Navigating Major Trends in Retail Data and Analytics

Everyone, welcome back to keeping count from the retail and inventory accuracy experts at DataScan. I'm Tyler Kern. And joining me once again is Adrian Thomas, President and CEO at DataScan. Adrian, thanks for being here. Tyler, good to see you again. Thank you for having Well, I'm thrilled to have the opportunity to talk to you today, and today we're diving into the big topic of data and analytics, which is a huge topic across a number of not just retail. We were talking before we came on the air just that, you know, this is something that that people are talking about all over the place, but how can analytics really help improve retailers from an inventory perspective. I think as you said analytics are as a as a topic is growing in the realization that is of its importance. And data within retail has been limited prior to the last sort of two to three years to you know, POS data -- Mhmm. -- maybe delivery data, supply chain data, and obviously some accuracy data that we provide as as alongside others. I think what the from the inventory accuracy standpoint now, what we can start to begin to show retailers is what is actually going on with their inventory in the store. And the challenge in the past is because inventory counts had been performed maybe once or possibly twice a year, the relevance of that data was relevant was very short What we're seeing now is that people are counting much more frequently, and that may be using traditional barcode counts or as we're seeing, you know, further adoption of RFID, and we're getting that trend data. And it's really when you start to build up that trend data that you can see patterns within within the within the business. So what are some challenges that retailers are really facing when it comes to analytics and inventory accuracy? And and and how can we help maybe solve some of those challenges you're seeing? It's a question of what what data points are important for them. Mhmm. And that varies from retailer to retail So from an inventory accuracy standpoint, it can help with shipments. It can help with potentially identifying overstocks within the store or understock within the store or stock outs within the store. It can start to identify whether there is a loss prevention issue or issues within the stores. And it can do it from store to store. So it's not just across a chain. It can be identified right down to a particular store, where there may be specific issues the store management may be lax. So you can start to track behavior all the way down to what are my employees doing within the store. Are my customers doing in the store and how is my inventory moving? So you know, one of the things you brought up there that I thought is is really interesting. Just from a broader data and analytics perspective is that these days, everyone knows they should be collecting data, but not everybody knows exactly what to do with it when they get it. Right? You you brought up just the the idea of what data is important? What data points can you actually take and turn into actionable insights. Right? And I think that that's a challenge for a lot of people is they say we have all this data, but I I don't know what to do with it. And I think that's right. And I think the challenge for a lot of retailers with store data is they're getting multiple layers of data data from from the various systems that they may be using. Whether that's the distribution data down to the store, whether it is the point of sale, so the the retail sales data that they're generating with that system, and they may be using other consumer interactive data to track how many people are coming into store, the behavior when the people are in the store. So there is a a lot of data which retailers could have access to. The piece that we think we can help people with is that inventory accuracy data. That's just another layer. Mhmm. And and so from a retailer to retailer perspective, they need to decide which of these data points and data sets are the most important. And then within that, hone in on the very key data that they want to be changed either a process or a system or an activity within the store that could impact productivity and sales. So how would you walk through that process if a retailer came to you and said, hey, I have a lot of data points when it comes to inventory accuracy. Help me narrow down the ones that that are gonna most important to me. Is that something that you could you could help with in that process? So from our perspective, you know, we have a lot of data based on the counts that our system is used to perform. Mhmm. The the importance of that data grows over time as you build up the trend data. And the good thing is that we can track data down to a SKU level at a particular store And then we can start to track the accuracy of those SKU counts to the book stock that the the retailers will have. So over time, you can track as it ebbs and flows and the accuracy changes and then go back and analyze why is that. Mhmm. Right? Right. And just one example of that might be, well, if we have a supplier that is is shipping to us directly and it just passes through our distribution chain down to the store, the supplier might be the problem. Not our internal supply chain. So we can start to look at whether it's a store performance accuracy issue, whether a department within a store accuracy issue, or whether it might be related to supply chain that could be internal to the warehousing and distribution that the retailer manages or it could be the retailer's suppliers themselves that may be causing an issue. And we've done some very interesting work with one of our retailers that does do that. And we've identified four culprits, if you like -- Mhmm. -- for for suppliers. So manufacturers of product that are highly inaccurate with their deliveries to the store. Interesting. Interesting. So it sounds like you can get extremely granular with a lot of data and really get down to to the nitty gritty to help solve some problems. You can. I mean, I have what interestingly, we're working with one retailer now and this is just something that we've started in this last year actually, you know, during the COVID impact -- Sure. -- to track the accuracy of their receipts into the store. Because they identified that as a major challenge for them, and they wanted to make sure that what what they had delivered matches what they thought was being delivered. And then how did that move on to the shop floor? So there's two components. One is the actual physical receipt, and the accuracy of that. And then there is the physical transfer from the back stock to the floor stock. And does that match what their expectation is. I was talking to a retailer recently and their biggest concern is that their distribution to their stores is handled by a third party logistics distribution company. Mhmm. And the way they track accuracy doesn't drive the right behavior. So they're looking at changing assesses based on the ability to track the accuracy of the receipt from that third party logistics provider. And I think we're seeing the – because the importance of imagery accuracy in the store is now becoming so critical -- Mhmm. -- we're seeing retailers look at each of the different components that could potentially contribute to that inaccuracy that they're experiencing. That's really, really fascinating. And Datascan has a new tool that helps in this regard too. Is that right? Yes. We're about to launch a new product called DARTSmart, and this is really tracking tracking the the data that we have, which is our count data. Mhmm. And we're working a number of with a number of retailers to look at how does that count data trend over time and then working with them to say what does it actually mean? Yeah. I mean that's the challenge with the the volume of data that these retailers have access to and we're just come one component of that is it's a huge volume of data, and you need to be spending time on analyzing which of those data components is important And maybe what do you what do you want to attack first in looking at the reasons and the causes why this data gives you a concern. I wonder, you know, I I almost have this assumption that everybody is on board with data and analytics, but do you still run into skeptics at all? Or people that that think that's that's not for me. We do things the old school way, the old fashioned way. Oh, absolutely. I mean, what is the saying saying lies, lies, and damn statistics something like that. Something like that. Yeah. So yes. I mean, there there there is. And I the the challenge with any data is is before you start making decisions on what you think the data is saying, you need to understand the true impact of what you're looking And we were working with one retailer recently and and they were looking at and if this happened during this sort of explosion of online sales and BOPUS, where sales of a particular, you know, category within a store was was growing and growing and growing and and it was in it was in the fashion space. And they thought this was good. So if you just look at the raw data, well, our revenue is going up, our sales quantities are going up. Right. Things must be good. Actually what they weren't tracking was, well, how many of those sales are actually returned? That's a great point. So if you start to look at the returns and net those values against the revenue growth, the picture wasn't so good. And I think that's just one example of when you look at a particular data point, you need to look at the data around it to make sure that your conclusions are accurate. I think there's a lot of conversation when it comes to data and analytics and also how you can combine that with things like AI and learning and things like that. Do you see those kind of emerging technologies coming into the retail space in a bigger way in the future? Yes. I think and and I think that is gonna be guide buyer behavior. Mhmm. And so once and retailers are doing that today. So they're looking to drive activity within their customer base that that will hopefully drive revenue for them. And then on the backside of that, you know, they need to look at, well, how do I make sure that that buying activity is fulfilled fulfilled correctly on accurately. Mhmm. And whether that's through their online or whether it's through their buy online, pick up in store, strategy, or whether it's the, you know, good old fashioned foot traffic through store that needs to find something physically present in the store? And I suppose that just with the the emergence of more omnichannel approaches to retail, right, that people wanna be able to capture data from an online interaction or an app interaction. Right? And and transfer that into how that customer then interacts in the store environment too. Right? And so there's lots of different angles from which you can come at it from a data and analytics perspective just in in a general retail sense. I I think that's right. And I think that that, you know, there is enormous amount of data on what we as consume as actually do. Mhmm. I mean, I was was astonished the other day that my my wife is a crafter. Uh-huh. And she wanted to buy a particular crafting piece of equipment. And so she did some research on it and didn't buy anything. But I then got this that piece of equipment as an advertisement on my Google homepage. Mhmm. That's how powerful some of this data is now. And that is, you know, just tracking people's, you know, buying, you know, buying activity researching activity. And I think that is gonna lead to people needing to understand, okay, we know there may be activity going on that is gonna drive demand. And we need to be able to fulfill that demand, you know, when it actually materializes. That's a good point. We've seen some consumers not want that level of that level of information about themselves out there in the marketplace. Right? Is there any concern amongst retailers that there will be a large portion of the population that says that's not for me, you know, I don't want that kind of data collected on me. I I think I think there is. Mhmm. And I think that, you know, the the answer to that if I if I'm going to jump into the retailer's shoes is to say, well, those retailers, those consumers who are not going to want to engage through some form of remote activity, whether that's their own store app or whether it's, you know, researching through Google, they will resort to you know, going into a store and physically buying something. And I think retailers still have a huge investment in their brick and mortar fleets. Right. And we've seen some store shrinkage over this last year, which was obviously caused by the change in in buyer behavior as a result of COVID. I think we we will probably see some continued attrition as as retailers look at the profitability of their stores. Mhmm. But I think they do see that people will still go back whether that's a customer who is happy to be tracked or those customers that are not happy to be tracked will still visit the stores, and they're gonna need to maintain inventory in the store to make to act you know, to meet meet that demand Absolutely. Absolutely. Well, Adrianne, as we as we come towards the end of our conversation, just about data and analytics and inventory accuracy see today. Any final thoughts? Any conclusions that you wanna make here before we wrap this episode up? Yeah. And I think, you know, from a from a data scan perspective, we're excited about the opportunity that we the the part that we can play in the inventory store, you know, accuracy process, you know, we were gonna we're going to be launching our dart smart solution coming out probably in the next ninety days. That's going to build over time. And our ability to analyze that data is something that we're excited about to be able to change behavior and change the business process within our customer base. Mhmm. And that's what really it's that added value that we think we can offer by being part of this data story. Is there anywhere where people can go to get more information on DARTSmart at this point? We have a a teaser out on our website Okay. Good. And we're gonna be talking about on our you know, you know, on the keeping count podcast as well. Excellent. So stay tuned for more information on Dartsmarthen. Alright. That's the that's the good message. Adrian, thanks so much for joining me here once again on keeping count and diving into the world of data in Please to be here. Thank you very much, Tyler. Appreciate it. Absolutely, everyone. Thank you for tuning into this episode of Keeping count. We appreciate it very much. Of course, make sure you subscribe or bookmark this page, whatever you need to do to make sure to stay up to date with the latest from DataScan. We're going to be exploring a lot of topics across the world of retail and inventory accuracy. So you're gonna wanna stay tuned for that not in this a single episode. But until we are back with those new episodes for Adrian and Thomas on this time, Tyler, and we'll talk to you again soon.