Environmental Data: Optimizing Supply Chain

Brian: [00:00:00] Hello and welcome to Future Commerce, the podcast about cutting edge and next generation commerce. I'm Brian, and I am here live at GroceryShop 2019. Super exciting conference put on by the founders of ShopTalk. Just really, really interesting content and people and ideas and all kinds of really cool new ventures that are being brought to the table here. And I'm here today with Hugh Holman, who I'm really excited to talk to. Hugh is the co-founder and CEO at Observa. And he's also from Seattle. Hugh, introduce yourself.‍

Hugh: [00:00:39] Thank you very much, Brian. I'm excited to be here as well. Like you said, GroceryShop is really an amazing event. We were here last year, too. And I'm glad to be back.‍

Brian: [00:00:49] Awesome. Yeah. Welcome to Future Commerce. I'm so excited to have you on the show, because I think what Observa is doing is really, really groundbreaking and something that I see having a bright future in this industry. So maybe tell us a little bit about yourself before we get into Observa. Who are you? How do you get your start? We're both from Seattle, which is awesome. I love being around a fellow Seattlite. Tell me a little bit about, you know, who you are.‍

Hugh: [00:01:21] Sure. Well, you know, I spent most my career as a technology manager. I started out in a startup and then progressed to, you know, a little bit larger startup again and again. And then during the dot bomb, I found myself going to a large financial institution, and it was functioning much like a startup inside where we were in-sourcing technology away from a large service provider. And after that took a CIO gig at AquaStar, which is a global seafood brand out of Seattle. Also another Seattle connection there.‍

Brian: [00:01:54] Nice‍

Hugh: [00:01:56] And AquaStar was really amazing for me because it put me in a role where I was really in control of driving technology and the technology strategy. And at that point in time, I went back to school and got my MBA at the University of Washington.

‍Brian: [00:02:11] Oh, that's my alma mater.‍

Hugh: [00:02:13] There you go. Another connection. And which was excellent. By the way, my co-founder, Erik Chelstad, was earning his MBA there at the same time. And we'll talk more about Erik later, I hope, today. When I was done with my MBA, at AquaStar they offered me a different role, and I moved into a strategy role to help that company to kind of modernize and approach the market differently. And we really did. We became a more data driven organization, more [00:02:43] brand forward focused organization. [00:02:46] And eventually I took over retail sales, as well. And so now I'm, of course, running 400 plus million dollar business in the retail sector. And it really was interesting for me, and it's foundational, and it's kind of what led me to start Observa. And so it was really awesome in my career.‍

Brian: [00:03:07] Ok. Tell me a little bit of Observa. So why why does that lead you to start Observa? What is Observa? Why did you start it?‍

Hugh: [00:03:15] Well, at AquaStar, I identified this problem in retail where you just had a black hole. There was no real time data about what was happening in the store. And you can subscribe to data sources, Nielsen and IRI are great, interesting sources of data where you get these four week chunks. Listen to that. You get a four week chunk of data, meaning you're not getting real time data, and it's aggregated, and you're able to see, you know, how your brands or products are doing in the market relative to your competition, which is awesome. But that's not how we think today..‍

Brian: [00:03:49] Right. ‍

Hugh: [00:03:50] We think based on what the real activity is. When e-commerce has trained us to want that kind of information. We can follow the path of the consumer. We can see what they're doing. We know what products were presented to them. We know what they're doing before they buy the product, before they put it in their cart, and what that process looks like. You can't see that in a grocery store. It's a black box. And so I identified this problem at Observa where we were building trade programs, and trade programs are where you're giving dollars to the retailers. We're working with the biggest retailers out there. I'm talking Walmart, Albertsons Safeway, Kroger, you name it, here in the U.S. and Canada, in Great Britain and so on building better, better trade programs to move our product, to put promotions in place in the stores, getting adequate shelf space for our products, growing our presence and also putting deals out there at the right time for consumers to drive increased demand to get those new customer acquisition and so on where we didn't know whether any of that was really happening.‍

Brian: [00:04:53] Ahhh.‍

Hugh: [00:04:54] It's just crazy, right? Because most brands don't have a field workforce. And so we're getting anecdotal information, meaning when one of our sales guys is out in the field, maybe a family member or member of an employee of the company tells us there's a problem. They can't find our product in the certain store. That's helpful. But it's not complete. And so what I identified was there was this problem of being the see, what the consumer sees in the store, in real time, and with data and mobile phones and so on, it provided that opportunity. And voila, that's the genesis of Observa.‍

Brian: [00:05:32] That's super cool. You took this what was basically a black box of what was actually happening, and now it's open, and you can actually analyze it and use that data to make informed decisions. This is a huge trend in in-store brick and mortar, having it sort of match how we think about e-commerce and think about conversion and think about, you know, representing our brand well. So I would imagine you're having to use some pretty interesting technologies to accomplish this. So tell me a little bit about how you're able to capture this data. What are you using to do that?‍

Hugh: [00:06:08] Yeah, it's so we're really a retail audit service company, so we're measuring a retail store execution. And I know these are industry terms. And so I hope you did a little bit of an understanding of how retail works. You're paying for space out there on the shelf. You're paying for promotion. And so what we're doing then is we have a field workforce that we've built. We call them Observers. And it's over 90 thousand people across North America, for instance. They go into stores and use our mobile apps to collect information. They're auditing what's happening on the shelf where products are placed, or promotions elsewhere in the store. You may be familiar with display promotion, where you walk by a product maybe in a different section than you would normally find it in a retailer, or end caps, which is the space at the end of the aisle which changes on a weekly basis with new products. We're going into the store with our field workers to find out what's actually happening. And so they're using our mobile apps to collect data. They're collecting information. They're answering questions and collecting photographs. So what we do with that is we process that information using artificial intelligence and image recognition to turn it into photos and more data. And so we have a lot of information about what's happening on behalf of the brands that we represent. We also work with third party service providers, which are advertising agencies, distributors, merchandisers and so on, as well as retailers themselves to show them what's actually happening in the store. And the thing about our platform, the Observa platform, is we're really kind of agnostic to the data capture mechanism. So we also have clients that capture the same kind of information using our mobile apps. And we take feeds into our API or application programming interface from clients where they're sending us photos out of their existing systems. So it can come from a camera that's fixed in the store. It can come from a robot or a drone. It doesn't matter to us. We're all about processing and delivering quality data.‍

Brian: [00:08:11] Wow. Wow. So you have your app, but you can also accept feeds in from different other image sources you just mentioned...robots, cameras, drones. Are you seeing brands and retailers actually using cameras in store to capture this data?‍

Hugh: [00:08:30] It's just starting. And, you know, that's why we build out our crowd. We did it because we wanted to make it easy for companies to start using our services. We know where this is headed. We know that it's heading towards, you know, ways to collect the data, where it doesn't require a human, it doesn't have any additional effort, where it's more consistent. And we love that idea, but we're not waiting for that to force itself to happen. We know that it's going to be shelf cameras. We know that it could be robots and it could be drones. And things are being tested. There is not mass adoption yet, and we're looking forward to that.‍

Brian: [00:09:03] That makes sense. Yeah, I love the approach of using sort of the human manual element right now, but being set up for when you can get this just from like a live camera stream or other kinds of sensors. And I think you're right. Like we're just at that the cusp of seeing more adoption on that front. And so I think it's really smart that you've built a platform that can work with any kind of a stream, whether it's actual people or something from in-store.‍

Hugh: [00:09:36] Absolutely. Because in the end, what we're looking for is information. Our clients or making business decisions based on the information they have. Like I said in the past, it was a black hole. They had no information. And so now we're able to provide that today. We're not wait for tomorrow. The technology will advance, and we will utilize the best, lowest cost mechanisms available as that happens.‍

Brian: [00:09:58] Yeah, that makes sense. That's good. So what happens with these images once they come in via a manual capture, via your app and a person, or via these video streams?‍

Hugh: [00:10:10] Yeah. So we're using computer vision and within computer vision, we're using artificial intelligence to do image recognition. And so I'll kind of tell you how this works. We're training the intelligence and neural network in the computer system to recognize products. And we do this by training it with hundreds, if not thousands of images of that product that we capture out in the wild. We're getting them from the stores because we have people in the stores all the time, and we train it up to recognize those products, so that when [00:10:43] we receive photos in on our platform, we can instantaneously digitize them and recognize what's in the photos. And this is really amazing that it's something that's come to reality today. And what it enables us to do is actually measure against expectations. So now we're back to the same problem that I was mentioning before that I identified at Observa. I had an expectation. I thought that certain products were on the shelf. Well are they? Now we can capture that photo, process it, measure it against the expected results, and tell what the deviations are. And this is super powerful because not only can we identify the deviations, but it leads us to the ability to fix things in the store. [00:11:28]‍

Brian: [00:11:28] Yes. So I was just at REI flagship recently, and I saw this table that was dedicated to Filson, which is a really cool brand. And they did a really nice job of setting up the display. But I would imagine that Filson has a very specific way that they want their brand to be represented there. Like this jacket on the model. And, you know, this shirt is folded this way... But I saw another display, and I won't mention what brand it was, where there was a lot of stuff missing from the rack. And maybe that's because they were doing really well and things were selling really well that day, so there just wasn't as much available... But, you know... So this is the kind of data you're trying to capture? Correct.‍

Hugh: [00:12:11] Absolutely. [00:12:12] And what you're pointing to is what happens every day in stores. The problems occur and re-occur, whether it's that the product never made it to the display location it was supposed to be at, or whether it ran out of stock. Out of stocks or a huge problem in retail. And what we're able to do is very quickly measure those situations, and direct actions to fix them. And so you end up leading to a much better consumer experience where you are more likely to go to the store and find what you're looking for. [00:12:43]‍

Brian: [00:12:44] How often are you monitoring that? Like, is it every day sort of situation, or is it an every week situation, or is it an all day situation?‍

Hugh: [00:12:52] It really depends on the client and what the requirements are. And it's all driven by decision making and actions. And so if it's a consumer brand, they might want to measure on a weekly basis, or maybe a daily basis if they have a certain promotion happening in a store. If it's a retailer, they're more likely going to want to measure on an hourly basis, or at least more frequently, because they have people in the store all day long that can actually address the issues.‍

Brian: [00:13:21] Yeah, that makes sense. Do people use this data to help plan or to inform the actual inventory planning, and stocking planning?

‍Hugh: [00:13:31] Yeah. Excellent question. Yes. So a supply chain, on a whole, has been missing this information. The inventory data in most retailer's computer systems is not accurate. And there's a lot of what is referred to as phantom inventory, which is where the computer shows that there's product where it doesn't exist in the store. And then it happens for many reasons. Theft or shrink is a common cause. And unfortunately, there's no way to relieve it until you identify that the problem exists. And so this is one of the things we help our clients with often, is identifying those phantom inventory issues or the problems where what the computer system shows does not reflect reality and help relieve those problems and fix them.‍

Brian: [00:14:20] That's interesting. I can see is totally playing into an omnichannel strategy where you need to know if an item is available in store, and if someone's going to purchase the item online and pick it up and store, you know, having that sort of data right then and there would be really, really useful, especially as we have cameras that are placed and just monitering in real time. That's going to be huge because that's a real problem right now. Understanding what's actually in store, what's available... Retailers and brands always end up erring on the side of caution, or they want to error on the side of caution right now, if they have a true omnichannel strategy. So, yeah, I can imagine that's really useful from that perspective.‍

Hugh: [00:15:07] It is. And [00:15:09] you're bringing together an excellent point about the consumer experience these days. It's not all brick and mortar. It's not all online. It's a combination of the two, and [00:15:19] how those things merge. And the whole idea of omnichannel originally was about how you manage your inventory, and that's changing. It's really about the consumer experience, because in the end, that's all that matters, right?‍

Brian: [00:15:34] Yes. Yes. ‍

Hugh: [00:15:35] So managing the consumer experience is very important. And another thing that comes into play with e-commerce, now that people are ordering online and picking up in this store, or ordering online to have it delivered to them, is you have somebody else that goes to the store and will pack that shipment.‍

Brian: [00:15:53] Right.‍

Hugh: [00:15:54] Right? And so they're going... They're being directed to go to certain places in the store to find the inventory. And if they don't have accurate directions and know that that inventory exists, they'll tell the online customer that they're able to fulfill their order when they really aren't. Our technology is able to deliver better quality information on what is actually there in the store to be picked and packed for that client.‍

Brian: [00:16:17] That's amazing. I see like a lot of tying to digital commerce here as well. So I would imagine that with this data, you're able to start to do some analysis on how customers are interacting with how things are set up, or planogram, as they call it in the industry, right? And we've never talked about planograms on Future Commerce before, and I'm super excited to talk about them for a minute. So one of the things you're doing is you're measuring against planograms, right? And so are you able to help retailers and brands be able to get a better idea and make better decisions around how their products should be set up in store?‍

Hugh: [00:16:55] Absolutely. And just so that everybody is aware, a planogram is the design of the shelf, it's sometimes referred to as the modular, but the products in most retail stores aren't put out there are haphazard. It's to a design. And so what we're doing is we're measuring against that shelf design. And there's a lot of science behind designing how products should appear on the shelf. And there're dollars behind it because most of that shelf space is sold to the consumer packaged goods brand. And so they're paying for that placement. So when the marketers do their analysis, they're looking to drive the most revenue out of a category and ultimately profitability for the store. And so they really want the stores, the individuals stores, to be set up to those standards. And of course, we've been talking for this entire podcast about the fact that that's not what we're finding in the stores. It is not what happens. [00:17:46] We love measuring against a planogram because it is the expectation. So by getting that expectation, we're able to quickly tell what the differences are, the problems there in the store and started direct change. [00:17:58]‍

Brian: [00:17:59] That's amazing. [00:17:59] I was actually just talking to someone recently who did an analysis of their planogram and how their products are being displayed in a store. And they did some like in-depth analysis on it, deeper than they'd ever gone before, and as a result, and as a result of configuration changes, they actually ended up doubling their shelf space with that retailer, the brand did. And they saw exponential growth, not just linear growth, but exponential growth in how much they were selling through that retailer. [00:18:30] How are your customers, you know, seeing results from optimizing this sort of data?‍

Hugh: [00:18:36] Absolutely. First of all, they have the data. So you start to see what is leading to the sales results because with a retail store as a black box, you know how much product may have been shipped to the store. And sometimes you don't. The brand might only know how much products are shipped to a warehouse and feeding multiple stores. But then you might have sales results coming out, but you don't know what happens in the middle, in the store itself. You know how many items are actually on the shelf? You know how many facings, which is the number of slots on the shelf that the item might have. And that determines how much inventory is being hold on the shelf. So how much is available there for sale? So when we're doing the measurement for the clients, we're able to tell them, are they getting what they expect? And then we can also start identifying issues about them running out of stock faster and how to maybe reallocate space from products that aren't selling as well to products that are selling faster than they think. Think about it like this. If you had your dominant product, and it's selling out by noon every day...‍

Brian: [00:19:35] Right.‍

Hugh: [00:19:35] ...you don't have enough shelf space.‍

Brian: [00:19:36] Right. Exactly.

‍Hugh: [00:19:37] So how do you get more space? You have to have that argument for the buyer that you're working with at that store chain to gain more space. You're going to do that one of two ways. You're either going to make an argument to give you somebody else's space or take that space away from another brand and their product, or you're going to take it away from one of your slower selling products and give it to your higher selling product.‍

Brian: [00:19:57] This is super interesting. And it's more complex than e-commerce in many ways because in e-commerce you're merchandising. There's really no limit to the product on the shelf, so you know exactly when people are clicking on things and how often and you know what your conversion rate looks like. But in-store, because of that limiting factor, you don't have the same kind of knowledge right now, unless you're using Observa, to be able to have that real time analysis. I would imagine that retailers are trending, and correct me if I'm wrong here, but they're trying to get more and more real time with their data from you.‍

Hugh: [00:20:39] Absolutely. [00:20:40] I mean, really, e-commerce has whet the appetite of all players in the retail markets for more data about what's happening. In e-commerce if you spend a dollar, you can track it through to getting a dollar fifty in gain, right? It's really hard to do that with brick and mortar. At scale, when we're measuring the shelf with Observa over and over, we end up being able to report much like Google Analytics over brick and mortar. And with that, you're able to then do A/B testing and use other marketing mechanisms that you do on e-commerce. And so we really see the future as being very bright in brick and mortar with this type of technology. [00:21:18]‍

Brian: [00:21:19] Wow. Yeah. Yeah. Cause you could run one configuration in one store and another configuration in another store and see how they play out against each other.‍

Hugh: [00:21:25] Absolutely. ‍

Brian: [00:21:26] That's super, super interesting. This is again, just part of a larger trend that we're seeing towards being able to use the data from in-store like you would in e-commerce. I expect to see a lot of people that are in e-commerce right now get hired into roles, and there should be a whole new set of roles in terms of analysis and strategy for brick and mortar that has traditionally sort of lived in the e-commerce worlds. Have you started to see roles be created within organizations because of the data you're providing it?‍

Hugh: [00:22:01] Yeah. Even I yesterday here at GroceryShop, I was talking to people, you know, major brands that everybody listening to this podcast knows, where they're creating either roles or teams, even groups of people focused on retail execution, store execution, you know, different types of terms like that where maybe they didn't have that focus before. Maybe they left that all to their distributors, for instance, and they're taking control of understanding what's happening in brick and mortar, because they know that by impacting that consumer experience and really managing that last mile with the consumer is how to have the greatest impact. And we're excited to be a part of that. ‍

Brian: [00:22:46] Awesome. I would imagine that not only are you able to affect the configuration, but also maybe product assortment as well. And so does Observa provide people with recommendations on how to manage their assortment within a store as well?‍

Hugh: [00:23:07] Yeah, we take a bit of a consultative role in that aspect. You know, we're capturing lots of information. And we're looking at the trends over time for the brands that we work with, and you can see the patterns. You can see where products are, to use my example from before, where they're running out of stock and one item, but the other one's always on the shelf. Reallocating spaces is a great way to grow sales. You're working with existing resources. The brand already has the space. And so we do make those types of recommendations.‍

Brian: [00:23:40] That's cool. Another thing I was thinking about is, with this sort of data, I could see this being really powerful with some other emerging technology that's coming to play in stores and, you know, using this data with see like a foot traffic data and other types of in-store interaction data that's being gathered from other platforms. Do you see your retailers combining data and building sort of dashboards arounds everything that's happening in store? How closely are customers being observed in store right now that you can see?‍

Hugh: [00:24:14] Well we know that some of the retailers are playing with that. They've been using heat maps on traffic and grocery store in a long time, you know, because they need to get the consumers to walk the perimeter, walk down aisles, and they use different tactics to kind of direct and change the patterns that people walk in the stores. And so we see that, and we know that that's being used. But they're using newer mechanisms to track certain people and segmenting the customers walking in stores and stuff like that. And we haven't been combining our data with those sources to date. But we expect, going down the road, for retailers to want to capture all kinds of environmental data that will help them better optimize the supply chain on the whole. If you think about it, weather impacts demand for certain items, other activities in the community, sporting events, concerts, whatever... Those types of things impact demand.‍

Brian: [00:25:14] Yes. Absolutely. ‍

Hugh: [00:25:14] And so by planning for as much of that in advance as possible, helps brands sell more. It helps the retailer optimize for the consumer experience. And everybody's happier.

‍Brian: [00:25:26] Absolutely. Another emerging trend that's coming to stores near you everywhere is that checkoutless payments.‍

Hugh: [00:25:34] Oh, absolutely.‍

Brian: [00:25:35] And I can also imagine that as that develops, that technology develops and retailers adopt checkoutless payments on a greater scale, I can imagine Observa is actually going to get even more important because the transaction is actually gonna be closer to the item. Have you started to think about that future and where you're going to play into that?‍

Hugh: [00:25:55] Yeah, I mean, because we're doing image recognition for the items, we definitely see a role there. We're talking with other companies that are in that space. It's being tested at lots of retailers with different platforms today. And we know that image recognition is an important part of that. But the reliance on the barcode presents challenges for a lot of retailers. And with the consumers doing the checkout themselves, there's the possibility for them to make mistakes. And so by being able to identify the package and not just rely on the barcode, there's huge value. And so we see ourselves moving into that space as we go down the road.‍

Brian: [00:26:34] That's awesome. Now, let's talk a little bit about brands, because I feel like a huge part of what you're doing is very brand centric, right?‍

Hugh: [00:26:43] Sure.‍

Brian: [00:26:43] I think that the way that brands are interacting with their customers is changing right now in a very, very big way. We've got a lot of digital native vertical brands, the DNVB's that are coming to market. You see neighborhood goods, which is the, you know, sort of DNVB department store, just get another round of funding. And so the way that brands are interacting with their customers is clearly in flux. And so how are you thinking about brands? Is stressing the integrity of your brand representation just as important as the ROI of Observa? How are you positioning this to brands specifically, maybe not retailers, but brands?‍

Hugh: [00:27:31] So we work with some major brands. And when you're a brand, having your product presented the way that you expect, is very, very important. There are lots of promotions that happen at retail. In the brands design with their marketing team, they'll design how the product's supposed to be placed out there, which pieces of point of sale materials should be out, how it should be priced, displayed to the customer, how much inventory should be available there. You know, even how they sample products in stores, we measure all of that. So we work with the brands to help them measure the quality of the presentation of their goods and how the consumer's coming into contact with the brand in that last mile. And I don't think there's anything more important to the brands than ensuring that their marketing is deployed the way that they design it. And it does lead to sales. So I don't think that it's one or the other. I think those things go hand in hand. And it's really interesting, because how is marketers supposed to be able to measure results if they don't even know if the execution happened correctly? And in playing that role with Observa, where we're capturing that information for the brand, they're really able to measure the ROI where they haven't been able to in the past.‍

Brian: [00:28:57] Interesting. So I think that brands these days are really looking to tell a story. And so that display, you know, often leads back to that story.‍

Hugh: [00:29:05] Yes.‍

Brian: [00:29:06] And so what you're really doing is you're saying, "Hey, your story is actually being told the way you want it to be told."‍

Hugh: [00:29:13] Absolutely. And you know what? It's not only for the consumer brand, it's for the retail brand, too. So we're working with retailers on their marketing assets and measuring execution of those marketing assets, making sure the right pieces of marketing are in place at those retail locations, because in the timeliness of that, it all ties into other marketing efforts. [00:29:34] So you think about print media, advertising on the radio or the television, these campaigns all go together to impact the consumer and tell the story. And so we're out there measuring for the brand, we're measuring for the retailer, making sure that these things are cohesive. [00:29:51]‍

Brian: [00:29:52] This leads me to another question then. Do you feel like right now retailers have more power than brands or brands have more power than retailers in store, in the retail store? And where do you see things headed? Do you feel like brands are going to really have the final say on their shelf space or their displays, or do you feel like retailers are going to have the final say? And do see that as being an in conflict sort of scenario or you see them partnering together more?‍

Hugh: [00:30:23] I see it is both. I mean, they're definitely in conflict in negotiating for the space because the retailer is charging the consumer brand for most of the retail space. And so, you know, that's a negotiation so that turns the conflict into a partnership, in a sense right? You know, as we go down the road, there's a lot of conversation right now about segmentation of the market and presenting curated experiences at retail. And grocery stores, for instance, are looking at this, and what we might see, as we move down the road, is sections of the store for different people where they're coming into contact with the likely basket of goods that they're looking for and common products that maybe other people in the segment buy together as opposed to within a category. And it's something that's being tested in certain stores. But it's not very broad, but it's an idea that's out there.‍

Brian: [00:31:25] Yeah, I think that's super smart. You think about walking into a department store, and it's like everything's organized in a very like disparate way. It's like clothes: men, women, children. Home: bedding, bath and beyond. [00:31:41] You've got these very logical organization by grouping as opposed to what a customer might actually want. And so I could imagine your data is really helpful for retailers to start to help sort of craft the story. Actually, what I'm seeing right here is this is where brands can actually start to partner together, and retailers can almost be a broker of that discussion. Because right now, a lot of times brands are just selling in their little section, in their little world, and their little brand story, and they aren't thinking about how they could be playing into other brand stories. This has been a big theme on our show recently, as well. The brand stories that are told together are actually way, way, way, way more powerful than brand stories alone. And so retailers actually have the opportunity to make that story the most, like the more compelling and help brands understand the value of what a complementary story would look like. [00:32:47]‍

Hugh: [00:32:47] Absolutely. So now we're talking about basket of goods. So your basket of goods may look a little different from my basket of goods, but if the brands come together using the underlying data, understanding which products go together, and present an offering to the consumer via the retailer that is positioned for that specific consumer, you're going to walk in and buy everything you need at one location in the store. And that's really a different shopping experience than you have today.‍

Brian: [00:33:17] Much different and also very exciting. I think this is huge for story, for storytelling, and also for customers. This is going to make my life way, way easier when I go into a store. I think that's really exciting. Well, that's really interesting. I think this sort of data and other types of data streams like it for in-store purchasing are both going to elevate brands to a new level and also help retailers be able to survive in this environment. I think that's another thing that we're seeing right now is, you know, a lot of retailers are going out of business. And I think that a lot of them are ones that have not focused on investing in technology, like Observa, to be able to have gathered data like Observa gathering. How do you see Observa being able to elevate some of these retailers out of the situations they're in, in this retail apocalypse environment that we're in?‍

Hugh: [00:34:22] Sure. [00:34:23] So, first of all, brick and mortar retail is growing. So that's the first thing. It is growing. There's so much focus on e-commerce, which obviously has high growth, but brick and mortar is growing as well. The retailers themselves are changing. The shopper experience is changing. And the consumer expectations for that shopper experience has changed based on predominately their experience online. And so I think that that's what we're going to see going forward, is the retailers that really pay attention to their consumer and listen to what their wants and needs are, watch their path to purchase, and then try to implement changes in that consumer shopping experience that really meet those changing demands and expectations are the ones that are thriving and living. And, [00:35:15] you know, it's interesting because there are a lot of direct to consumer brands that are now opening retail stores. You see companies like Warby Parker or Allbirds. I'm on the board of a company called Rad Power Bikes. They're testing store locations and are seeing what the impact is in that local market of having that tangible ability for the consumer to go into the store and touch the product, to try the product. And it's powerful in driving a different relationship with that brand, a strong relationship for the consumer. And the traditional retailers need to understand this. And they need to approach not only e-commerce with that kind of open eyed ability to try new things with their consumer and ask them how they feel about it. They need to bring it to brick and mortar, too. And the ones that are adopting the technology, really have those conversations going to their consumers, and want to deliver the best experience possible are going to win going forward.‍

Brian: [00:36:24] That's awesome. I think it's a good place to wrap up our conversation, but before we leave, I would love to ask you what you think is most important for retailers to focus on right now in the next eight months. And do have some advice for our brands and retailers that are listening about things that they should be looking ahead in the next five years?‍

Hugh: [00:36:49] Yeah, I think that I'm going to go back to the consumer experience. [00:36:53] You know, nothing's changed in retail in the sense that it's always the consumer is the most important thing. Without a shopper or somebody buying the products, whether you're a consumer brand or the retailer, you don't have a business. And so the focus on understanding the consumer, what the journey that they take is, understanding that it may involve e-commerce and the store, you know, using their mobile apps and then delivering to their expectations is of paramount importance. And having data to drive the understanding is really fundamental to that. [00:37:26] And I'm excited to be able to offer Observa services to the retailers, to the consumer brands, to really understand, you know, what the shopper's coming into contact with, what their experience in the last mile is, and to be able to impact that and help them drive to meet higher and higher consumer expectations going forward.

‍Brian: [00:37:47] That's super exciting. Well, thank you so much for coming on the show, Hugh. It's a pleasure to speak with you and learn more about what Observa is doing in this world of gathering data in a brick and mortar level, and I'm really excited about the future of what's ahead with this. And thank you for listening, to our audience. We always love hearing your feedback. So if you have thoughts on how brick and mortar data can be used in the future or anything related to this conversation or stuff that you saw at GroceryShop, if you're here, we'd love to hear from you. Go leave us a comment on our website, FutureCommerce.fm or reach out to us on LinkedIn or or email me or Phillip... Brian@FutureCommerce.fm or Phillip@FutureCommerce.fm Thank you again for listening, and we'll talk to you again next week. Thank you.‍

Hugh: [00:38:39] Thanks for having me on, Brian.

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