you're completely right that the brand is, in many cases, logging into these different platforms, logging into Tik Tok logging into Google logging into Clavijo, logging into meta, right. And all of these platforms have their own attribution model. And then you have free GA, which is, in most cases, click based. And if you're not buying and or building an analytics tool, using Google Analytics is definitely better than not using Google Analytics, I would never stray someone against using Google Analytics. But at the end of the day, Google Analytics is just one single data source, in the piece of the puzzle of if you're actually going to create 360 degree analytics around your business, it's still just one point, right. And when we started Cart a little less than two years ago, we had the benefit of knowing where the world was going, we knew that the cookie was crumbling, we knew that third party cookies were going away, right. And so you have some of these other platforms out there that, you know, by no fault of their own, have technical debt that is reliant upon these data points that soon are just not going to we're not going to exist. And so with our technology, specifically, we don't even capture them. We don't use them at all, we don't place them, we don't use them. Because with adblockers, your pixel technology is going to be completely gone. Right? No one, no one's going to be using your tracking, you're not going to be able to have tracking. So we use a combination of first party cookies, and an AI based digital fingerprint technology that goes across all those visitor those visitors and visits and hits across Europe. So with the first party pixel with the digital fingerprinting, we do have we believe we have somewhat of a leg up, we also have a segmentation and cohorting capability that allows us to understand digital profiles, because at the end of the day, even if we go back to third party, let's say cookies, you're not going away. Right? Let's say you can lay all the cookies you want, you can identify all the visitors, the only reason that anyone's trying to identify that you're William Harris, is so that I can identify what cohort William Harris is in so that I can remark it to that cohort. I'm not marketing to William Harris, that would be incredibly expensive. I could write. But from a CPM perspective, it's much more efficient to market to a cohort. And so we we started with cohort base. And we also started with the digital fingerprinting and the first party cookie and not relying upon those third party cookies. And then lastly, that allows us to create that single source of truth across all of those ad platform data data points, right, and so we're pulling in all the reach, we're pulling in all the impressions we're pulling in all of the spins are pulling all of that by geo by device, and by all of those segments that you still can obtain from all those marketing channels. And then all of those data points at the granular level gets folded in to that rich Clickstream data that we have. And then you start creating independent, very independent predictor variables, right. And so I started understanding how an increase in spend and reach and impressions on a mobile device in the New York City area was impacting direct and or branded search traffic to your website, and unfolding in all of those views, all the clicks. And that's really, I think, at the end of the day, kind of how we're different than a lot of the other a lot of the other platforms out there. And the last thing that I'll add is, there are some other very successful folks in our space in marketing analytics, specifically, because of our ecosystem and our very, very, very strong presence in logistics and supply chain. We are, we have to build amazing logistics and supply chain capabilities for Cart.com. Right, just for us, for our brands that uses for three PL for us to optimize labor planning for us to optimize inventory movement. And so as we're building all those things for ourselves, we're also building those things into the platform, right? And so we were more than just marketing. We're more than just sales analytics. We're talking about prescriptive inventory movement move 1000 units from New Jersey to Salt Lake City, you're about to run out of this inventory. You have too much inventory of this specific SKU because we have Product Level attribution, I can tell you which campaigns are driving that skew in which you know, lever you need to pull to actually rid yourself. And so it's just going to get more holistic across the stack. And, uh, you know, we have, we have just as many fulfillment analytics customers as we do marketing analytics that you could you can buy marketing analytics or fulfillment analytics, or you could buy the whole stack. So that that is a very, very key differentiator, right is we're not just, we're not just marketing.
William Harris 20:34
And so you know, and that's good, right? Because we talked about the idea of, sometimes there's a lack of communication between the two different departments, we've run into this before, where there was a customer of ours that said, you know, something to the effect of, hey, here's our growth plan, month or month, we want to grow to under percent. And because we go a lot deeper with our customers, we are actually having these conversations with them, we find out well, you only have 50%, more inventory. So, you know, unless you're raising your rates, you're not going to grow 20% This month, it's not, it's not going to happen. There's this disconnect. And so and even the person at the company, who was in marketing wasn't aware of that, because they weren't talking to the inventory ordering team. And so, you know, we find that this becomes an issue where the finance team isn't talking with the marketing team, the marketing team is not talking to the inventory team. And, and there's these disparate knowledges, or goals or whatever's going on. And so I liked that you guys are unifying, a lot of things that I'm not seeing unified, because I think that that does allow for some better predictors of success for a lot of these companies. What's your thought on? You mentioned, you talked a lot about first party cookies, first party data and unifying some of that stuff in here. Where do you think this is going? Because I think there's still somewhat of an end to this where we're Apple is trying to eliminate any potential for tracking to a point that you can get, where's this limit to where even potentially first party data becomes useless?
Chase Zieman 22:06
Yeah, I mean, it's a great question. And it starts right by the IG, none of these platforms like Facebook, and Google and Apple are going to ever share data with each other. And I think Apple beginning to put these walls up and creating this ecosystem internally, sets them up for a unique position to where I could see a world to where Apple begins to create an advertising network inside of Apple, right to where you to actually advertise to someone on an Apple device, you're actually doing CPMs inside of Apple, right. But if even if that were to happen, obviously, that's drastically detrimental to the Googles and Facebooks of the world. But I have to believe that the brand who's now bidding, through Apple is still going to be able to access some level of aggregated data. So it may prevent Facebook's ability to optimize and spend and serve on Apple and really more understand, right, because you're still going to be able to target those folks. The problem, you know, when, when iOS 14 came out, it's funny, and taking a brief detour really quickly. And then it's going to come right back when iOS 14 came out. And, you know, Facebook said, Oh, shit, we have to, you know, push the conversion API. And then you saw all this content come about, and it's like use the conversion API, and your return on adspend is going to increase by 15%. Overnight. Your ro s doesn't go up, your reported ro s goes up, right, you just you just allow Facebook to attach itself to more conversions, your campaigns are just as efficient as they were yesterday. Now one could argue if you're attaching more conversions, you have a better picture of reality. Therefore you can optimize your media spend more. Therefore, in the long term, your media is going to be much, much more efficient. So in the same manner, I think Apple starting to put these walls up is going to make it more difficult for the likes of Tik Tok and the likes of metal and the likes of Google to understand what's going on on this Apple devices. And then fast forward. I do believe that Apple at some point is going to allow you to maybe market inside of Apple. But if that day ever comes I still believe that the brand who sits at the center is going to be able to get the data, right just like the brand can get the data from Google and get the data from Tik Tok data from meta. It's going to be even more important For the brand to have a consolidated analytic Strategy to centralize that data and make sense of it, rather than relying upon these ad platforms to make sense of themselves.
William Harris 25:09
But I think that that's what we're seeing now in in the idea behind the AI with all of this data is, there's so much data. And if you're looking at it, just from a very simple human perspective, you're likely going to miss what you need to see within this. And we'll even say, Okay, let's take this down down the road, I want to bring this up with with even physicians, you mentioned you had some doctors in the family. There's a really interesting thing called Bayes theorem, which, for those who aren't familiar with Bayes theorem, you know, there's a really great article that I have on Elumynt website that kind of goes through Bayes theorem and how it applies within at least from a an advertising perspective. What's interesting is they actually found that doctors who are presented with these same sorts of problems where they're given sets of data, and they have to now make decisions based on well, how likely is it that this person actually has this diagnosis, because they have this test, that test is 90% accurate, but they also have this, you know, comorbidity and this and this and this. And doctors, if I remember correctly, there was a study that was done this, the doctors only got it right, like 34% of the time, like they actually don't really even know. And these are, these are very highly logical people who are, you know, very good typically at being able to interpret this. And then you put that into, let's just say, you know, marketers hands and not saying that marketers are not doctors by any chance, but just simply saying that, it's like, there's oftentimes a higher weight towards creative side of brain thinking in a marketing space. And so it's very easy to miss some of these underlying things that are taking place within the data. But that's where the AI comes in, you bring in data from Tik Tok, bringing the data from Google Analytics, you bring in the data from Shopify, you bring in your own first party data, you start putting this all together, now the machine can look at significantly more data points and start pulling in these different correlations that you simply couldn't even begin to understand or see or visualize as a human being. And I think that's one of the exciting things about not just you know what Kurt's doing, but just the the AI revolution that I think that we're in right now.
Chase Zieman 27:17
Yeah, yeah, no, I couldn't agree more. And, you know, you asked a question earlier about, we started the conversation with like, how are we different and looping back to that in the power of AI, we, we are, we strive right every day to get, you know, top right corner. So to say, of the Gartner analytics, maturity curve with respect to prescriptive analytics. And so when you log into our platform, I don't want you to have to dig through filters and dig through KPIs and understand why you're winning and why you're losing only to figure out what you need to do next. And so we have a an AI powered what we call a business recommendation, engine, TBD, on branding, not like the the most friendly one in the world, the brand folks who come up with a better name of it. But we tell you exactly what to do. Like you log in and you go to the Adjust adspend dashboard. And we literally say that the campaign and soon to be ad set ad group, keyword skew level, increase CPC, decrease, CPC, widen audience, refine audience, review, landing page, pause, campaign, order more inventory, run a campaign to rid yourself of this inventory. So the only reason most people are looking at analytics, and in my mind, two reasons. Number one, figure out how we did so I can report out to folks, and that's really more of a descriptive and a diagnostic banner. And on the other side, it's what the hell do I do right now? What do I need to do today, at this exact moment, to improve my business. And if I could just serve that on a silver platter to you, you can get on with your day. And you can start thinking more strategically, right? You were just talking about the creative side, rather than rather than messing around and understanding what do I need to do, you can start saying, hey, based on all this AI, doesn't look like I'm gonna hit play on next month. Let's start thinking of creative ways to hit plan. Not trying to think about how I need to edit my campaigns for this week, right?
William Harris 29:24
Great to maybe even just adjust what the plan is, hey, what's the acceptable plan? Hey, we're in the middle of a recession, potentially, right? That might be the part and you say, well, maybe I'm not going to triple the business this year. But we can figure out how do we make sure that we maximize cash flow because we ordered X amount of inventory and so EBIT, the wise we might not be up but we can at least make sure that we you know, have the cash flow that we need to keep the business moving forward. That's it that might be an adjustment of a plan that needs to happen. And you were talking about, you know, being able to use, being able to use the data in a way to create prescriptive ideas. And I think that's one of the things that a lot of AI software is doing. And I'd say some of the more advanced AI that is out there that's, let's say, been used to a point is, or at least from an algorithmic perspective, would be Google's prescriptive things that it's doing within their Facebook's prescriptive things. There's a lot of very interesting things that they've done. And there's some brilliant people that are working there. But even still, as an advertiser, I find myself looking at a lot of Google's recommendations that they say, Hey, increase your spend here, increase your budget here, and you think, but you don't actually understand what the business is trying to do. And so while that might be fine, anything else? Right, right. And so, you know, I liked that you called out the idea of, you know, maybe you need to create a campaign to sell through this inventory. That's a whole completely different thing that I feel like Google isn't going to give you that recommendation. But that's a recommendation that might actually be very beneficial to the cashflow business. And that can be a very important thing right now.
Chase Zieman 30:58
Yep. And you see it, right, because you can go into our optimize inventory analytics experience, you come into there, we're doing demand forecasting, we know zip codes of where your inventory sits, we know where everything is located at. We know at the geo level, we're minimizing shipping cost per pound based upon minimization of zone cross edge, I know where you're going to run out of inventory, where you have access, I know that this SKU, you hit, you're sitting on a buttload of inventory for whatever the SKU is, then you go from there. And you say, how do I rid myself of inventory for this SKU? Well, guess what we have I tell people in demos, sometimes it's like the translator app on your phone. When you go from like English or Spanish or Spanish to English, the translator between marketing and supply chain is product level attribution. And you can go in both directions. And so from a SKU, I can actually go to my product level attribution experience, and understand which specific campaigns are the most efficient in driving sales of that product in a sensitivity manner, and which campaigns have the least halo effect of others, other skews. And, you know, if you're trying to read sales, it probably is a little less relevant. But if you're out of stock, you obviously want to minimize halo effect if you're going to turn down spin. But if you're if you have too much stock, I can go in and I can say, Hey, these are the campaigns that are most efficient and driving sales to the SKU. And there could be a lot of different reasons it's efficient, it could be where you're dropping him in the funnel, it could be based upon the audience could have an affinity for that product, there's a million different reasons why that campaign could be efficient, it could be based on the keywords, right, but agnostic of any of those reasons, this campaign is the most efficient of driving that SKU and that's what you need to go dial up, right. That's how you need to double down. And so like that, that level of translation. From my perspective, I've never seen anyone do it in a platform. I know that, you know, companies like Wayfair, or like Home Depot, and things like that have probably built these things for themselves Amazon for themselves as an example. But no one's actually put it in a platform to be able to scale to, you know, 1000s of brands, in these types of capabilities.
William Harris 33:14
So, you know, that's one one example of how somebody can practically use a tool like Cart.com to significantly improve their e-commerce Store. But what are other problems? What are the two or three other problems that you find? You know, as people onboard their data, you're like, these are the three things that we find are the biggest opportunities for people to improve their e-commerce Store their their their profitability, their cash flow, whatever it might be, but you're like, these are the things that we seem to see over and over again.
Chase Zieman 33:44
Yeah. So all right, three things.
William Harris 33:48
or two or five? Doesn't matter. Yeah, whatever. Yeah. So it's 3.143 3.14 things
Chase Zieman 33:55
love it. It depends upon their maturity, right? It depends upon how big they are. A lot of brands are going to have different types of problems. But they all have a lot of similar similar problems as well. I do think, number one, first and foremost, creating a single version of right, you cannot have conflicting data, and even a I've been inside of companies. I've built world class analytics programs at Enterprise enterprises fortune one hundreds. It's not uncommon for even inside of a company that has a 50 person data science team to have two reports that have different numbers, right, like that's not uncommon. And so creating a true single version of the truth is, is massive, and going going going in the right direction, right. So that's number one, consolidating and harmonizing all the data together. I always tell people you know, the Attract overview gives you a full funnel of all your marketing. I think that dashboard alone has Over 50 datasets, and one one dashboard, right? single version of truth, first off one single row as number two is the automated decision making, right? So it's my goal and people ask is, am I going to take our jobs and things like that. And, you know, I always bring out the analogy to the iPhone. And when the iPhone came out, you know, and you know, I have a iPhone 14 Pro plus whatever, amazing camera, right? iPhone cameras, top notch. Just because the iPhone had an amazing camera does not mean the photography industry went away. Right, you still hired a photographer, to, you know, a videographer or photographer to take pictures at your wedding, you didn't do it with the iPhone, there's still a need for professionals out there to do top tier quality work. And so what we would love to do from the AI space in, in e-commerce and analytics, is automate all of those simplistic decisions that yeah, you could dig through spreadsheets and through data for 45 minutes and come up with one single decision, or we could just automate 80% of the decisions, right. And even more importantly, you know, we we more times than not, if we're making a prediction in something, we're actually transparent with our confidence level, I'll tell you, I'm 99% Confident in this, or I'm 50% Confident in this. And so you can see a world in in the not so distant future, but what I call human in the loop is, let's say you start letting us make the decisions that are greater than 90% Confident, because you're not comfortable with AI. But then you know, it, maybe you want to review things that are less confident. And so you know, going back to the second point, right? It's, it's how do I allow you to spend more time being strategic and creative in your job, which is what you're hired to do, you're not hired to dig through data, right. And so that's point number two. And then the third piece, I think that is a really differentiating factor of us, and we talked about it earlier, is truly connecting the dots across your value chain. Like you said, finance and marketing and supply chain, they don't normally talk. And it's funny when when I'm when I'm demoing our product, and I hate to go back to talking about demoing, I go into the fulfillment section. And if I'm talking to a marketing person, they're like, oh, I don't care about that. Like, I don't need to see the filament section, I only care about marketing. I'm like, no, no, no, I promise you care about this, because 20% of advertising campaigns are impacted by stockouts. And if I can tell you with a 95%, confident within the next 30 days, and you're gonna run out of this SKU, guess what it's going to do, it's going to torture ro s, right? And so on all the time. And by the way, the supply chain people are like, I don't care about the market, I'm like, but do you don't care about the demand forecast that's bringing in the upper funnel and the visits and the new visitors and the conversion rate trends and the ad spin trends? And I can tell you supply chain guy, you're about to get run out of inventory. You know, that's that's important, right. And so those are, those are the big things single version of truth, prescriptive, automated decision making, and really just connecting the dots. I need an extra point one four somewhere, but
William Harris 38:44
yeah, well, here's the point, one, four, it's the questions that I have for you. Because I agree with you on a lot of things, there's a couple of things that I want to call out at least like somewhat of a devil's advocate, one iPhone photos, you know, in AI, I see your point there. But I would also say like, right now the iPhone isn't taking the photo for you, where you just like hold it up and just let it just do whatever the heck it wants, is just getting the perfect photo, the AI has gotten a lot better where it is, it is starting to do some of the thinking for you. And so I do think that there's a lot more opportunity for to remove jobs. And I want to say it was IBM just this morning announced that they were going to use AI to replace a lot of jobs. And I want to say it was something to the effect of like 7800 jobs are saying we're just not going to hire for these jobs that we believe AI can do. I do think that it is going to come for some jobs fair. Interestingly enough, what I would actually say is interesting enough, I see it coming for potentially, let's say like outside of the advertising world. I can see it coming for doctors jobs faster than it comes for nurses jobs. We go back to the medical side. What are the reasons for that is because yeah, doctors are diagnostic, right? Whereas the nurse, it's a lot harder to replace the nurse with some type of an AI there's just the need of like actually having to go and assess this patient or something. But the doctor you're taking on all that data and making an interpretation Have all of the data that's, that's largely gonna be able to be replaced very quickly to some point with AI.
Chase Zieman 40:06
Yeah, I don't disagree, right? Because if you think about the your normal visit to a doctor's office, the nurse comes in, they talk to you, what are they doing? They're gathering data, right? writing it all down, right. And then after all of that data is gathered, it all gets, you know, consolidated together, and the doctor comes in to do what make a decision, right? But any, like, in any AI space, it's garbage in, garbage out. If you if you give me a problem with good data, we could probably figure out a way to create an AI model to do you know, make a decision. But to your point, that human care piece on the nursing industry in general, they, yeah, gathering on your feet, right?
William Harris 40:58
Well, you can, you can build tools to gather that data automatically. But the cost of building the tools is, is I'd say, significantly more than the cost of just building out the rest of the AI because it's already there, you need a tool that's going to do all of this stuff, and be able to assess somebody's pupils, and whether they're react, you can build that out to do that automatically. I just think that the machinery and everything there is is not as cost effective as just hiring a person to go ahead and look at the pupils and see Yeah, okay, the reactive to light right now. The other thing that you caught up to is the single source of truth. And I want to I want to clarify on this too, because I like the idea of single source of truth. And what I see though, and I want to make sure I call out for people that are listening, I see a lot of people that say great, our single source of truth, is this last click touch attribution, or whatever that might be. And it's like, yeah, that's, that's a crappy single source of truth. Have you ever? Have you ever read or heard of the book flat world? Is that one that you not? Okay, so flat world, is a book that talks about the idea of what it would look like to live in, if you were on a completely two dimensional world, and what would three dimensions look like you and so, you know, let's imagine you've got this ring here. And I take the spring off, and I pass this ring through your two dimensional world, you're on a piece of paper, you see a line and then you see two lines. And then two lines that are further apart, two lines further apart further apart, closer, closer, closer back to a line, right? Like, that's what you saw, pass through as this ring went through, like this sheet of paper right through this playing, you could eventually understand that this was a ring potentially, by being able to understand and model that out in this dimension that you don't even understand. And that's how we begin to understand what fourth dimensional things look and act and behave like is by understanding how they appear in our three dimensional world. It's a very fascinating thing. And I want to come back to quaternions, by the way, but aside from that, the other thing that's interesting then with this is, when we talk about a single source of truth, I look at that going back to, let's say, an x ray. And what a lot of people's single source of truth is just a partial truth. And an x ray, you know, I've broken my wrist before and they're they're going to do the AP view, they're going to do the lateral view, they're going to do a couple of views to be able to see, okay, but what does that break look like in three dimensions, if I look at just the single source one image, I'm only going to see part of what that break looks like. And I'm going to maybe not understand the severity of the break not treated correctly, or whatever that might be. I think we see that with data all the time as well, where, yeah, we say we have 360 degree data, we say that we have a lot of, let's say, a single source of truth. And what I think for a lot of people that means is one bad, incomplete source of truth, rather than still using multiple sources of truth, but Unifying them in a way. And I know that's where you get to with unified analytics. But what I'd like to see, and I haven't seen anybody do this yet, although I've been working with some people over at MIT to see if I can graduates of MIT to see if we could put together 3d visualization of that data, like I actually want the data to be modeled in a three dimensional, you know, cubish type thing where I can say, I can see visually the changes that have taken place over the data over these periods of time. And that's where the quaternions could come in. Because you could actually see this even add in, you know, four dimensions worth of data now, instead of three dimensions of data. But yeah,
Chase Zieman 44:11
yeah, I mean, it's interesting concept, right? I could see a world where you could explore data and basically start rather than asking, you know, what was my return on adspend by channel, you could start asking yourself, How does my return on adspend? Change when? Right and so like taking that derivative, and start taught start thinking about data in terms of how is it changing? How's my CPC changing when how's my row as changing when how you know, all of these types of questions and then you start thinking about those sensitivities, and those relationships between all of the data points because I guess the only concern right you go to three dimensions because it's not just three dimensions. It's it's many, many, many dimensions, right. And that's the power. You know, we have some super, super bright people on our team, just like some of the leadership team alone is one guy was part of the engineering team that built the first virtual warehouses at Amazon. We have a guy who has, you know, PhD and generative AI who worked on software on the Mars rover for autonomous driving. You know, folks from IBM, NASA, Home Depot, Alteryx, Amazon, Google, you name it. And so we, we, we have a group of folks that, you know, you said earlier, is marketing people that are just trying to drive, put math on top of marketing. And so we have expert marketers combined with not just experts in data science, but experts in data science, from the Commerce Industry, as well. And, you know, we've been lucky enough to raise $400 million so far and do a lot of things at Cart and what where we're at right now is just the beginning. Right? We we do the analytics product alone became generally available last summer. And you know, we've we've come this far already, and we're on a tear, but I'm excited. I think some of the next six product releases and even this next six to nine months are going to be pretty, pretty amazing.
William Harris 46:31
I see that with just AI in general, like everything that's taking place. In this whole whole AI world is massively changing. It will be one of the fastest changes I think we see in the next decade to to every industry. I want to transition here to a little bit about who is Chase Zieman learning a little bit more about some of like, what makes you tick and who you are. Do you have any weird quirks where if I was in an office with you, I would find out? Oh, you're you're the guy who you know, chews bubble gum, you know, with his mouth open or whatever. This might be.
Chase Zieman 47:11
weird quirks? Yeah, I mean, I got some weird office Quirk. I love so number one. I don't really like touching food. Despite me loving to cook, one of my favorite hobbies is cooking. I love to cook. I loved growing up from rural Louisiana as well. So love a good crawfish boil. But in general, I don't like to. And so because of that, and and by the way, I love Asian food. I pretty much eat anything with chopsticks. And so it's actually pretty bizarre, you know? Yeah, you look at my top drawer. There's a just probably 100 pair of chopsticks sitting right there. And it's always interesting when new employees come or somebody passes by my office, and they see me like eating chips with chopsticks.
William Harris 48:03
They have that though. Right? There's like a little like, finger thing that's like, my while you're still doing this.
Chase Zieman 48:11
Yeah, my wife saw that the other day. And she's she said, Oh my God, you need these things. And so I've been I've been eating everything with chopsticks for years. It's just a it's just a great vehicle for food in general. And you can you can just grab something. So that's, that's
William Harris 48:28
one I can use to at most.
Chase Zieman 48:31
Yeah, that's great question. I mean, I normally just buy the bamboo dispose owes off. Yeah, you know, off online, and they come in a 200 pack and throw me to get them away afterwards. I do have some nice chopsticks that were present. But you know they a nice nice bamboo. Or wooden.
William Harris 48:56
Yeah, nice. What some? Is. I appreciate that. I appreciate simplicity. What is uh, what's one of the craziest things that you've ever done? Is there anything you've ever done? That's just like, This is crazy. And I can't believe that I'm even going to share this on a podcast.
Chase Zieman 49:12
Man. He's thing I've ever done sharing on a podcast
William Harris 49:25
that's a tough one. So if there's not a
Chase Zieman 49:27
no, yeah, no. I mean, there's a there's a tough one. I mean, I think the so last year now a few years ago now, three, three years ago when we were living in Colorado. I was at Vail snowboarding. I love to snowboard with some friends. And we had just gotten it had warmed up the previous weekend and they And we gotten like 334 inches of powder. So warmed up snow had melted, it got really cold, it froze. And then we got three, four inches of powder. So anyone who knows that anyone who skis knows that there's ice under that powder, right and you're, you're at risk of slipping very easily. And so I had, you know, we were snowboarding out there and I'm I would consider myself a solid intermediate snowboarder. I wouldn't consider myself an expert. I'm not doing backflips or anything, but I can generally keep up with people that are experts. And some of my friends took off dislike tips down, down this like double black diamond in Vail and and flew down. And there was like an easier way where I could have probably went like, Screw it, we're gonna go down. And the problem is, I mean, it's so steep double black diamond, there's no there's no room to like, slow down, especially on a snowboard, like you can't just carve sideways, especially when the strange powder sitting on top of ice. And so I I go down and I tried to slow down immediately fall I literally thought on the whole mountain. Like the whole mountain. My friends, they, they were like, This guy's going to the hospital immediately. And so like looking back on it like this no reason. I should have done that I knew this was a bad idea. And then my whole body's hurting a my friends come up and they asked me if I'm okay. And I'm conscious. I had a helmet on obviously. And, you know, retired for the rest of the day. And I my body was killing me. So like I went back to the condo ended up going home back to Denver area. And just severe body ache. And I thought that, like I was gonna have to go to the hospital because my body was hurting so bad. And just hurting no broken bones. Nothing. I mean, I thought that I thought that I was dying on the inside, literally. And I didn't know I didn't know what it was. I didn't know what was wrong with me. So I did have bruises and stuff. So I ended up going to the doctor and I started kind of getting some flu like symptoms. And this is February of 2020. February 2020. I go to the doctor, and they look at me. And they say, Have you heard of COVID-19 This is before COVID-19 Hit the United States or like there because it didn't it didn't really hit until q2 late q1 into March. So she said I'm not supposed to test you because you're like a healthy young male. But I've never seen a case yet. I want to test you. And don't test me. Come back. They they call me and they're like, Oh yeah, I call the back. They say everyone we tested this negative. And COVID-19 was hitting the United States and Okay, fine. And then they ended up calling me back. They're like, Oh, yeah, we made a mistake. You have Coronavirus, I was actually like case number 17. Wow. And in Colorado ended up I'm Oh positive ended up donating tons of blood. But it's like full circle. I'm laying in my bed thinking that I'm dying from going falling down the mountain. But in fact, it's actually dropping. But just but so I never know to this day, like how much was COVID versus how much was my body hurting from literally falling down a mountain, which is kind of fun.
William Harris 53:52
That's wild patients. 17. Um,
Chase Zieman 53:55
okay, in the whole state, not just a which
William Harris 53:58
is just, yeah, that's crazy. Thank you for spreading that out.
Chase Zieman 54:02
It was kind of cool. Yeah. Right. It was kind of cool, though, because I was part of the experimental program in the United States for blood transfusions for plasma treatment. Because they contacted me and they they said, hey, you know, we want to we think this could work can you start donating plasma so that we can see if this would work and it's kind of cool. I got this, this little piece of paper that basically said I didn't have to wear a mask, because I was working with working with the Colorado Department of Health to donate plasma, because my plasma, my the reason that they weren't able to stay, weren't able to state that you didn't wear a mask because they didn't know how long the antibodies lasted. And after after you had COVID but the trick though, for me was I was going to the hospital every single week and they were testing my antibody levels. They were testing my antibody levels and they were getting my plasma and then they were doing And transfusions. And so since I'm constantly getting my antibodies levels checked, I had this like, Get Out of Jail Free card, which is
William Harris 55:08
and we all hated you. Well,
Chase Zieman 55:09
everyone hated me. Right? put your mask on. What's your band?
William Harris 55:14
Um, okay, I see that you've got a lot of books in the background too. Yeah. What? What? Favorite book out of the ones you've got back there? What's a book that you're like, Hey, you guys should read this, or it has nothing to do with business. You're like, I just love this book.
Chase Zieman 55:27
I think my favorite book ever is radical candor. I'm not sure why I I've read that book many, many times. I think I think it's a book to where if you get a group of people that have all read the book, it can be somewhat of going back to translation factors, right? We talked about product level attribution, being a translator, you can, you can start molding extroverted and introverted personalities together, right. It's a book that some books work for certain types of personalities. I think that that book is someone that really brings people to the middle it, it teaches maybe pushy, extroverted people how to be softer, right and respected. And then it also teaches introverted people that maybe don't speak up enough how to be more outspoken and maybe get their way in, in a world. And so I have bought that book for many, many teammates over over the years. And I think it's a book that just it's one that applies to all types of personalities and, and really kind of creates a gel right in in a team. And it also just allows you to move faster, right? If there if there is that radical candor, I'm a huge fan of 360 degree feedback. everyone on my team knows that they can give me feedback, you can give feedback sideways, up down at all times in a respectful way. Right. We also give each other a little bit of we have a little bit of banter, sometimes in a funny way. And I think it just breeds it breeds a great culture.
William Harris 57:10
Yeah, yeah, that feedback is crucial that I've heard somebody say before that it's like if you don't give feedback, even criticism, but in a kind way, if you don't, you don't actually care about that person. There needs to be some element of hey, I'd like to share this, especially within the workplace. What about last thing here? Before we run, show until opportunity. I understand because we talked about this earlier, so this isn't a surprise to me. Please show us something that is in your office that is special and unique that probably a lot of people don't have in their office.
Chase Zieman 57:43
Yeah, yeah. So we did talk about this earlier. So I there's a people up here.
William Harris 57:50
I want to give you guys a music concert. Here, right?
Chase Zieman 57:52
I've been playing the trumpet since I was a kid and played throughout high school also played in college and the LSU marching band was a lot of fun. One of the reasons actually went to LSU over Duke is to play in in the band. I grew up watching the band. So still play not as much as I would like to but do weddings funerals really? Yeah, yeah. Last thing it did was it was a funeral. So a little bit sad. But if you have someone close to the family, their eyes like hell cheese, like so. Yeah, that's something that I love trumpet. But his hobbies trumpet golf, cooking. Playing Yeah, my son have a 19 month old now. So he takes he's like the number one hobby. I love it all. And you see all of his little math books that I've gotten him back. Organic Chemistry for babies, Bayesian probabilities for babies right here perfect. You can never stop to earlier can never stop them on rarely
William Harris 59:03
Simpsons paradox as well. So you and I share a love for the trumpet actually as well. I played the trumpet all the way through high school as well. My My mom was a band director. She played the euphonium I think we were talking you know took him to a solo ensemble you took your solo and ensemble you know superior ratings all the way through. You know, I think I took it you might know this and other band people know this one but Allegra spirit Tosa was the one that I took I want to say as a freshman, which is a very very advanced piece and got a superior rating on that as well and loved loved playing the trumpet and just being able to make music and just everything with that too. So maybe you you do you think that you would play a quick song for us or are we a little too rusty for that
Chase Zieman 59:49
on the on the bugle man so the first off this is just like a this is a bugle for a shelf. This is not the real one not Starbucks Stratovarius No. But we could we could try. I'll see.
William Harris 1:00:04
All right, let's do what we got
Chase Zieman 1:00:16
all right. Not not not warm for sure. Hasn't been played that thing hasn't been played since college at like 3am in the morning, right?
William Harris 1:00:27
I love it. That's great. Chase, if people wanted to follow you get in touch. What's the best way for them to do that? Yeah. So
Chase Zieman 1:00:36
LinkedIn is obviously super easy to get in touch with me for slash Chase Zieman here, and Cart.com as well. We have information pages there, but shoot me a direct message on LinkedIn. That is the easiest way to get in touch with me I promise I read every single message that I get even all of those PRs all those videos that come out as well I will respond to you. I may tell you I'm not interested in your your software. I may also give you feedback on your on your outreach, but I will read it. And I will respond. I promise you, I promise you that and so hit me up on LinkedIn and we can you know, any sort of partnerships we could be interested in or interest in car.com any products and services or just nerding out, always love to nerd out as well.
William Harris 1:01:31
Awesome. Chase, I really appreciate you coming out and giving us some wisdom here today.
Chase Zieman 1:01:35
Yeah, appreciate having me William and always good talking to you.
William Harris 1:01:39
Thank you everyone else for jumping in and listening to us today. And if you have any other questions, go check out Chase Zieman there and check out element E-L-U-M-Y-N-T.com as well. Have a great day.
Thanks for listening to the Up Arrow Podcast with William Harris. We'll see you again next time and be sure to click Subscribe to get future episodes.