In this episode, Jeff Buck, CEO, Robling and Graeme McVie, Managing Director of Data Science and Analytics, Logic Information Systems, discuss how to bridge industry silos, the future of data and retail, their take on current technology trends, and much more.
In this episode, Jeff Buck, CEO, Robling and Graeme McVie, Managing Director of Data Science and Analytics, Logic Information Systems, discuss how to bridge industry silos, the future of data and retail, their take on current technology trends, and much more.
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Steve Hamm: [00:00:00] So welcome to the data cloud podcast. We've got two guests today. You've got Jeff. Welcome Jeff.
Jeff Buck: Thank you, Steve. It's great to be here.
Steve Hamm: And we've got grants.
Graeme Mcvie: you, Steve. I'm happy to be on the podcast. Thanks for having us.
Steve Hamm: Yeah. And in case anybody has a doubt about it, grandma's the guy with the Scottish accent. So you'll know that going forward, let's start with you, Jeff. Tell us about roadway.
When and why did you and your colleagues start the company? What was the problem that it was set up to solve and how does it solve it? And also why is the cloud so import.
Jeff Buck: We started in late 2018. And this is the second time that we built a company in this space. So we really have been doing this. Over 20 years. The, the problem that we're after is the global issue of data silos and retail has it bad, and it isn't getting any better because [00:01:00] retail is getting more and more complex at the heart of it is truly getting an overall view of the business and enabling information sharing across the business.
And what happens when you don't do this? Is that your most valuable people? The ones that make decisions with millions of dollars of inventory and labor costs are burdened with unreal mind-numbing and soul crashing and spreadsheet work to integrate data from disparate systems. So it is fun because we're changing lives by giving access to two data resulting in these aha moments or, or insight, for instance, uh, you know, when was the last time you made a quarter of a million dollars with one decision in one week?
That's what our analytics can do that the cloud is so important for two reasons. One, uh, no matter what new advances in [00:02:00] database performance, you'll always hit a wall related to the physical machine. It just has a limit. The cloud has solved the processing bottlenecks because of a near infinite expansion.
Compute and storage. Uh, although the database technology had to be redesigned to use resources in this way, snowflake has done that and uh, and there's only a few others that have even come close to, uh, to the elegance that they've done it with. So storage for data warehousing purposes is cheap and compute is elastic, which fits the model nicely for retail because the usage is so spiky.
And number two is that the next frontier in analytics is integrating external data, uh, with the internal data.
Steve Hamm: Right.
Jeff Buck: And the cloud makes this very, very easy. So every day we are opening eyes to sharing across, uh, business partners. Like the relationship between retailers and CPG and manufacturing, and also [00:03:00] augmenting our understanding of customers through all the information available from third-party data providers and digital properties that are also rich and customers.
Steve Hamm: Well, that's a big job and, uh, it's such a young company. It's great that you're doing this thing now. Um, you talked about silos being the problem in the industry. So if, if silos within companies, but also silos between companies that you're really bridging between.
Jeff Buck: Yes it is. Yeah. And that's what enhances the experience all around, uh, through, uh, you know, better manufacturing of products, more targeted advertising, and then a product design that is influenced by a more complete understanding of what, uh, what the customer.
Steve Hamm: Okay. Great. Great. Now let's move to the ground for a second here. Now, grab your logic. It's a global retail technology consulting firm and it partners with Robling, snowflake, [00:04:00] and others. Tell us about your company and the nature of your partnerships in particular with
Graeme Mcvie: Yeah. She'll thanks, Steve. So thanks for having his own. So logic has been around for about twenty-five years and we spent the entirety of that time working with retailers to help them with our technology needs. Um, some of our clients would fail to assist the largest retail focused technology consulting firm, uh, that they've worked with.
And all of that time we've grown to work with about 150 different retailers across every retail vet school from the food drug mass. True all goods across the self goods, the pile, uh, home promising, um, and not so retailing. And we've walked in every geographic region across the planet. And this has largely been driven by the needs of, uh, the retailer, uh, back in the.
Uh, retail's laws. We just said technology to help them with backend, um, systems, you know, financial processing and, you know, patches, all those and those [00:05:00] things. But, um, along came the barcodes and the mid seventies, and all of a sudden there was this explosion of data that retailers had access to and they knew exactly what items sold, sold credit sold, and what the price was, and that impacted all the different areas of the business.
It's supply chain, but the inventory levels and the stock levels. So it's all the merchandising and things about how you price and how you promote the mark down to what items you carry and every single store, and then not move farther forward into the process to marketing. And I was seeing it manifest itself through store operations and the e-commerce side of things, and even into customer interactions with digital capabilities and a lot of things.
Um, I've had to grow the it and technology capabilities to try and keep up with that so they can compete successfully in the marketplace and find customer needs. Somebody tells I've worked with, uh, several thousand members of the it team and others don't have [00:06:00] quite the same results, but they all need help with understanding what is the right technology strategy?
What are the best technology decisions they should be making? And then how are they success? Implement those solutions so that they can enable the decision makers to make the best possible decision for the shoppers. And that's where that logic comes in. We help all of our clients across all the different elements of the technology stack.
We help them make strategic decisions. We help them with implementing. We help them with support thing and we try and keep them at the forefront of the technology revolution so they can continue to satisfy the customer needs better than the current.
Steve Hamm: Yeah. Yeah. Now you have a lot of partnership relationships with those that you talked about. All the different, uh, tech vendors in particular are the ones with Roebling and snowflake and he different than the others. How would you characterize it?
Graeme Mcvie: We've seen a significant increase and retail's needs around data and analytics. Uh, as Jeff talked about and there all day, there was a [00:07:00] lot of on-premise, uh, behind the firewall type solutions, but those were limited by what was on the box. We're starting to see that retailers are needing access to greater and greater amounts of.
And as Jeff talked about, it was oftentimes data was in different silos and refilled need to bring that data together. You can no longer have your sales data in one system, your customer data in another system, inventory data, and another system needs to bring all of those components together and you need to do it in a very timely fashion, a very granular level Roebling and snowflake enable all of that for our clients.
Snowflakes. System with an elastic storage and compute, you have talked about, enable us to bring together all those different elements so that the retailer can gain that full 360 degree picture of their business, of the customers, of the competitive marketplace, and then their opening solution puts all of that in a format and a consistent way that everybody is talking from one source of the truth.
Everybody knows what. At each metric means ever these talking with ladies data from the latest date assaults. So [00:08:00] there's no longer any discrepancies between what the messenger team is saying. Basically stood operations versus marketing versus supply chain. They're all on the same page. They're all looking at the same data, the same metrics, the same calculation.
So it makes the whole organization. Far more efficient. So we've seen a lot of interest from retailers that are in the world who need help and understanding how do they deal with these issues and how do they migrate to a cloud environment where they've got that elastic, storage and compute, and they can get consistency of the data now.
Steve Hamm: Yeah, no, that's great. Hey, I want to go back to you Jeff though, and let's, let's go a little deeper on the, on the partnership. I understand that these two companies, Roebling and logic have deep personal connections going back more than a decade. Talk about that personal connection, those personal connections, and talk about how that helps the partners.
Jeff Buck: Yes. So, uh, some of the, the executives, uh, Of, um, logic and Roebling. We actually started our careers in the same [00:09:00] place over 20 years ago. Uh, and that, that grounded us in analytics. And then we have both grown up in the retail space, focusing our analysts. Attention on that industry. So the combination is really important because we are because of our focus in retail and logic's focus in retail.
We can come together by Roebling producing the product. And logic then, then focusing on the customer implementations. And so we, we both have developed a lot of skill and expertise in and in our respective areas. And then when we come together, it's, it's, it's like music for, for our customers.
Steve Hamm: No, that's that's great. I, you know, I know that there are a number of data integration vendors. Uh, like you make connectors that integrate a wide variety of data types from a wide variety of [00:10:00] sources into cloud data warehouses. Um, some of them are kind of broad across industries. Some are particular industry.
So talk to us about how you fit in.
Jeff Buck: So most of the integration companies are the connectors as you call it, make it easier to move data from one place to another. And we, uh, at Roebling leverage those. So, so, you know, we don't, we don't really compete with those companies. We partner with those companies when it's appropriate. The problem is it's it's, it's not, it's not enough just to get the information from those disparate systems into one place.
It's truly a data engineering problem, meaning that you have to stitch this data together in a way that makes it intuitive for users and that it solves the business problems. So Roebling is an industry vertically focused business, which means that we hone our products specifically like personalization to the retail and [00:11:00] CPG industry.
So we don't stop at the first level of integration. We see it all the way to cross-functional insight that solves business problems and creates competitive advantage for our customers
Steve Hamm: Yeah. Yeah,
Jeff Buck: as maybe it maybe be helpful for an example, would that help?
Steve Hamm: Oh yeah, absolutely.
Jeff Buck: There, there was a great example. Recently when retailers had to shutter their stores and inventory was left stranded in those stores, nobody was coming to the stores.
So what they did is they turned that inventory, uh, to the digital channel where everybody was going to shop from their homes and then they'd ship the inventory from the stores. So even, even though they knew that would be more expensive, but at least they would be utilizing that inventory, turning the inventory and serving the customer.
Well, no good deed goes unpunished, as they say, many of these transactions were unprofitable. Uh, we analyzed some orders that actually shipped [00:12:00] from like 15 different or 15 or 17 different stores resulting in like a huge loss for that transaction. And the fix is to stop these orders from being placed.
But how do you do that? Uh, and, and what information are you going to use without understanding the magnitude of the problem? They can't make a good decision, uh, that doesn't also hurt customers and the whole business. And it would require data from the ERP system, from the order management system from fulfillment to actually put that together.
So before Roebling, this was. Just impossible. And now decision makers can see the entire order flow from what we call. Where they, where they originated the order online to door, click the door. And there, they were able to pinpoint the level of orders by gross margin of the products and the split shipment status to make adjustments to, to the order management algorithm.
And this is what I was talking about [00:13:00] before this one decision, uh, saved over $240,000 in one weekend.
Steve Hamm: Yeah. Yeah. So the idea is that some of the, some of the order fulfillment could be done from stores. On the fly because of your technology, they were able to decide, oh, this one is profitable. We'll do it that way. Or this one isn't, we'll take that out on, uh, you know, our, our normal inventory or warehouse inventory or something like that is,
Jeff Buck: Right. Well, exactly. Well, retail is complex already. And then when you, when you have distributed inventory that you exposed to the online channel, And then fulfilled from all of those different store locations. It, I mean, it's a, it's a total spaghetti mess. And so it's super, super complex. And so what we help them do is, is kind of sift through that to see what, what kind of decision they need to make about what orders were actually good orders to take in this new format, a new way of doing it.[00:14:00]
Steve Hamm: Right. I got it. I got it. Yeah. You mentioned engineering there for a second. I noticed that you named the company after John a blank. Who's the famous 19th century engineer. He was the project lead on the Brooklyn bridge among other things. So what is the connection? Why did you name the company even though you spell it differently?
Uh, after this, this 19th century engine.
Jeff Buck: That's a great question. Thanks. The, uh, I was actually sitting in a restaurant with a dear friend of mine talking about starting this business and behind him in my view was this amazing, beautiful bridge that was all lit up. And I asked about it. My friend was a history buffer as a history buff. So he. He told me about John Roebling and how he built his bridge.
This is a prototype for the Brooklyn bridge. So the bridge, instead, this is a bridge in Cincinnati. So that, that bridge John Roman blues was a prototype for what eventually became the Brooklyn bridge. So I thought this was a really cool story. Bridges [00:15:00] are cool. The story is fascinating. Uh, bridges are important to our society by bringing people and things together.
I liked the way the word sounds. And then I believe that bridges are also a great metaphor for what we do. We bridge people and data to insight.
Steve Hamm: Right. Oh, that makes total sense. And I love the engineering connection too. Now, you know, there's a lot of talk about data engineering. There's a lot of talk about data science, walk us through that. You're on the data engineering end of this. How do you know, how do you contrast the two and why is data engineering so important?
Jeff Buck: Well, I would, I would also put in, uh, you know, the, all the talk about AI and, and, uh, and. And machine learning into, into the equation for why data engineering is important. So I would liken this to, to driving a Ferrari on a dirty bumpy road. It just doesn't work very [00:16:00] well. You need a paved road or you need the infrastructure for technology to work properly.
Uh, study after study shows that analytics and data science work aren't impeded because of the lack of clean and organized data. And I would add integrated to that equation as well. You can't have your data scientists doing data janitorial work like combining and cleaning datasets in Python.
Steve Hamm: Right.
Jeff Buck: We fixed all that with our data model or data transformation routines and our knowledge of the industry to get it right the first time.
So, you know, how, how important is data engineering? I say we won't realize the benefit of AI machine learning or even analytics until we solve the dangerous data engineering.
Steve Hamm: Right. Right. So basically you deliver to the data scientist, the data they need in the schemas that are most useful and flexible for them. So they can put their [00:17:00] algorithms again, ask them to do their queries against them. And it's like, you're teeing it up for them. It sounds like.
Jeff Buck: That's right. If you, if you ask them what, the first thing that they did, they normally do in any data science project, it will be to find then gather and then clean and organized data. And so we do all that in our system for them. And so it's ready, sitting there for them to get to the more interesting stuff, which is, uh, creating their, the right algorithm and honing the models for whatever, whatever purpose they're doing.
Steve Hamm: Yeah. Hey, um, let's explore the partnership with snowflake a little bit. How did that emerge and what form has it taken?
Jeff Buck: Well, we chose snowflake really from day one because they had the only show in town in terms of a truly cloud-based elastic data warehouse. Uh, that was when there was really only a few hundred people working at snowflake. Now Snowflake's public, there's thousands of [00:18:00] employees and they're taking over the data warehouse world and transforming it with, with unique capabilities.
Like.
Steve Hamm: Right.
Jeff Buck: And what we at Roebling we're lockstep w uh, with, with snowflake as they evolve, evolve. And, uh, so for instance, snowflake has adopted the Roebling data model as the snowflake standard for retail and CPG, and they have also made a financial investment in roadway. Through the snowflake ventures division.
So as snowflake moves to a more industry related go to market strategy, we will be an important part of the story because of our industry focus and our partnership with them.
Steve Hamm: Yeah. Yeah, no, I believe we used the snowflake data marketplace.
Jeff Buck: Yes.
Steve Hamm: Okay. Talk about that. How do you and your clients use that?
Jeff Buck: So the marketplace is a, is a very interesting innovation for, for snowflake [00:19:00] because it builds off of their unique capabilities of data sharing. And it's in its first versions. It's, it's bringing, uh, data providers together with people who would be able to use that data. And so what we have is a standard data model that back ends that that people can comply with either on the data per data providers.
Or the data consumer side so that that data can join more easily. So basically what we're doing is making the data cloud a whole lot more product.
Steve Hamm: That's great. Hey, I want to get down to the nitty gritty here a bit. And grandma, I'd like to ask you this question. I want to help our listeners understand the practical flow of business and data in the retailing industry. Give us a scenario, say a retailer is making the move to the data cloud after they do.
So how do they use Robling to move and format their data and, and what happens next? What's the payoff [00:20:00] that they get from all this.
Graeme Mcvie: Sure. So I said, Jeff mentioned retailing as a very complex operational business to run with a myriad of decisions required by thousands of employees. But at its simplest level, retail is about people buying stuff. So those people, the shop was, and that stuff. And when retailers are making the decisions, they want to understand as much as he can about those people, the shoppers, and about the products that are being demanded by the marketplace.
So they can work out what products to buy at what quantities to buy them and try to move those products for the supply chain and then high to sell the customers in the stores. So if you've got a lot of different systems that don't talk to each other, that whole end to end process becomes very complex.
So what we found. Retail is I have three key areas that need to address from this perspective, if they need to have a data platform that brings all of that information together. And snowflake is a great solution for that. [00:21:00] Uh, I've worked in the past with a lot of retails where they only had the sales data at stool skew week level, because that was all they could handle in those systems.
Well, over time, that's not because. Adequate. So they needed to get done to the transaction level in the basket. So you can see what items are purchased in which baskets together and what prices paid and all those things. And then you need to attach a customer identifier to that. So you can understand what customers are purchasing, and then you also need to combine your imagery data.
So you need a data platform. That can bring all of that information into one system. And historically we, there just wasn't a solution available to enable that until solutions like snowflake came along. So snowflake is fantastic that bringing all of those components, get them, putting them into one system.
And I'll give you just one practical example as to why that is important. If you can't lay up your sales rate data from your sales information, with your inventory level. You might run into an [00:22:00] unexpected out of stock situation. Whereas if you can isolate line up your sales rate data, I sold this number of items in the store over this last day, with your imagery data and said, I have this number of items available in that store or in that warehouse.
Then you can stop to see in three days time, I'm going to be out of stock. I need to address that issue straight away. So that's why I can pull them to bring together those. Data elements into one data platform and snowflake is the best we've seen. It's solving that problem on top of that though, you need a data model to make everything consistent.
And that's where Robling comes in. Now, before I started working in retail, some of the challenges that all retail data will not at all obvious to me, one that I find, which was surprising, but then made sense to me is around the area of costs. Now you'd think that when a retailer buys an item from a supplier, it has a cost associated with it.
And that would be the end of it. You'd be able to take that cost of goods. You'd be able to work out what your gross margin is. And [00:23:00] that will be simple. The problem is retail is oftentimes multiple cost elements and then Telmo systems. So sometimes it was only until I worked with the hide a list cost.
Then they had a net. Uh, net net cost and a dead net cost. So if you have different people in different parts of the organization, not using the same cost to calculate gross modeling, you have people that are talking past each other. So that's one example where the Roebling standardize all of that. You have all the standardized hierarchies of all the standardized metrics.
There's the time that I timeframes. So everybody's talking about the same thing in the same way. So decisions can get made consistently. Then once you're finished with the data model, You then need to make that information available to the business decision makers. So you need a reporting solution on top of it.
You give the business decision makers, access to the insights and information that they need on a very timely basis. So I think of it that I was putting the data and the insights, uh, [00:24:00] the fingertips of business decision makers in a way that enables them to make the best possible decision on the shortest possible amount of time.
So you need solutions across those three different areas in order to be effective across all your different functional areas and the high paced, changing environment that is retail. But then on top of it, You also need to be able to collaborate and let's just feel that collaboration, the data marketplace comes into play.
And I kind of think about that in two ways. There's kind of an inbound data. The retailers need access to, to take something like geodemographics, you know, it used to be that you would get that sent over to the, I used to go on a DVD or a CD that would be sent over you load that into your internal system.
Now all of that can be available online. You can just connect them to. You could things like Google analytics, when you're doing digital marketing MC clickstream information, you can receive that and incorporate that into your process. And then you have data syndicators like Nielsen and IRI to have market level [00:25:00] information about what's going on in the marketplace.
And you can connect to all of that in the cloud. That's an inbound side on the outbound side, you've got to the ability to communicate effectively with the. Over the years, there's been a number of different Tams at improving the efficiency between the retailer and the supplier. So you don't end up with supply chain shocks because the retailer makes a decision that the supplier didn't expect.
And all of a sudden the demand and the volume sold goes through the roof on a product. And all of a sudden the supplier can't adjust the factory manufacturing process. They can't adjust. The logistics process, and you end up with an out of stock situation and you ended up with unhappy customers and you end up lost sales.
So the collaboration piece is kind of the overarching piece that goes along with the data platform, the data model and the report.
Steve Hamm: Yeah. Yeah. So I want to continue with. And let's go up about 40,000 feet. Let's go up to the high level now to say that the retail industry is [00:26:00] in turmoil. Isn't is an understatement. Obviously there's this massive multi-decade switch, uh, retelling online. There's all the tremendous success of Amazon.
We got, uh, you know, kind of the, the multi-channel retailing, uh, elements that make it even more complex. And then we got. Disruptions caused by. COVID give us the big picture here. This is an industry in transition. Some of the entry, you know, some of the changes are, are hopefully just a couple of years.
Others are most could be decades. I mean, where do you think this is?
Graeme Mcvie: Yeah, you raise a really great point. Yeah. Steve, uh, resale is, uh, has been under a lot pressure for a number of years. That's accelerated a lot in the last couple of years. It used to be that retails, um, to get away have basically changed the, the shopper base. Either the product times talking you come by store and I'll fulfill your needs.
Well, that dynamic is completely flipped on its side. It's no longer. The manufacturer gives me [00:27:00] both to the retail, the retail stock for the customer buys that the shopper is now. And if you don't meet the shopper's needs, then they'll go elsewhere. And they'll likely tell everyone on social media platforms about the perspective, which is a magnifying negative effect.
So retailers have to understand that the shop is no king and they have to adapt to that. And the start of this was back when Walmart started to drive major change with the everyday low price model. I did on top of that with the rise of Amazon, all of a sudden, I don't know, you may know that Steve, uh, Jeff May know that, um, I was actually surprised to alumnus, uh, Amazon's original slogan was they wanted to be us most customer centric company.
So it wasn't about online. It was about being the most customer centric company and because of the business model and the fact that they knew each individual customer. On all the transactions, they could do a really good job of understanding customer needs and then making sure that we're delivering a customer [00:28:00] century offering that increased the competitiveness and the retail marketplace, but all those of magnitude and then non Amazon retailers has to work yet.
How do I compete? Hi, do I catch up with it? So accelerated all of that change retail has had to put, um, an online offering out. They had to become basically savvy what I to do on digital advertising and okay. I have to handle interactions online, monitor social media platforms, and then along comes COVID and you end up with two years.
In the space of a few months and they all of a sudden had to massively embraced online. They overnight went from the stores, been open to the store being closed. I know they had to adapt and see, okay, stools, and I'm going to be fulfillment Santos, and I'm going to be able to have to offer curbside pickup, buy online pickup in store.
And I'm going to have to make sure all my online offering is really up to standard because the interesting thing about online is. The shelf, they're supposed to look at another [00:29:00] retail and say, how's your online presence compared to my own LinkedIn, they look at the best online experience on the marketplace, which in a lot of the cases is Amazon itself.
And they say, well, if I'm getting that from Amazon, I expect that from you as well. So retailers have had. Uh, respond to that. They've adapted, I think tremendously well over the last few years, they've become very agile from an operational perspective and it's forced them to apply again because one thing that retailers are used to as a highly competitive environment.
So they've done a really good job of adapting. And there was, there was one of our clients that we worked with and it was a great example of snowflake, Roebling, and logic, all working together with a major retailer we'd actually just implement. Uh, snowflake and Roebling with this retail right before the pandemic hit, unless we tell it one of the senior supply chain executives said to us, because of the snowflake Robling logic solution that you guys have implemented for me, there, isn't a question that I have that I [00:30:00] can't answer with this solution.
And as a result, this retailers had one of the best performances of any retailer during the pandemic. And they're very well positioned though, to capitalize on. With the accelerated growth as they come out of the pandemic. So it's up to, again, it's up to the stakes and some retailers are doing a great.
Steve Hamm: Yeah. It seems like COVID on top of everything else has really forced retailers to be more flexible and innovative on the fly than ever before. And it seems like. The technology has been, has played a vital role in enabling that, Hey, so Jeff, why don't you talk about how important data management and data analytics are to the industry, especially now, and what are some of the new technology trends that you see like coming now and even in, you know, maybe the next few years.
Jeff Buck: Sure. Uh, so as the, as the environment for retail gets more complex, as, as I had mentioned [00:31:00] before and had Graham had described very elegantly just now. Uh, the, the need for analytics and data management as a result of that becomes greater and greater because there's no really one system that's going to, uh, handle all of this complexity.
There is. There are all these different ways that, uh, that retailers have adapted to the changing world. And one of those ways is to implement new systems for, for that. So when they have questions about what to do next, with all of the change that happens, they need something that, that answers those, those questions.
And that's essentially what we do in partnership with logic and snowflake. And so where's it going? You know, it's going the, the w the way the, what Graham had described with the, with the strength of Amazon's approach and the focus [00:32:00] on customer is what every retailer is now trying to do just as he said.
And what's also been so interesting, especially in, in COVID is the increase in an advertising digitally. So because of the information that's available, And you were able to target your advertising much, much more finely than ever before. And so, uh, that could be time of day, day of week, but then also who and what you deliver to them in a personalized experience becomes also possible.
So. I think that maybe the target advertising is a little bit creepy and it's good to change with, uh, with all of the regulations around cookies and that kind of thing. However, I think it adds value to our experience when retailers know us better. And so retailers are now adding in that. [00:33:00] That component to the way that they merchandise the two, the way that they stock their stores to the way that they talk to you.
And I think that that's actually getting us closer to where we should be, um, which is a very personal experience with, um, with our, with our.
Steve Hamm: That is really interesting because, you know, in a way the cookies are kind of a crutch, you know, and they're, and also a blunt instrument. If I could mix metaphors, because if you follow, if you're just basically using cookies to figure out what a person is doing, it, it's a bit of a mystery. Why not engage with them directly and gather information around them from your.
Engagements. And I think it seems like that takes a more sophisticated level of technology and analytics, but at the end, it's going to be more satisfying for the customer and probably more profitable for the retailer, I would guess.
Jeff Buck: I totally agree and gone are the days like Graham said, we're, [00:34:00] we're a retailer can just purchase a bunch of. Creator manufacturer a bunch of product, put it in stores and have it sell. That's not the way we do it anymore. We've got, we've got to think with a customer first approach and then hone it based on what the customer is telling us.
And so sophisticated, more sophisticated. Yes. As you said, every retailer, every CPG company needs, it's also its own customer database. So that you can use that database to know better who your customer is. Uh, we've got some great examples from, uh, our customers. Uh, for instance, one of our customers, uh, uh, was stocking vinyl, but in some stores, it wasn't selling that well.
And they said, well, why don't we just get rid of it? I don't even think that's serving our core customer. So, because we're records, right. That's probably, uh, like guys like me, wasn't that wasn't the core cause, or it
Steve Hamm: Older than 30, right? Yeah.
Jeff Buck: When they, when they [00:35:00] analyzed, they analyze the transactions, uh, as well as, uh, the customer information, they realized that no, as a matter of fact, it's, uh, there's a lot of 13 to 15 year old girls that are buying vinyl.
And so that is my core customer. So it's not getting rid of that. That w that would be a, that would be a big mistake. So this is the kind of thing that we need to pay more and more attention to.
Steve Hamm: Yeah. Yeah, no, it's really interesting. We thought we'd go ahead, man. Yes.
Graeme Mcvie: Oh, I was just going to another example that's to build on Jeff's example. So when retailers make, uh, assortment decisions, oftentimes they'll do what they call stacking rank and they'll rank color items based on sales. And they'll cut off. So the ones that are in the bottom that don't sell a lot. What we oftentimes find though, is the one thing the bottom of don't tell are oftentimes very important to your most loyal customers and does not another item in the assortment that they would substitute in for that option.
We oftentimes [00:36:00] find items somewhere in the. Of the stack that I actually do have other items that will be substitutable for a customer. So you're oftentimes better keeping those ones on the bottom that are more valuable to your most loyal customers and substitutes and making some changes in the middle of the section where they've got high substitutability and Omnicell important to your most loyal customers, because to what you injectable saying, you know, I, I have a belief that the, the retail.
The best satisfy customer needs will win in the market. You know, if you'll do a really good job of satisfying your customer needs, why would they go somewhere else? But those two fundamental components to underpin that the first is you actually have to understand what your customer needs are. So you need to customer data to be able to do that analysis.
And then secondly, you actually have failed to take actions so you can consistently satisfy those customer needs. So if you don't have the data platform, the most. Um, upon what you can build the analytics, you won't get to [00:37:00] that level of understanding. And unless you can actually get to actionable and executable insights to satisfy customer needs, you won't be able to win in the marketplace with shoppers.
So you can continue to end the loyalty and grow your business in a profitable way. So in some regards, there's a personal example I had recently, which I don't know if any of you will be able to relate to that. My kids. Uh, the holidays, when they were going back to school, they actually had to have a, a COVID test, um, you know, before they went back to school.
So I went online to one of the big drug chains to see who had the online at home test kits online. And I found my local store that had it. And I was like, great. And there was some other stores that were stopped, but that's to go home. So I drove up to the store and when I go up to the store, there was a handwritten note, had been stuck to the door saying we are.
Of at-home test kits. And I was like, well, that's frustrating. And the problem with that is the retailer didn't have the systems in place to update the data in real time and [00:38:00] then connect it to the inventory that then connected to the website. So they would tell me that it was a stock. So that's why you need these data platforms.
You need them to be real time and need them, all the systems be connected. So you can get that experience to be what your customers want to.
Steve Hamm: Yeah. Yeah. That's really interesting. You know, one of the themes that keeps coming back in our conversation today is just how complex the situation is. And I think what makes it even more complex is because you've got two industries, retail and consumer packaged goods that are really wedded at the hip.
You know, their supply chains, that distribution change make up this vast global network. And you know that the, but one industry owns one and the other industry owns the other. So how, and of course right now, They're experienced these, these incredible disruptions because of COVID, you know, we read about all these, all these ships backed up, but, you know, and, and, and, um, and the Harbor is out west or, or, or in New Jersey, [00:39:00] things like that, causing incredible disruptions and outages and stuff like that.
So how did these two industries, how do they kind of. Connect much better and, and kind of create a, a global nervous system where they all share information. What's, what's, you know, what's going on right now. And I'm really asking you, I guess, uh, grand, but this, this point, because Jeff answered this partly with his, with his answer, but how do you see technology helping solve that problem?
The problem, and also the opportunity of the vast global.
Graeme Mcvie: Yeah, it's a fascinating topic they had a system called retail link where they'd actually released the sales data to the suppliers. And the whole intent of that collaboration was to drive costs out of the supply chain and they were, they were pretty successful.
And in terms of what's called replenishment inventory, which is just your ongoing natural inventory. [00:40:00] We had sort of struggled with on the emotional side where you get these massive spikes and the mind when a promotion was running the store, and then it would have this massive ripple effect all the way through the supply chain.
You then had, um, companies like Dunnhumby, uh, working with Tesco in the UK, in Kruger, in the U S where they started to provide. More information available. And it used to be a, uh, a portal they had where you submit your request and then you would wait a few hours while it ran in the background, and then you can get it by and an Excel spreadsheet.
And that was the form of collaboration in the past that you couldn't really do a lot with that at the time. And just sort of understand small snippets of data with the advent of the cloud. This whole process becomes much more. And you can reap the supply chain benefits. If you have visibility into not just your replenishment situation, but also your promotional situation, where you share the promotional plans and the promotional forecast back and forth.
But then you can also go further and you can collaborate around what items should be in which tools and [00:41:00] what the consumer trends that are going to drive new product innovation. So you can collaborate on product innovation. And then the latest trend that taken off is around the market. Side of things.
So, uh, their initial stats on the marketing side, where the suppliers were given access to customer personalized customer marketing campaigns, that the retail would execute with their customers and the suppliers would participate and they would line up the respective marketing budgets, and then they would have a good sense for what that would do to the mind.
So they could line up the supply chains with the factory production schedules and know that solvable. Beyond that. And you've got the development of these things called retail or media platforms. I know the latest version as a retailer marketing platform, because you've got retailers with these online properties or e-comm site to have massive audiences going to them on a very frequent basis with the exact customers that their suppliers are interested in, as well as the retailers.
Right? So instead of the suppliers [00:42:00] spending, you know, an ordinance amounts of money on un-targeted. Advertising or media advertising that might not reach the right customers. And as long with it, now they can be laser focused on the customers that they're going after. And those needs because they can partner with the retail.
So you've got this collaboration or marketing going on, where you're lining up the different marketing budgets, and you can be more suggest point. You can be more personalized and targeted in the communications that you send.
Steve Hamm: Yeah, that's a, that's a way to get. Thank you. Thank you for that. Now we're coming to the end of our podcast. And at the end, we like to have a lighter note to get a little personal. So I have a kind of a topic for each of you, and we'll just start with you, Jeff. I see that you have some racing car experience.
Would you mind telling us about that and tell us how you use data and.
Jeff Buck: Yes. Well, I had the distinct pleasure of being a pit crew member on formula Atlantic back in the late [00:43:00] nineties. But it was not very glamorous. I must tell you I was in college at the time. So the guys that ran the pig crew made me basically wash the tires.
Steve Hamm: Oh, you were, you were at the data scientist now,
Jeff Buck: No, I was not, I was definitely the grunt. Uh, so, uh, I also started a team that built a formulas style car, uh, from scratch, which is a, which was a really cool project. Um, So about data back then, we honestly didn't have that much, uh, during the race day, uh, that I saw at all, we had a lot of tech that was dedicated to engine tuning, so fuel curves and stuff like that on the, on the dynamic dynamometer.
Um, but today it's different because we have a telemetry tag and sensors on everything like braking and turning GS and all of that. So w what that does. Is it helps the car and the driver in what I think is exactly the same way that [00:44:00] the data helps retailers and CPG. You get feedback on performance, where you made mistakes, where you have opportunities and it points you to the future actions and how you optimize a complex machine team.
Steve Hamm: right.
No, it's interesting that the whole metaphor tuning, I think, is something, you know, there's those run through our conversation today. The, the, the technology really enables people to know things much more and to, and to fine tune their offerings and their understanding of a particular customer. So that's really cool.
So grim, I know you played soccer at a high level in the university and it's been a few decades, so I'm sure things have changed a lot. And when we talked to you earlier, you talked about how, how data and analytics has just transformed, uh, soccer professionals, talker. Tell us a little bit about that[00:45:00]
Graeme Mcvie: Yeah, sure. I mean, soccer is like most team sports you need. We've got to come together with a common practice and to all be working together and pulling in the same direction. When I was playing, you know, there was, there was a lot of the element of, uh, of individual responsibility that came with that with the manager standing beside and cajoling to put it nicely, uh, all the players to.
There has been a massive evolution and that I I'm sure, you know, you and a lot, the audience, all the hell of the novel and the movie Moneyball, but you started out with the Oakland A's and all the analytics that they applied and the people that the Oakland A's then went to the Boston red Sox and the family sports group is on a Boston red Sox.
And they actually also happened to all in my favorite soccer. And the UK Liverpool football club, and they brought those analytics to soccer and the analytics are used in a lot of different ways. So just as Jeff was saying, the risk cards of sensors, all of them know the top professional players. We, these center vests.[00:46:00]
During practice and during games and those that track exactly where they are on the field, they track how much they run. They track how fast they run, how many power moves there's other, you know, vital signs that they also track. So that allows not only playing. What was on during the game at halftime, the courts can turn it into play and say, Hey, I'll shape.
Isn't quite right. There's too big of space between you and you. So they're getting true. They are so they can use it in real time. They can also track it from a full science perspective and identify when players are potentially reaching the point where they may be about to have an injury because of overstressing their muscles.
And maybe they have to take them off, or maybe they say, Hey, the next couple of games, you need to say, if I'm pushing it too hard. And then just like the Oakland A's example with money. Uh, all of this data is now you use, when people are trying to acquire new players and take, do they have the same attributes that we need to fit into the way we play on the other players that we have on the team?
So I actually am thrilled at the fact that my two passions are colliding here, got my soccer and my analytics [00:47:00] coming together. So it's usually exciting to see that and that walk of life. But I think every walk of. As going to see the benefits of analytics coming forward and the data profusion that we see out there from all the different sources are going to feel that.
So we just need to make it available in a way that people can take it back, go it to maximize the chances of success.
Steve Hamm: That's interesting. It's like, you're fine tuning your life, your professional life, but also you're fine tuning your life as just living it too. So that's, that's something that I think that hopefully we'll see some benefits for the consumer in the future. You know what? I looked back at our conversation today. We've we've really had some interesting themes.
We've talked about complexity and we've talked a lot about partnerships, uh, between companies like snowflake and Roebling and logic, and between players and industries, the retail industry, CPG industry. And I think. It's just a reminder of how important partnerships, those [00:48:00] connections and the collaboration between companies has to become kind of in modern business or certainly in retail and CBG, but actually across all businesses.
So I feel like you guys have given us some really good insights, not just for people who are interested in retailing, but really for people in any industry. So this has been really cool. Thank you so much for your time.
Jeff Buck: Thank you.
Graeme Mcvie: Thank you.