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ReThink Productivity Podcast
Beyond the Yellow Label: Transforming Retail Operations with Smart Technology
Tom Coe, VP of Growth at Retail Insight, shares how data-driven solutions can tackle the perennial challenges of retail operations while empowering store colleagues to deliver better customer service
• Retail Insight provides in-store operations analytics software for over 50,000 stores globally
• The complexity of modern retail operations creates compliance challenges as colleagues juggle multiple responsibilities
• Process execution suffers when labour models are tight and colleagues are overwhelmed with competing priorities
• Date checking for fresh products exemplifies a mundane but critical process where poor execution leads to waste and financial loss
• Smart systems can track inventory expiration dates and direct colleagues to check only specific items requiring attention
• Data collected provides valuable analytics for head office teams to improve forecasting, ranging, and supply chain decisions
• Successful AI implementation in retail requires balancing technological sophistication with practical simplicity
• Data quality is essential for any effective retail technology solution
• Future applications include democratizing analytics through conversational AI and enhancing root cause analysis
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Welcome to the Productivity Podcast. Today, I'm delighted to be joined by Tom Coe, vp of Growth at Retail Insights. Hi, tom.
Speaker 2:Hey Simon, how are you doing?
Speaker 1:Yeah, good, thanks you.
Speaker 2:Yeah, doing good despite the rain outside.
Speaker 1:Yeah, well you can tell we're recording in British summertime because the kids are broken up and guess what? It's started to rain, exactly. We'll give you something to listen to for all those listening on journeys to and from the office or while the kids are in the playground and stuff. We'll give you some interesting insight in the conversation with tom. So, tom, tell us a bit about yourself first, before we talk about retail insights. How did you get to be vp of growth at retail insights and what have you done before?
Speaker 2:yeah, so as you say, vp of Growth at Retail Insight, I spent most of my career at the sort of intersection of commercial strategy, operations and technology. I'm really focused on helping retailers and suppliers unlock performance through data, ai, beta execution, those sorts of things. I've been at Retail retail inside about five years now. Before that, I was in America, used to be a runner, did my MBA out there when I was a younger man, I worked at Sainsbury's for a number of years. You could say retail was something I've been around a long, long time.
Speaker 2:My job now is really about delivering as much value as possible for our customers, both within what we do for them already and also how we could do more I suppose newer things with them. I really want to be able to ask the question how can we position Retail Insight with all of our senior and retail operations stakeholders? How can we position Retail retail insight as the guys that you call when you've got a problem you can't solve internally, so you need to solve it through data analytics? Well, I'm going to give calm and retail insight a call and see if they can support me that.
Speaker 1:That's really what it's focused on right now perfect, so worked in different countries, which I always think is fascinating and brings some different perspectives on life, doesn't it? So just talk to me a bit about running. So was that a uni thing? Was that a Forrest Gump type thing? How did that work?
Speaker 2:It was a university thing, university thing. I was a keen athlete most of my life. I wouldn't say anymore I'm not much of an athlete, but running was what I did at university. I went to university of Birmingham sort of a big heritage as a great athletics and cross-country school and managed to go out to America for a couple years did a master's and I was. I was a steady distance right like a middle distance runner. But yeah, in a former life now a former life, it was like a long time ago.
Speaker 1:Excellent, there you go, there's your, there's your fact about Tom. If you meet him anywhere, you can talk to him about running. So on onto the onto the important stuff then. So you've touched kind of briefly on you want retail insights to be the kind of go-to for all those organizations that that want support around whatever it might be in terms of data insights process. But give us a bit more flavour about what you guys do, maybe some of the tech you've got, before we get into the detail of what we're going to talk about today.
Speaker 2:Yeah for sure. So Retail Insight I'd position us as the world's leading in-store operations analytics software provider for retail. In other words, we're about applying really smart, pragmatic technology to help tackle the perennial issues of good shopkeeping. You know, questions like do I have product on the shelf? Is my data accurate? How do I stop product going into the bin these sorts of questions that retailers from a single corner shop on the road that we all live in to the Walmarts of the world Everybody asks them and everybody grapples with them.
Speaker 2:To achieve this, we have an arsenal of analytics products and these are a mixture of really, really intelligent advanced mathematics and some of the leading edge technology AI, ml, real-time streaming and infrastructure and these cover things like prompt availability, waste and markdown, phantom imagery, correction and detection, online fulfillment, maximization and a few other pieces around that, and really they are very complex products in terms of the output and what they actually do, but they're remarkably simple in terms of setup.
Speaker 2:They need the most basic data points out of the EPOS system. A retailer has four to five data points and you can set it up and you can have an output within 10 to 14 days, which is pretty unprecedented in the in the market. We do this with over 50 000 stores globally right now. This includes people like the carp in the uk sprouts market in in north america. Soon that'll be at sort of 60k plus and, yeah, real heritage. It's been something we've done for a very, very long time and I think we are really seen as experts in the field and just continue to double down on that grow, continue to focus on what makes us great and expand out what we can do for for our customers brilliant, so that, I mean, some of those initial questions are really big ones that you know, people have been struggling with for a number of years, haven't they?
Speaker 1:but I suppose, more so in the current cross, cross, cross cost climate and we talk a lot on this podcast about, you know, the dni increases and national living wage and stuff, and they're all real and they're all. They're all meant with the right intent, but they're all a cost to any organization, specifically where you've got a big number of people which tends to be in the customer facing environment. So it's a true challenge, for sure.
Speaker 1:So, one of the things we were going to focus on today and yes, we will talk about ai as we we get to the end of the podcast, as it feels like we're obliged to do, but we were going to kind of focus in on compliance and, like you said, you worked in Sainsbury's when you were younger and I've worked in retail all my life. It is the holy grail in retail, always has been, probably always will be, is the reality, and for those that work in retail, I'll get that instantly. For those that may be listening that don't. Why is that? Well, you've got lots of that.
Speaker 1:Well, you've got lots of people, you've got lots of physical locations. You've then got got what I'd call and I don't mean this disrespectfully a random factor of customers, because some will interact with you, some won't, some will be nice, some won't. But you've also got people making decisions. So a store manager, unless you can really help them with a process, will make a different decision in store A than store B, than store C, so on, so on. So when you're speaking with customers and your team, what do you typically see in that space around? Lack of compliance or what drives poor compliance?
Speaker 2:Yeah, you're absolutely right. It's a huge challenge, something that, if I think back to when I worked in a store many moons ago, you could almost see the compliance challenge as you worked on the shop floor alongside your colleagues, people that maybe weren't engaged as much on the job as the retailer probably wants them to. And that's a massive, massive challenge and tackling it is not easy, I think, particularly in an era where, as you say, labour models are incredibly tight. You know razor, razor thin in terms of tightness at the moment. So ensuring that the resource you have deployed is executing the process, the tasks, the ways of working that you need them to is has never been more important, because the money you're spending on this labor you need them to do the job you need them to do. Otherwise that's just a sunk cost. I think there's a couple reasons for for poor that we see. No doubt there's many more, but these are sort of the big ones that come to mind.
Speaker 2:I think there is a challenge around process in-store and the job these associates and colleagues need to do is really complex. It's overcomplicated. Often when I worked in-store it was a bit simpler. You were an associate or a colleague who worked in dry grocery. So you knew the 10, 12 hours you worked on the products, the process, the ways of working, the picking and binning out the back, how you replenished, how you took the products back out, how you dealt with the customers, et cetera. With less labor on the floor, colleagues now need to do way more. You might need to replenish products, you might need to accept deliveries, you might need to shop online orders, you might need to jump onto the self-checkouts, you might need to go and collect trolleys and amongst all that, there's new process around self-scanning a store and things like this, which is making it very noisy around process and colleagues, ultimately, are trying to figure out well, what am I supposed to focus on, what are my priorities? And it's not a surprise that things get lost because there's just so much to focus on and it.
Speaker 1:I found that interesting because we've been on a journey again as far back as I can remember of simplification, simple stores, one best way. I'm just quoting all the kind of strapline across the years from various different organizations. Yet yet you're right, because when you reflect actually multi-skilling, a get. Because why do you want just people set on checkout, somebody you can just work, sat on checkout somebody you can just work in delhi, somebody you can just work in ambient whatever, because it limits your flexibility with a smaller pool of people.
Speaker 1:But then we've kind of enabled some of these people with tech that that might be a challenge. They're not used to tech, don't work with tech outside of work. Then we've given them most instances delivery, uber, eats on demand, click and collect, picking goods, returning goods, layer on top, then shrink theft. You'll have everyone listening, will have seen it on various social media platforms and you'll see lots of colleagues wearing the cameras now. So the environment you work in is tricky as well. It's interesting that we've probably ended up in some respects in a slightly more complex world 10-15 years after everybody's been on a simplification journey yeah, yeah, you're actually right, I think, a more complex world with less labor.
Speaker 2:To deliver against that complexity, I think you could have had some amount of segmentation or separation of responsibilities. When this person's going to be solely responsible for, you know, merchandising, planning and online ordering, this person's be solely responsible for fresh and produce, but now it's the world's a bit um a bit more challenging. The store people are doing wearing multiple hats and doing multiple, multiple things, which is which is very difficult, very difficult and are there any specific examples you can share with us without kind of naming organizations?
Speaker 2:I think a great example is sort of linking maybe the mundanity of some process into the complexity.
Speaker 2:If you think about date checking for fresh products, it's a classic example.
Speaker 2:If you work in a big sort of superstore hypermarket, you're going to have five, six, seven aisles full of fresh products and your responsibility is to individually check every product to make sure that a it's not gone out of date yet and b it's got a markdown, and making sure you then apply that markdown and then later in the day you probably need to check it again to make sure the products that have a markdown have either sold or they get a second markdown or maybe even a third markdown. Amongst all that, you've also got to rotate products. I know as customers we often go in and buy the the longest, longest sort of date item anyway, but colleagues bring forward the earliest date to try and encourage velocity on those first. So I think that's a classic example where it's just a very manual process and it's easy to understand, as I explained it there, manual date checking. It's not a surprise that colleagues are disengaged and compliance is poor and you see instances where retailers get fined and trading standards are involved, all these sorts of things.
Speaker 1:And ultimately I know we talked about cost quite a bit. That poor, mundane process if you're not kind of doing it properly and we'll talk in a second about how you can fix it costs you money because ultimately you'll end up getting fined, you'll end up dissatisfying customers and throwing the product and writing the cost off. So you know, if we just work that through, somebody's made the product, that company's bought it. They've then shipped it to the warehouse, the warehouse have shipped it to the store, the store have put it on the shelf, probably touched it a couple of times, and then we throw it away. So it's not just it cost me I'll make it up four pound 99 trifle, it's the layers of salary that go before that to physically get it on the shelf that you paid for. Yeah, you're absolutely right, you're absolutely right. So so, fixing it then, so that we'll all have seen that, we'll all have seen yellow labels, red labels, whatever organization you're working in, this, this kind of supermarket example, how can you guys help fix it?
Speaker 2:I think, a level. There's an amount of diagnostic and process mapping. Where are colleagues spending a lot of their time doing things that's really high frequency and could be automated or improved or augmented with technology? We talk about the idea of technology augmenting process in store. How can you create almost a bionic or superhuman associate that's got all the data, the insight, the technology at their hands to perform process to the maximum so ultimately they're freed up to serve and sell? Because that remains the big differentiator for how retailers can win on on manual date checking.
Speaker 2:The classic example would be can you just build a ledger which is essentially inventory holding of what products you have when those items expire, and then you work that into your markdown process, which you probably already have? You clearly already have to say that you know today the first markdown, I need to scan five individual skews of vanilla cheesecake because I can see they're expiring today, on the 31st, and then tomorrow I need to scan these items and then you check in new items as they come and it's a self-updating ledger system that colleagues are responsible for. So they're buying into the process. They know that good process execution is going to make their job maybe this afternoon or tomorrow afternoon easier. So there's an amount of sort of buy-in because they know that they're going to make their lives a bit easier there. I think it's just about quite simple technology that removes some of that mundanity, makes it a bit quicker, a bit easier and ultimately gets colleagues a bit more engaged in that process as well then that must give a benefit.
Speaker 1:So they kind of using that through your software. That must give a benefit. A central level, ie the head office, there must be some stats or management information that they can glean from that yeah, a huge amount of insights and analytics off the back.
Speaker 2:You can see what individuals, what stores, what times of day these processes are happening. So you know you typically will have a markdown period happen between hours X and hours Y. You can realize when those processes are happening out of hours. If they happen too late you've got less time to sell products. If they happen too early, you might be cannibalizing full price sales that you might have otherwise got and then, beyond that, bringing in more ways to see what colleagues are facing in store. Are they dealing with too many markdowns? Do I need to edit my forecasting replenishment plans? Do I need to figure out better ways to improve that replenishment process to support less waste on the shelf so there's less going into the markdown process at the end of it. So a huge, huge amount. That's just a lot of scratching the surface.
Speaker 1:A huge amount of opportunity to explore from a, you know, insights and analytics perspective on that side yeah, I mean in simple terms, you can help reduce the volume that gets thrown in the bin, so that's a full on cost and make sure that the markdowns are intelligent, priced correctly, based on the you know vanilla cheesecake example so they actually sell through. Then, stepping a stage back from that, stop, stop people checking every item every day. So we're just targeting the ones that we know that need reducing. So there's a huge labor free up there. But then, yeah, back to your final point. The analytics then can start to be interrogated to say, well, why are we always reducing the vanilla cheesecake? Do we need to ship it in threes rather than sixes? Do we need to sell it at all because we reduce more than we ever sell at full price? There's different bits of information for kind of the ops team, the buying team, the ranging team, et cetera, that they can start to work with. Is that right?
Speaker 2:Yeah, absolutely. It's that root causing, you know, diagnosing what issues consistently happen on the shop floor. And then how can I make broader, enterprise-wide decisions to remove those challenges in the future? Do I need to change the range? Do I need to alter the forecast and demand plan? Do I need to change the promotional plan? Do I need to work with suppliers to alter the case pack that comes to store? Those are just a few of the examples that could be taken at head office level to help improve things for the colleague on the shop floor and, ultimately, the customer.
Speaker 1:Good, so exactly let's, let's talk about ai. It feels like we have to talk about it on every, every podcast. So what kind of are you seeing in the world of, specifically in retail ai? So any good examples you can share, any poor examples.
Speaker 2:Yeah, yeah, lots, lots in both camps, I think AI and retail operations. I wrote a piece about this a couple of months ago. It's really challenging because there's so many issues on the shop floor. You have to compete with the chaos of the shop floor, the lack of the shop floor, the lack of data quality. Getting around those are massive, massive problems. I'm yet to see fully-scaled AI that is autonomous and just deals with those perfectly and has no issues. It doesn't really exist.
Speaker 2:I think the best examples, the most successful implementations of technology that has, ai balances the sophistication of those technologies and the simplicity. So you can have the most complex, advanced machine learning model in the world, but can you deliver it to a colleague in the store in a simple, digestible way that, as we're just talking about, a they engage with, b makes them buy into the process and c directs them to do a task that they can achieve, and then maybe d? Can you see the impact of the task that they've just done on the shop floor? So an example might be our intelligent inventory alerting system, inventory insight, which is basically about flagging potential instances of phantom or shadow inventory, essentially where there's a mismatch between pi and what you actually have in the store. This is a really advanced system. It's got, I think, 20 plus machine learning models that balance and make predictions about your imagery position across the entire shop. But it brings in really quite elegant and pragmatic analytics layer and decisions system which produces insight in a way that is quite human and I think that's really important, instead of just issuing a generic stock command around. Go and check this stock, simon. It's going to give the context. This item has not sold for five days. It's got 20 units on hand. It sold 15 units yesterday.
Speaker 2:We think there's an issue really simple, digestible colleagues in store probably understand and appreciate why they've been alerted to that item to check it. Because I think that's a big issue that colleagues will get alerts from systems in store or technologies and eventually so many bad calls. You're just going to think that every call you get from this system in the future is going to be a bad call. You completely lose the store teams. So I think that's a good example. A bad example.
Speaker 2:I think a lot of people retailers and some vendors will deploy dynamic markdown systems. We've got a dynamic markdown capability at retail insight, so this is just about capturing as much margin as possible whilst minimizing waste through exploration-based discounting. Yeah, a lot of the technologies and systems, and you can go and see some of this and experience it in some stores, but they'll deploy machine learning models that consider loads of factors, for example, the volume of full price items in store. How is that going to cannibalize discounted items? But they really fail to appreciate, I think, the full value chain in retail ops.
Speaker 2:So these systems might assume that the on-hand system, the on-hand count for these items, is correct and it will make discounts based on the fact that I've got 20 items of that cheesecake that we talked about earlier, but really 12 of those items are phantom and your system's not correct and it's making decisions based on inaccurate, flawed data.
Speaker 2:And we can all know what the outcome is going to be discount's going to be all over the place and the chances are you're either not going to sell it and throw away a great product, or you're going to sell it at a massive discount and potentially lose a lot of money. So I think it's how systems that are really intelligent rely on data that is flawed, is inaccurate. I think that's the big thing that we started to talk a lot about in the last sort of 12 months is there is a connected tissue between our suite of products. How can availabilities lost, sales drive figuring out what items that have phantom imagery, and then how can phantom imagery level set on hands in store to drive the right discounting levels within our markdown system. And there's sort of this ebb and flow between all of them that feed into each other.
Speaker 1:The key and it's, I suppose, something that rethink. We've we've learned and there's this big unknown, I suppose, about ai and it's moving so quickly. The bit that people don't talk about is and I think you've just hit the nail on the head is the quality of data that's needed to drive, drive any algorithm, any system, ai or not? Yes, and the consequence of having that data not to the quality it needs. And again, I think most people listening to this that work in a retailer will have questions about data sources, quality of data, consistency of data. And until people get a grip on that and you know it's gold-starred and we all know where it comes from and the sources are reliable and anomalies are detected and moved out or dealt with we're not going to harness it to its full potential. Because the software is there. It's like anything. It's only as good as what you feed in, right?
Speaker 2:Yeah.
Speaker 1:Yeah, you're absolutely right. Okay, so we'll just close on where you you're. You are retail insights in terms of using ai in products. How you're thinking about it might shape your products and therefore your offerings to customers in the future.
Speaker 2:It's a great question, I think, across a number of guises, there's probably two that really stand out. I think the first is how AI can democratize access to our insights and analytics across organizations. I think, historically, bi analytics has been served and is customized in a way that works for the retailers, but AI unlocks that and creates almost self-serve for us, and I think creating giving our customers the chance to speak to our system essentially and have a semantic layer in place which makes that conversation possible, and an AI system that can produce a really great output throughout that conversation is powerful. You know, if I'm sat down, I'm a retail operations leader, a customer, and I've got our availability system, and I'm sat in a store wondering why why availability is so poor in this store, because the sales are fantastic, had a brilliant week, and you can have a conversation with our system, understand what's driving it, and I think that's really exciting, really exciting. We hear that as well, and that's something we're definitely capitalizing on.
Speaker 2:I think the other area is how AI can to that point I spoke about earlier around diagnostics. We're doing some really great work around root cause analysis, so figuring out why out of stocks keep happening and then making corrective action at a head office level, an upstream level, that essentially cancels that out in the future. Ai is an amazing augmentation of that process to help us move through that root cause tree much quicker, much more elegantly and, I think, get to an answer that retailers are happy and comfortable with much faster, and I think will mean that what is a very complex system in terms of root cause analysis there's so many reasons why this might be happening can be deployed, managed and used in a far more efficient way, easier way in the future, and that's really exciting. I think those are probably two examples that really stick out, but there's many, there's many.
Speaker 1:Yeah, no, I like those. I'm just thinking back to my store manager days. How would I feel if my area manager or somebody from the center came in and started interrogating the stock system by asking it questions? There's nowhere to hide, right? But ultimately, if you're a smart store manager or team leader colleague, you'll have interrogated it yourself first. So that surfacing data quickly is great.
Speaker 1:I think again, there'll be loads of organizations centrally where they've got their monitoring, supply performance and all those other things that that go on. So surfacing that quickly, you know, looking at suppliers with poor, poor supply rates, all those kind of things. It'll all be happening now. I don't think anybody denies that. But getting there quicker, flagging it earlier, predicting it even, will make such a a big difference to that whole end-to-end state. Yeah, be interesting to see, but I can only see positives from it. Yes, definitely We'll pause there, tom. Great conversation, really good to hear about the great things you're doing at Retail Insights. If somebody wants to get in touch with you, find out more, have a chat about maybe, how you can help, what's the what and where are the best places for that to happen I would say reach out on on linkedin as a classic one, or or drop us a note on our website and we can have a chat.
Speaker 2:We can come to store, grab a coffee, walk around and have a chat about what you're facing, what you're challenged with, and see how I might be able to help.
Speaker 1:Perfect. Appreciate you taking the time out, tom, always great to chat and we'll catch up soon. Thanks a lot, simon. All the best.
