121 — Data at Scale: How Walmart Luminate Powers Retail Innovation with Mark Hardy

Lenny: 00:00:00

Hello, everybody. It’s Lenny Murphy with another edition of the Greenbook Podcast. Thank you so much for taking time out of your day to spend it with me and my guest. And all of our guests are just amazing and awesome, but some are just a little more interesting in some ways, and [laugh] today is an example of that. So we are pleased to have Mark Hardy, the head of Walmart Data Ventures, joining us. Hello, Mark. How are you?

Mark: 00:00:38

Very well, thanks. How are you doing, Lenny?

Lenny: 00:00:41

I’m doing all right, man. It’s good to have you. Now, I’m going to have you kind of introduce yourself with your bio, but I want to just put a little context there. You are a hero to so many because, you know, you’re the success story—someone who came up through the trenches on the supplier side, on the sample side of the business, at that, and then you created InContext Solutions, and now you lead Walmart Data Ventures—and what an amazing story that is. We’ll get to that story, but give our audience a little more information.

Mark: 00:01:09

Yeah, I’m not sure I’d ever use the word hero. I’ve been very fortunate to have the opportunity to work with phenomenal people throughout my career in the data industry. So my background, basically, I started a long time ago in the CPG space working for PNG, and that’s where I got the bug for data. I was in the early days of category management, really looking at warehouse withdrawals at the time, and getting exposed to some customer data. And then I evolved into the secondary research arena as that industry moved from warehouse withdrawals to point of sale, which was really exciting, really allowed us to elevate the use of analytics and insights at the time. Then I evolved my career with the industry in the late nineties, jumped over to the dot com business, starting up the first online panels and full service primary research companies within Greenfield Online—the good old days.

Lenny: 00:02:05

The Greenfield Mafia.

Mark: 00:02:07

Yeah, that little mafia. And then rode that out through the dot com bust as well, and continued to get involved in the primary research industry, really trying to productize what used to be project based—and went through a lot of mergers, acquisitions roles where I was trying to turn around companies, and then jumped into the VR/AR craze as well, bringing simulations of retail into the business for suppliers as well as research companies. And then, from there, I had the privilege of coming to Walmart, and at Walmart, what they did was asked me to figure out how do we create a data business within the organization. So I’ve had a very fortunate career, met a lot of great people, and worked for some tremendous companies throughout it.

Lenny: 00:02:53

That’s very cool. I remember the first time I saw InContext Solutions, for instance. Just amazed at what you were doing at particularly trying to scale that business, make that a platform versus just a project. So, anyway, my own experience of following along over the years. But let’s talk about Walmart Data Ventures. Because I think probably from many listeners, Walmart obviously captures attention no matter what [laugh], right, in any way you mention it. But as you started making moves as part of this remit of creating a data business, even before the launch of Walmart Luminate—I know I personally have used you as an example quite often of paying attention to what’s happening here. You know, this is where the future is going, and you’ve been shaping that over and over again. But kind of give us the inside view. What has that journey been like within the culture of Walmart, as well as through your perspective of the—you know, where does that fit in the world of, you know, our market when it is a pretty different play? It’s not like the old Dunnhumby days. I mean, it is radically different than these other forays that we’ve seen happen in the past that we’re just very focused on “sure, you can get our purchase data.” Right? This is a lot more and bigger than that, right?

Mark: 00:04:08

Oh, yeah. So when we embarked on this journey, our remit was to create value out of Walmart’s data. Naturally, everybody gravitates towards monetization, and it is part of our output or outcome. At the end of the day, a lot of companies, especially retailers, are looking for alternative revenue streams to be able to subsidize a reinvestment back into their core business. Right? And that’s the reality of the business today. And the reason why that’s becoming more and more important in our world is that you just think about the world around us when customers want more privacy, but at the same time, they still want more personalization. And in order to achieve that, first-party data is becoming more and more important. Walmart naturally sits on a lot of first-party data, as well as a lot of first-party relationships with our customers. That is what allowed us to then think about what does creating value mean above and beyond the ability of creating new revenue streams to reinvest back into the core business. Creating value for us meant how do we start bringing all this data together that allows our business, our merchants, our operations, our store personnel, to get better visibility on how to service the customer better, right? If you think about our world in retail, just even 20 years ago, a Walmart Supercenter had 120,000 products in it. A customer would walk in and their selection, their choice is what was in that store. Fast forward to today. Our stores are still the same size with the same 120,000 limit of SKUs in it. However, we serve over 20 million first-party products when you look at our omni business. So how do we ensure we get the right product in the right place at the right time? So we, as consumers, are now spoiled. We live in this world where we pull out this digital phone or any other device. We can order any product or service we want, anytime we want, and have it delivered at our doorsteps or have it ready at the store for us to pick up. So how do we, as a retailer, get ahead of that? How do we, as a retailer, become better at predicting what our customers want and when they want it so that we can provide the best experience to our customers? The solution to that is around data. It’s about being able to bring disparate data points together and making sense of them so that we are proactive versus being reactive. So, when we looked at creating value out of data, that was on our forethoughts of what we needed to be able to do. It was not about taking single pieces of data, or streams of data, and being able to just look at them the same way that we’ve consistently done. I think the advantage that we had was—if I look at my career, I’ve been privileged of being able to work with supply chain data, working with secondary research, primary research, visualization of data, simulations—being able to see the value that each one of those data points had on its own. Being able to bring them together and understand how they fit together is what we believed was the next generation of where the value would be achieved from the data that we had.

Lenny: 00:07:24

Now, if I remember correctly, a few years back—well, first, when did you join Walmart Data Venture?

Mark: 00:07:30

January of 2020, just before the pandemic.

Lenny: 00:07:32

[laugh]

Mark: 00:07:35

You got to love that. Your first few years, you don’t know anybody face-to-face.

Lenny: 00:07:38

[laugh] We’ll take all this data—people in our stores. Oh, crap. Wait, the stores. [laugh]

Mark: 00:07:46

[laugh].

Lenny: 00:07:47

Auspicious timing. Right? Okay. Prior to that, if I recall correctly, Walmart was making acquisitions, or at least investments or some combination thereof, in kind of analytics companies. So this journey of recognizing that there was value in the data, I assume, had been there for a while before they asked you to say, all right, now let’s take this to the next level. Is that right? Am I remembering that correctly? You know, seeing some investments being made around big data?

Mark: 00:08:15

Yeah, I think if you look at the heritage of Walmart from early days, they were very much focused on the value of data that would allow them to run their stores better, right? As a chain who really spread themselves out into a national footprint, they had to have a better way of collecting data, making sense of the data, so they can then make sure that the products got out to stores and we serviced our customers to the best of our ability. So Walmart has always been a data company, rigth? And they’ve understood the importance of data. I think what they’ve seen in this evolution, as we brought out Walmart Luminate, which is our product that we introduced to the market, was new forms of data. A lot of the data that Walmart really focused on was their supply chain data, and they’re experts in that. Right? Bringing customer data, bringing the voice of the customer consistently into decisions was with something new for Walmart as well. We did it as projects but not necessarily as an ongoing piece of a business decision process. Right? And I think that’s—if I were to be fair, I would say the same is true with our suppliers that we engage with. As we’ve launched Walmart Luminate—and I’ll explain a little bit about what Walmart Luminate is—we’ve engaged with suppliers on very sophisticated in data. But again, it’s always a project mindset versus how do you enroll this data into an ongoing way of thinking of the business. And in fairness, some of the data that we bring to the party is data that some of our teams have never seen. I’ll always use survey data, for example. Survey data tends to sit at headquarters or in specific groups within companies, not necessarily in the hands of our merchants or the hands of the sales teams who are calling on Walmart. Being able to bring that voice to the customer—a methodology that has existed forever in survey research, but now putting it into a more agile methodology where we can bring the voice of the customer to the table every week that you are talking to your merchant. Right? Being able to have that as a collaboration platform is what really changed the game between suppliers and merchants. So to explain what Walmart Luminate is—because I already use the word—we launched about two and a half years ago a platform called Walmart Luminaire. This platform was intended for a collaboration platform between merchants and suppliers to be able to accelerate our business and accelerate the decisions, bringing new data to light in a way that was easier to manipulate and gain visibility. When we launched that, we launched it with three modules initially. It was: shopper behavior, which is our tender, traceable data, being able to see shopping behavior over time; our channel performance, which allowed us to look at our supply chain data and understand what products were where and what our inventory levels were; and then lastly, customer perception—the ability of really being able to engage a Walmart community of customers with an invitation-only, double opt-in approach that allowed us to be able to target those customers based on their buying behaviors. So we didn’t have to screen customers and understand whether they bought a product not based on what they claimed, but rather we understood what they bought or what they lapsed in buying based on their actual behaviors within Walmart. That really started changing the game with us over the past two and a half years. And then we added to the platform. Over the last few months, we launched digital landscape. Because up to that point, we were seeing post-purchase behavior. With digital landscape, it really looks at the behaviors online. How do people shop at walmart.com or in our app? What are they browsing for? What are they searching for? What is their path to travel to conversion? Being able to understand that pre-purchase behavior gives us even more visibility. So ultimately, when we built out Walmart Luminate, we really looked at it in two different journeys. First, the journey of a product from source to shelf, and then the journey of a customer from home to store, ultimately coming to that point of conversion. If we can understand the customer better and we can understand our product journeys better, we can become better at servicing our customers and really impacting their experience. That is what Walmart Luminate was intended to do, and that’s what we are seeing today. And it’s evolution. It’s change management at [laugh] its best. We’re seeing whether it’s an internal audience or external audience. It is forcing people to level up and to think different.

Lenny: 00:12:55

Yeah, I remember that when I first spoke to Linda about the launch of Luminate and that sense of the world’s changed. Right? As I mentioned, other folks have played in this arena, but it seemed very different. And being Walmart, my assumption has always been, you’re in it to win it because [laugh] that’s what you do. Why else do it, right? You weren’t dabbling like we’ve seen other forays into the industry. Right? They always seem more dabbling. So, when you started, the basis was primarily serving within your ecosystem of suppliers. Right? So your— the Luminate customers were the suppliers of products that are being sold through Walmart primarily. Is that right?

Mark: 00:13:39

That is correct.

Lenny: 00:13:40

Yeah. Has that changed? Has it opened the door now to whether you’re a Walmart supplier or not?

Mark: 00:13:47

So within Walmart Luminate, it continues to be a platform collaboration between our suppliers and our merchants. That’s the main purpose of it, right? And I say our merchants, but it’s actually within our suppliers, all different operational organizations within theirs, and our different operational organizations as well. So we are using Walmart Luminate even to help address store-level issues with our store operators, as well as merchandising organizations that help our suppliers make sure that retail is executed, their plan at retail is executed well. So it is opening up to other related ecosystems to our suppliers. And that is where we are focused today with Walmart Luminate. But as Data Ventures, we are also looking at how do we leverage our data with other markets where we may have an impact in creating value. So we are constantly looking at the horizon, and we’re doing proofs of concepts to understand whether what we can provide provides a view of the world that was not previously there before.

Lenny: 00:14:50

I love that view. All right, so we’re going through all that, and along comes AI, right, which I consider to be just an enabling technology that just unlocks so many things that we’ve been thinking about doing. It just makes it easier, right, to do a lot of things, but it’s also created a butt-load of disruption. So now, kind of put on my market research industry analyst hat for a minute, and here’s kind of my take on the state of play. There’s these dynamic forces of change. On the positive side, that data’s never been more valuable. I mean, truly, data is the new oil. Hell yes, it is. And everyone recognizes that. It’s also ubiquitous. But making use of it, that’s a different story. So we’ve got that good thing happening, right? A lot more buyers, you know, the ecosystem is expanding. For the existing market research industry, there’s a downside to that, and that is the disruption from players like you, from Walmart, Luminate, from the 1,001 new AI startups that just came to market this week it seems, you know? [laugh] The realization that unit of value—that market research supplier community, particularly, has focused on, which is the project and even the interview, the completed interview, is not necessarily the driver of value going forward in the future. The driver of value in the future is the data, how that data is utilized to answer the business question, which increasingly will be from existing data rather than new data. And the focus of new data—this is my hypothesis—is what I think of as last-mile data. Right? Now let’s fill in the gaps of information. And that will increasingly be experiential, customer focused. Right? It’s one thing to be able to profile somebody’s behavior, but what do they think and feel, you know, at the shelf or in driving that decision? Or what other situations happen in the world that rapidly changes people’s priorities and making those decisions, et cetera, et cetera. So we as an industry have to adapt to thinking about data as the asset and making sense of the data as the output versus collecting the data as the asset. And I think we’re seeing—we’re recording this at the very end of June, on June 25, and even in last month, right, there’s what I think are canary in the coal mine signs: the headwinds that many of the sample companies are facing, the issues with YouGov. Morning Consult is laying folks off. And I think about what they have in common. It’s because they’re all about the survey. They’re all about—it’s not about the methodology. It’s about the project. Right? And, but yet they’re sitting on this huge, massive asset that they’ve failed to monetize and seem to struggle to how to do that. But you get the best both worlds. You’ve got all this data. It makes a whole better experience, answers the questions without needing to be asked, but yet also gives you the pathway into being able to ask the right questions to add to that and then create this—the entire marketing lifecycle. So first, that was very long winded. My apologies for that, trying to frame all that up. Is that your read as well?

Mark: 00:18:07

Yeah, it is. I mean, if you think about it—and you’ve hit upon it. You know, I have almost 30 years in the industry, whether it’s secondary, primary research, panel companies, simulations. It’s not often that we ever asked ourselves what was the return on investment that that client made on us, right? We would deliver whatever it was that we were engaged to deliver, and then we were off to sell the next thing, pursuing that revenue. What’s interesting is I can sit here and tell you that within Walmart Luminate, we have a great idea of what the return is. I mean, some of our clients come to us and go, your subscription is x. And we get a six x multiple on our subscription to Walmart Luminate. Right? And that ranges. And again, it goes back to how well do you use the data and how well are you driving decisions? So it’s more than just the data, but where else does it go to help you make those decisions that are going to drive the business? But we consistently hear from our clients—and it’s not just the large ones. So 90 percent of the largest suppliers at Walmart are subscribers to Walmart Luminate. But 50 percent of our subscriber base are small companies. So just because you are large doesn’t mean you are the smartest with the data. We have seen very sophisticated data companies, and some of the smallest suppliers, coming out with great ideas. What Walmart Luminate has done is really even the playing field across all size companies to really allowing companies to leverage data to make better decisions. So it’s not the fact that you are bigger and have more resources. it’s what do you do with those resources. So back to your question. Because of that, we focus a lot on how do we continuously drive ROI. So we’ve connected Walmart Luminate into Walmart Connect’s Ad Center so that you can take the insights straight out of Walmart Luminate and pass them into the Walmart Connect Ad Center so that you can actually target and be more relevant in your positioning and your messaging to customers to drive your OAS. We are looking at how do we take our data and improve the ability for our suppliers to deliver products to the stores that need that product at that time. our suppliers are using our data to actually make new product development and positioning decisions, even though they are national products. We are creating an ecosystem that allows you to take this data, engage with our customers to be able to test new products, be able to see the outcome, be able to then from that outcome, work with Walmart to then understand what is the right deployment plan. Where do you go to market? Who’s our target audience? We’ve helped suppliers actually redefine who their target audiences are because they understood them better. Part of what I see in this—and again, it goes back to return. We’ve seen it through the Insights Association studies that they’ve done. We’ve seen it in other industry publications as well. The number one thing that I get concerned about is data quality. We sometimes know it’s there, but we overlook it. So when you bring up all these companies that are struggling right now, from where I sit, I wonder did we just wait too long to address data quality issues? I mean, me as a survey respondent, I would never sit behind a 45, 50 minutes survey. I wouldn’t have the attention span or desire. Right? So why do we think others will?

Lenny: 00:21:33

Yeah, well, as far as part of the Greenfield Mafia, you know how the sausage is made.

Mark: 00:21:37

So, yeah, it’s like the client has a lot more questions. They’re paying for it. Let’s add more questions. You know, at Walmart, what we do is we respect our customers. So we actually, within our community, allow the customer to tell us how often they want to engage in surveys. We then engage them on their terms, not our terms. It changes the response rate. It changes the quality of the responses that we get. We’ve actually done comparisons between data that came out of Walmart, Luminate, our community, and data that came out of some leading panels for new products, and we fielded the same product concepts. And the decision to go/no-go were diametrically opposed. When you use data that comes out of our community versus data that a panel, it was basically a whitewash. You know, you could do top two box, bottom two box, you could make your story out of it, but it wasn’t a clear choice, where there was a very clear choice of what you should do on a go/no-go decision. And it goes back to are we really respecting the people in primary research who we depend on to provide us insights? Right? And how does the data change? And this is not just the panel company’s issue or a primary research company issue. It really goes back all the way down the value chain from when it starts with a client being able to respect the people are going to give us those opinions. It’s no different than when we start looking at secondary research or other methods of collecting that data. Right? Whether it’s receipt capture or other POS data, are we really looking at the data and understanding the quality that we are getting and not just turning a blind eye to it? Right? So I think that’s one of the big things that I think is driving what we’re seeing out there is the fact that we’ve probably allowed it to go on for a little too long. I also believe that what we are doing within Walmart Luminate, we’re not the only ones. I think you’re seeing some mergers and acquisitions across primary research, secondary research, and other companies, right, trying to pull that same view of the total customer together. This is not the first time it has happened in this industry. It happened, you know, 20, 25 years ago as well. I think we’re in a unique situation because, again, we own the relationship with the customer. We have over 150 million transactions a week. We have real scale and data that allows us that granular visibility. Right? So that’s an advantage—hard to match. But part of the reason we embarked on this journey— because we needed better data. Right? If we had better data, maybe our journey would have been different. Right? So within Data Ventures, I always challenge our teams to ask two questions: what’s the problem you solve, and what’s your right to win. And it’s interesting how many times we just walk away from something because the problem may not be there, but if it is there, we actually cannot find a better solution than what exists already in the marketplace, so there’s no reason for us to tackle that. The only time we actually go tackle a problem is if we believe we can do something innovative that really changes what we deliver to our end customer, whether that is Walmart internal stakeholders or whether that is a supplier in itself. Right? So this is where I think the market and our industry is going to see a lot of change coming because we’ve taken—the world is moving really fast around us. And sometimes we are very conservative, and it takes us a while to embrace innovation. And I think we’re going to be pushed in that direction where we’re just going to have to take a little bit more risk and embrace innovation a little bit faster.

Lenny: 00:25:16

I couldn’t agree more. You know, we have a live show that I do every Friday with my colleague Karen Lynch and going over the news of the week. It has turned into the, you know, the weekly AI news [laugh], really, for the great extent, but we have a little shtick where we have a stuffed horse, and we just beat it now. You know, we just pick up—and like, come on, guys, it’s that same message. We’re going to beat this horse. The you cannot—this train already took off. Now we’re playing catch up, and there is no option not to adapt, not if we’re going to stay relevant.

Mark: 00:25:53

Yeah, you mentioned before data is the new oil. And then you’ve also mentioned AI. And it’s like when I take a step back at everything that I’m seeing and as I look out there, we get excited about new buzzwords: AI, GenAI, and everybody jumps on it. And what I challenge our team and I challenge others to do is it goes back to, is that the right approach? Right? So not a lot of people think about, if you’re starting a business or you’re managing a business, you need to make sure that it’s delivering the financial return you need. Right? And so we use general data, and then we apply GenAI because that’s the newest thing to talk about, and we get excited about the output. Now, the compute that that took is a lot more expensive than using AI. And if we use general information when it goes in, what you’re going to get back is a lot of hallucinations. Right? And that’s where you’re going to start seeing a lot more acceleration around domain specific data to be able to feed those models. I think you’re going to see a better separation between AI and GenAI and the use cases around them, right, and how to be able to interpret it. Right? When we look at our customer service and we respond to inquiries about our products, I don’t want two different answers for the same problem. So that’s AI’s approach. However, they may have asked it differently. So I may use GenAI to be able to interpret what was asked to then guide me to where I go. So again, not an expert there, but knowledgeable enough to understand that we need to really think about what we’re doing, not just get caught up in hype cycles, because that’s what unfortunately doesn’t allow you to do a scalable business.

Lenny: 00:27:29

Agree wholeheartedly. We could go off for a long time on that. I will say that I’m beginning to think, for my own purposes, there’s demonstrating AI from a workflow standpoint, right, and the benefits that it drives for workflow and development efficiency. It’s just a new tool that does that. Versus where we all think about AI is the generative AI component, which I totally agree, it’s really great at synthesizing information. It can answer a lot of questions, but it’s—we’re a long way away from it being making a billion dollar decision based upon that synthesis. Right? So it’s great for exploratory test hypotheses, you know, certainly creates massive amounts of efficiency gains for secondary research, desk research, all that stuff. Sure, got it. Absolutely. But I totally agree with you that the domain specific LLMs are the next thing that need to happen. And I also think that’s an amazing opportunity for research companies, for panel companies, especially, if they start thinking about being in the data business of the project business and if they start building out their own data assets versus everybody else’s data assets. And Walmart, certainly, you fall squarely in that category. You can create that domain expertise all over the place. So I want to be conscious of your time as well as the time of our listeners. Think 24 months from now, right, two years out, what does Walmart Data Ventures look like to you? What’s your vision? And where does the existing industry kind of fit in that? How do you think that we’ve adapted and aligned to all these different things that we’re talking about?

Mark: 00:29:14

So can’t actually give you all the specifics for natural reasons, but I would say—

Lenny: 00:29:18

Sure [laugh]. Right.

Mark: 00:29:20

--when you look at what we’re doing, we are maniacally focused on gaining more and more visibility on the journey of the product and the journey of the customer. So we are constantly evaluating where else can we get data they—integrated into the platform we built, gives us new insights, new visibility to help us make better decision. At Walmart, the customer is at the center of all our decisions. So, therefore, being able to understand what is important to the customer is there. But then being able to make sure that that product is at the right place is ultimately how we win. Right? So I think what you’ll see us doing over the next 24 months or so is continuing to fill the gaps where we see that we gain new visibility. Right? I think you also see us going more towards predictive and prescriptive from the core data that we have. How can we now apply it through models to be able to help us be ahead of the curve, be predictive, not reactive. Right? So you’ll see a lot more of that. Those are the areas I think you’ll see us continuing to grow in. And, you know, it’s hard to say there’s one specific area because I know we’ve been approached a lot, and we’ve looked at different areas that you would think are normal that we should have. And we haven’t because when we look at them, we can’t see how what we would produce would be different than what exists there. So we can never define our reason, our right to win. If we can’t define our right to win, it means there is no better alternative than what exists in the marketplace today. And therefore, our clients, whether it’s Walmart or our suppliers, should be happy with what they have. I think what you can expect from us with the industry is we’re constantly looking for partners, but partners to innovate, partners to do something where one plus one equals three, not reselling or just creating partnerships that don’t benefit Walmart, don’t benefit, our suppliers, don’t benefit, ultimately, our customers, Walmart’s customers, right, and their experience. So I think that I’ve seen a lot of innovation out there. I just don’t know that we, again, embrace it fast enough and bring it into the fold. And I would love to see more of that in the industry because that’s how we all win. We have not gotten to where we are alone. We have partnered with companies to get here as well. We have developed a lot on our own. But when we look forward 24 months, we look for further partnerships. We look for more thought leadership. And that thought leadership may not come from us. It may come from somebody else.

Lenny: 00:31:53

So I get all that. And I think that in the future, we basically have three categories of players in the insight space. Right? You’re either a data provider, which also means data collection. Right? Your task is to extract information and then provide that information back. Data activation, I think that’s that connectivity to delivering the right message to the right person at the right time, right, that marketing lifestyle component. Or you’re a data sense maker, which is more of the strategy consulting. Right? What the hell do we need to ask and what does it mean? You know, what are the implications? So in that world, where there’s basically three categories of business with lots of sub categories exist underneath that, right, lots of nuance and change, I definitely see you, which your description of being kind of bucket one and two, but not necessarily bucket three, which we would think of as the world of full-service suppliers or strategy consultancies. Are you going to be partnering with them? If I’m correct? First, am I correct and keep thinking about that, that you’re “Yeah, we’re not really going to test the strategy piece of things”—and does that create an opportunity of partnerships with the Ipsos’s of the world or the Materials of the world. Or are you just like play with that at all? Whatever you want to engage with us—“if you’re P&G, you want to work with us and you want to work with Ipsos, great, that’s up to you. But we’re not going to necessarily play in that world ourselves.” What can you tell me about that?

Mark: 00:33:18

I think your definition of three buckets is spot on. You know, where we are today is because you had to start with the foundation. So your data collection, right, and getting your data right. Because if you don’t have quality data, nothing else matters. So we started there. Go to the second one about really building the value around the data and the applications, that’s what we did with Walmart Luminate, where we’re taking now the data itself and putting it into the applications in a way that allows agility and faster, better decisions. And at times, that requires a combination of data from different data sets, which then occur there. Right? So I think that’s where we are today. We are not really playing in that third space. We have dipped our toe in certain areas up there because our clients have asked for help. So in certain areas, such as survey creation, being able to do interpretation, we’ve been asked, can you help there. And we have tipped our toe into there very successfully, actually, because it’s helping, again, elevate the organization. How you take that data, not alone in its own form, but how do you take that data now and build it in with all the other data sets so I can make sense of it? Right? So when we look at the three modules that we have today, right, we’re looking at—and then you add in Digital Landscapes, which is the pre-purchase, we’re able to look at what is going on through our shopper behavior, where is it happening with our channel performance scores, and then why behind the buy. So why is it happening from really engaging our customers into survey, qual or quant work, being able to pull them in. Right? And that Digital Landscapes gives us even further visibility because now it says what is the behavior that happens before I even purchase it. Bringing that all together is where we’re being asked by our clients, suppliers and internal stakeholders, to help us make sense, help us bring this data together, so that we can make that better decision than what we used to do. So I think you’re right. Today we’re not in that third bucket if you look at it. Would we be there in the future? I think it goes back to, if we need to be there to help change management, to help raise the bar, we’ll do it. But it doesn’t mean we also won’t partner with companies to do that third space. But I would also tell you we’re partnering with companies today to do the first two. So, you know, we’re not just looking for partners in the third space. It has to be somebody who really elevates the bar in all three of those areas.

Lenny: 00:35:47

That’s great. Mark, thank you so much. This is a fascinating conversation to begin with. And for somebody who’s really driving that innovation and change, take time and share that with our audience and even with the industry as a whole, that’s hugely important. So thank you for just being open and engaging and helping to have that conversation—that rising tide floats all boats, right?

Mark: 00:36:12

Yeah. You know, so, first of all, Lenny, thank you for having me. And I think one of the things I’ve heard the entire time I’ve been in this industry is insights should have a seat at the table. And I can tell you there’s no better time than now because all businesses depend on data. But it’s not the data; it’s what you make of it. So I think we’re at that golden time where our industry really can shine.

Lenny: 00:36:36

I could not agree more. But like all change, it’s going to be bumpy here and there. Right? Some things, some business models may fall away. Some players may change But the business need and opportunity is still there, no matter what. So that’s great. Mark, where can people find you?

Mark: 00:36:57

You can go to Data Ventures, walmartdataventures.com, and just connect through there. And if it’s worthwhile, it’ll make its way to me.

Lenny: 00:37:05

Okay. Anything else that you want to add before we close out?

Mark: 00:37:08

No, just other than I sincerely appreciate having this opportunity to have this conversation with you, Lenny. Thank you very much.

Lenny: 00:37:14

Thank you, Mark. I really appreciate it. And thanks to you, our audience, which gave Mark and I this excuse to talk. So really appreciate that. Thanks to our producer, Natalie; our editor, Big Bad Audio; to our sponsors. And that’s it for this edition of the Greenbook podcast. We’ll be back again with another real soon. Bye-bye.