Episode Transcript
[00:00:00] Speaker A: You.
[00:00:04] Speaker B: Welcome to Georgian's Impact podcast. I'm your host, John Pryle. 2020 was a challenging pandemic year. Ecommerce share of total revenue grew to 13.6% from 10.7%. Not a surprise, but I'm sure many expected that to continue. But no. In 2021, ecommerce rates dropped for the first time since 20 418. Now I feel like I'm a bit in the trees because I really want to talk about the forest, and the forest is that over 85% of retail sales are still in stores. Technology opportunity, anybody? Today we'll be talking with Francois Chaubard, CEO of Focal Systems. Focal leverages AI and cameras that focus on products on shelves. Focal automates many steps in the retail and delivery supply chain and is quite an amazing story.
Francois, welcome.
[00:00:57] Speaker A: Thank you very much for having me.
[00:00:58] Speaker B: Tell me a bit about yourself and how you got where you are.
[00:01:01] Speaker A: I come from mostly the AI side, building AI applications. And in 2014, 2015, we started to really see the applications of this step function in AI and what it could really do. Everyone was talking Silicon Valley in 2014, if you remember that this is the age of automation, age of automation. And the only applications anyone was really talking about was automating cars and automating doctors. That was really it. What about everything else?
[00:01:25] Speaker B: So I want to start really with an overview of the retail process. I mean, basically it's ensuring shelves are stocked and they're stocked with products customers want to buy. But there's so much more to it, right?
[00:01:35] Speaker A: Retail is one of the heaviest, most labor intensive businesses you look at, 13% of sales goes to labor. I think Walmart pays $100 billion a year in labor bill alone. It's really insane. They employ, I think, like two and a half million people globally.
I think the size of the United States army is not even that big. And so it's huge amounts of labor. So it doesn't take a genius to realize that AI is probably going to have a key role in that business. But the incumbents, I just didn't believe personally were going to be able to build those applications. Zebra NCR went public in 1880, right? I mean, it's like not 1918, right? It's going to take a long time for those folks to really grasp AI, then be able to develop it. So I think that was kind of the opportunity. So specifically, the question that you asked about availability. Right now, it's probably the number one concern of every single major retailer. How do I get my products on the shelf in the face of supply chain constraints, in the face of the labor availability going through the floor, wages going up and up and up, retention is going from on average four and a half months to two and a half months.
The turnover is insane. And so you have less experienced people that you have to pay more to and you can't find them with that. You're supposed to keep the shelf stock. That's very difficult.
[00:02:59] Speaker B: I'm sure it is a challenge. So let's talk about how the technology at the heart of all this image recognition has developed. After investing all your work in deep learning and computer vision, how would you say this tech has evolved over the years?
[00:03:12] Speaker A: The image recognition piece is really the big advancement that happened in 2012 with the Alexnet paper. I just started at Stanford AI lab in 2012 and that was right when the Alex net paper hit. For those that don't know, most important AI paper of the last 50 years, Alex Krazepski, Ilya Sutzker and Jeffrey Hinden out of University of Toronto, they published this paper called Alexnet and it basically just absolutely crushed this competition that every computer vision lab competes on every single year called imagenet. And imagenet is roughly 1000 classes. Cat, dog, pony, stop sign, 1000 images per class. And then you train the model and you have to submit into a competition on unknown test images and see how do you do. Here's an image. Is it a cat, dog, pony stop sign? What is it? And you have to guess. And at one accuracies I think were pre Alex net, 20 30%. At one accuracy of Alex net was like 63 64%, which is just insane step function nowadays at one accuracy I think we're at. Meaning that if I only get one guess, do I get it right? I think we're at 91% is state of the art. And that was basically the first time that someone took massive amounts of data, deployed these neural networks on top of gpus, used Jeffrey Hinton's 1980s paper called backpropagation and let it train over days, months to get smarter and smarter and smarter. And that opened up the floodgates on what that model can do, not just for computer vision and product recognition, which I'll get to in a second, but it opened up the floodgates, allowing for automatic speech recognition. Siri Alexa, same exact algorithm. If you go on Google Translate right now and you use it, you're using NMT neural machine translation. That is a deep learning model with basically the same exact framework. If you watch the documentary Alphago, how the computer scientists beat the best go player in the world, lease it all, that's same exact premise. I mean, there's countless. That's what focal does, really, is modeled right after that.
[00:05:18] Speaker B: I think about the whole retail process, and I don't know if this died or not, but I think a couple of years ago, Beacon systems were hot and they were tracking people walking around. And as I thought about it, doing research for you, I said, it's so much more important to look at how the products end up versus how someone wanders around the store. But I'm curious where you think about Beacon systems and why you chose to focus on products on shelves, which is what focal does.
[00:05:45] Speaker A: Yeah. When we started the company in 2015, I knew nothing about retail. I just knew it was going to be massively automated. And I was like, I'm going to be one of the first ones to be an expert in both AI and in.
I met, I think if you're in the Bay Area, I met with Richard Drager, who runs a four store chain out here. I bum rushed him when he was walking around. I said, hey, can you teach me retail? And I'll program anything you want. I'm a Stanford AI student.
He's like, sure, let's do so. And he said, it's not that difficult, right? You got to get products on the shelf and get customers out the door without stealing. That's it. No one else you want to know. And it's obviously being a little trite, but he was interested in, can the cameras do this? Can I do that? And one of the first applications, he said, is, I want to know how many customers are going down this aisle versus that aisle. And they were kind of interested in that. And so I spent a whole bunch of time, maybe three months, getting access to their CCTV cameras, trying to track where people go. And I had spent a couple five hour energy fueled nights. Next day I had this beautiful plot, and it showed me over the last six months, I had all the CCTV. I showed him the overlap and giant red hotspot at the checkouts, giant red hotspot during lunchtime at the deli counter, blah, blah, blah, and big cold blue spot over center of store. And he's like, francois, center store is dead. Yeah, thanks for telling me that. I was like, well, how much would you pay for this product? Like, nothing. You didn't tell me anything new, right? I didn't do that. You didn't know? People spent a bunch of time during lunch at the deli counter. I'm in the store. I know that. So it really isn't interesting. People talk about that application. There's 20 companies, maybe 50 companies that are talking about doing that, but they never really hit real revenues.
[00:07:35] Speaker B: It's interesting, but not a good business process. There is a supermarket chain, I forget the name of them, sprouts that totally redesigned the layout that you have to walk past the crummy, boring crackers to get to the vegetables because they assume you're always walking to the vegetables. Does that still sit in your model of. It doesn't matter.
[00:07:55] Speaker A: It doesn't matter. I mean, no one's going to pay for that. If you go into any traditional grocery store, you walk in, they are going to put the eggs and the milk all the way in the back. Why? Because that's the thing that everyone's going to buy, the highest movement and it makes you walk all the way to the back, walk by everything else. So you pick it up and then walk past a bunch of other stuff more likely to go grab some stuff. And that's been as true before computer vision, before computers, I would say.
But there's so many other use cases. I mean, if you're talking about from an engineering standpoint, I look at it as, okay, here's the pie, here's 100 billion that walmart spends a year. What can AI do of this? None of that is people counting in aisles. Why are we talking about that?
The two biggest buckets of spend are spent on getting product onto shelves and getting you out the door without stealing. That's where all the money is spent. The former robots can't do yet. We cannot get a robot to do this quickly, cost effectively, accurately. You just can't do it. We're not there yet. Checkout. Self checkouts cost $10,000 and depreciate over 30 years. That costs like ninety three cents a day or something like that. It's like insane. You can't beat a self checkout machine. That's why you see more self checkouts. So we've kind of already solved that and good luck finding anything. Amazon. Go. We put stuff on shopping carts that knew what you were throwing into the shopping cart, showed you an ad on the third thing you put in and all this kind of gimmicky stuff. And then you look at the cost comparison and we lost hand over fist when we got compared to a self checkout machine in terms of cost because you just can't beat it.
[00:09:36] Speaker B: Including the one where you've got like a scanner. Like a hand scanner. You scan it, put it in your bag to go out the door. It's ninety two cents or ninety four cents. It doesn't matter.
[00:09:47] Speaker A: Those hand scanners, just rough math here. They probably cost $15 a pop. They probably die every year. And you maybe get ten transactions out of them, maybe three transactions out of them a day, I'd probably bet. So you're paying $15 and how many transactions did you get of that thing? So you're paying two cent per transaction or something like that, let alone the upkeep. All that stuff the self Checkout machine does, let's say maybe 20 an hour. Times 20, times three, six, five, and times 30 years. And it costs $10,000. It's 500 times more expensive.
There's no chance.
[00:10:32] Speaker B: Machine with tongue firmly planted in cheek. Let's get to your bread and butter. And I didn't prepare that, but it just came.
[00:10:39] Speaker A: Yeah.
[00:10:41] Speaker B: So I want to talk about two parts. Let's talk about the shelves and stocking. So I have fantastic images on your website. Here's a camera, looks at every shelf. You could look at these products. You can recognize the products. You can see this brand of oil, this brand of vinegar salad dressing. I don't remember what the aisles are laid out. And you'll be able to quickly tell, I think you said you snap a picture every hour, so you'll be able to identify to someone in the back of the store that they need to quickly bring out some salad dressing is as simple as that. Once you've got the recognition, the labor is pretty easy. Right?
[00:11:18] Speaker A: Yeah. So I think you got to break out of stocks into three different chunks. There's out of stocks that are workable, meaning that there's inventory in the back, there's out of stocks that are not workable, but they're orderable. So you can order more cans of coke. Right. And there's out of stocks that are not workable and not orderable. And that's a supply chain issue. It's on a boat off of the coast of LA for the last three months, and nothing you can do about it. So that was the most common one. There's a strike at know factory, and there's no more cereal. So those are the three. You got to have a solution for all. Maybe, you know, going reverse order. If you can't order it, you can't sell air. And as a grocer, as a retailer, you're only as good as what's on your shelf. So if you have nothing, you're not going to sell anything. So you got to put something there. And today the sop is either. There's two sops, standard operating procedures. One, do not cover a hole with extra product because I'll lose that position and I'll never order it back. And it's gone forever, which is not good. The second one is cover over the facings, remove the price tag, that thing is gone forever. You broke the planogram. You'll never order that thing again.
And big no no as well. So you're kind of Sol focal would develop this solution called adaptive planograms and that we know one what the most chronic outs are. So what? Things that constantly go out of stock so we can adjust the shelf. If Pepsi goes out of stock by 05:00 p.m. Every single day in this store. Maybe not in another store, but this store, then maybe you should increase the number of facings. That's not the way planograms are set. They're set.
[00:12:56] Speaker B: You should define planogram, I think as well, if you go to a grocery.
[00:13:01] Speaker A: Store aisle, imagine yourself there. Close your eyes. Imagine yourself in the soup section. You're going to see two facings of every single type of progresso soup. Two facings of italian wedding, two facings of clam chatter, blah, blah. Meaning there's one next to each other. Right. What's the probability that in every single store in America there's equal demand for italian wedding and clam chowder and tomato bisque, right? No chance. So what happens very often, especially in northern New Jersey, if you said you were from Fishkill. So, yeah, there's a lot of Italians that came into New York and they moved into the suburbs if they were so lucky, like my parents. And they really love italian wedding suit. And so if you go to, you know, you're going to see exactly two facings and it's always going to be out of stock.
[00:13:48] Speaker B: Oh, my goodness.
[00:13:49] Speaker A: It makes no sense. But this is what happens. Because they don't have sophisticated systems and.
[00:13:54] Speaker B: Even the loyalty cards.
No, it didn't show it because it would just say if you had x amount of italian wedings, you'd be sold x. It didn't say there was demand of Y because you didn't do the demand. They maybe substituted.
[00:14:05] Speaker A: They don't have any system to create customized planograms on a per store basis. I use the analogy of shirts, right? Like, imagine that everyone's a different size. Imagine I'm a t shirt company. I can only make one size of shirt. So I make it smack dab in the middle. And I make a medium sized shirt. I don't make a small, I don't make a large.
And it's crazy. So if you go into every single shop, right, you see exactly two facings because that's what the average is. But in one store, you sell zero italian wedding, and you sell all tomato bisque, and in the other one, it's completely reversed. And so you're chronically out of stock. You're constantly spending labor, fixing it up. It's absolutely ridiculous. One of the biggest reasons for out of stocks, actually, is that the planograms are so you have this really restrictive constraint that they have to be the same no matter if you're in northern Jersey, southern Jersey, California, whatever it is, the shelf allocation you get, and it makes no sense. So that's one of the big things.
[00:14:59] Speaker B: I'm sure you have an ROI for that. So now, obviously, you've broken the planogram model.
[00:15:05] Speaker A: The ROi of that alone adapted planogramming results in, like, a 3% increase in sales and, like, a 5% increase in EBITDA.
[00:15:12] Speaker B: That's tremendous.
[00:15:13] Speaker A: And it's insane. No one believes that until they actually start putting it into their store. And then when you try to put it in their store, you're rewriting. It's almost like, you ever see the movie moneyball, where the guy, Billy Bean, the GM from the Red Sox, is saying, like, you're the first one through the.
You got the people who have their finger on the switch kind of go and, like, you'rewiring the way that they do business. This is crazy to. And. And if you go to a retailer, a merchant at a major retailer, and say, hey, listen, we're going to do per store planograms, I mean, they might fall over in their chair, be like, you're out of your mind. We'll never do that. But it makes so much sense. Yeah, I know. We just can't do it because they just can't. Right.
[00:15:52] Speaker B: Just to stay on that point, then they make money when somebody buys an end cap, so they buy some space on the end cap, and they make a certain amount of money. I've just got my head wrapped around Roi. Can you say, well, rather than take this much money for your end cap of potato chips, we're still better off putting something else there because we know what sells. Or is that money greater?
I'm just really getting to the nits here.
[00:16:16] Speaker A: Oh, man. I think that trade spend is a losing proposition for these grocers. But they don't know it.
[00:16:22] Speaker B: Right. That's what I was thinking.
[00:16:23] Speaker A: You were going to go 100%. I tell them all the time. How do you know that you putting ten facings of Pepsi on the 6th shelf is the right thing to do, is going to net you more sales or less sales. And how much less is it? Enough to recoup the trade, spend or not. And they don't have idea. And so that's a huge piece that we're getting into in this adaptive plenty of world. So anyway, so that's one of the sources of out of stocks. The next one is ordering. If you go into your local shop, right, stop and shop Kroger, Albertson's. The way that they order largely is they walk around with a scan gun and they shoot all the holes. Scan all the holes and Lowe's. And then they create a list and then they go and order it based.
[00:17:13] Speaker B: On order it because it's not in the warehouse, not the back of the store.
[00:17:19] Speaker A: In other words, it's not in the back of the store.
[00:17:21] Speaker B: It's your phase two. It's orderable, but not there.
[00:17:24] Speaker A: Okay. It should have already ordered. They have a system called CGO, or computer generated ordering, but very wrong. And I'll talk about why that is. It's so wrong. It should be really simple Transwat. This is like debits and credits. You have no cans of coke in your store. You order 100 cans of coke, you sell 50, you have 50. Why is this so hard? It's just not true. You don't have 50. The reality is that you didn't get 100, you got 80, or maybe you got zero, and you actually got 100 cans of sprite instead. And someone messed up in the warehouse. Happens all the time. It's called a misselect. It's like 3% of orders are miselected. Let's say that you solve that problem.
[00:18:02] Speaker B: You're saying there's not even that level of inventory management at the back of a store.
[00:18:07] Speaker A: Almost no one does receiving. They don't do receiving.
There's a lot of sources of product that come in. One of them is from my own warehouse. Let's say if I'm Shoprite, they have wakefern and they'll ship product. Kroger has their own distribution centers. For outside that, they use distributors like the three largest are kehi, unify and CNS. And so they will bring product. You'll order from them, and then they'll bring it over. And then the other big one is DSD, or direct store delivery. Coke, Pepsi, fritolay, which is over in Rockland county as well. They manage their own shelves, so they bring in product. They don't care about what your ordering system says. They just come in and they also.
[00:18:50] Speaker B: Have the jobbers that go on the shelves and count stuff. Right, exactly. Yeah. Right.
[00:18:55] Speaker A: Okay, so those are the three sources. All of that introduces error and no one is really doing it.
[00:19:00] Speaker B: And there's no common system. Now, does focal provide a common system?
I love your phrase that you talk about driving the store like you drive a car. So do you start at that end on the inbound with products coming in?
[00:19:14] Speaker A: Yes. So the first thing that you got to do if you want to automate anything, I don't care if it is a grocery store or car, is you got to digitize the space. You got to know what the heck is going on. If I'm in a car, I would need to put cameras all around it. I would need to tell that there's a car in front of me. I would need to infer that it's coming closer to me. So then I should probably stop so I don't crash into it. Similarly, in a self driving store, you have to have cameras in the back room. You have to have cameras on the sales floor. If you're trying to order more cheerios, if you think, hey, should I order more Cheerios? And you don't know what you have on hand, how can you do it? So then what happens, and this is typical, is that they're going to order from the scan gun and they're going to go around the CoI, see a bunch of out of stocks on Cheerios. I scan it, I see my inventory management system says I have maybe zero, and I order more. Okay. But in the back room, I had a whole pallet of Cheerios. They just don't know. And they do that all the time. That results in massive amounts of back room inventory and shrink because it eventually shrinks out. Sure. And so what does is we're digitizing the entire sales floor, the entire back room, all the end of aisles, the cashier stand, the produce, the fridges, everything, every hour of every day. So I know exactly what you have, and I can basically resubmit an order anytime I want the second it goes below some minimum amount that I want to make sure I always have on hand.
[00:20:38] Speaker B: I hadn't thought about it in terms of imaging.
It's the whole discussion about Teslas have cameras versus lidar systems and things. But there was a point, and I know, I think Walmart was pioneering having rfids for at least pallets and moving to cases and things. Is that gone too? It's all replaced by imaging. Now, is that where things are going?
[00:21:00] Speaker A: Yeah. It's the same exact vision where RFID, fundamentally from our first principles, could not deliver on that, let alone economically deliver on that. So ignoring the economics for a second, I worked at Lockheed Martin for four years on the spy one b radar. Electromagnetic radiation does not penetrate through water and metal very well.
[00:21:18] Speaker B: Got it.
[00:21:19] Speaker A: You got a lot of water and metal in a grocery store, in a hardware store, in fashion, it works perfectly. That's why when you walk out of Macy's, it gets right, like now you got the RFID tag on there. And let's say you solve that magically somehow. I don't know. Now, your biggest issue is economics. The margin of a stick of gum that cost $0.25 is, let's say, 10%, I don't know, 20%. So it's going to be five cents. An applied RFID tag cost like eight today.
[00:21:50] Speaker B: Okay.
I'm a big fan of imaging. The other thing I would like about it is it doesn't matter where they put the case of coke, you're going to recognize that if it doesn't have to go in the same place. Costco's are like that. There's always the above the shelf, there's stuff everywhere.
They stick it anywhere and they just know where it is. They have a more sophisticated computer system.
[00:22:08] Speaker A: Right?
[00:22:08] Speaker B: So they know that. Can you talk a little bit about other ways that people have tried to digitize inventory management, maybe outside of imaging? Yeah.
[00:22:16] Speaker A: So there's a lot of people that have tried to digitize the space with other technology. This isn't a novel idea. I don't think I've invented the idea that, hey, we should know where stuff is in the store. And so people have been at this for maybe 20 years, 30 years. So they tried weight pads, they tried light sensors, they tried drones, they tried robots, and all these technologies just kind of toppled over and didn't work. So there's a lot of ways to digitize it, to solve this ordering problem, to basically digitize the store. And so once, you know, hey, I have two case packs of Cheerios in the back room. I have a shelf full of Cheerios. Do not order more. IMS says I have zero. I don't care. Do not order more. It doesn't matter what IMS says. In the inverse, you would call that zero. Inventory ends, which happens all the time. It's scary. The opposite of that issue is IMS says I have 100,000 and I look in the back room, I don't see any. I look on the sales floor, I don't see any. Order more. And that's what, when you start to do that kind of stuff, it really starts to scare retailers. I didn't know how bad my IMS was because there's no truth. They have no idea how bad their inventory is. When we come in, we'll deploy our cameras and we'll override or make recommended overrides of orders, and we'll show a discrepancy list to them. We've had sometimes where we have like a 45%, 50% override of the orders. If they were going to order 1000 things, we told them not to order 50% of those things because the inventory is in the back room, and they just didn't know.
[00:23:43] Speaker B: Now, you talk about the term perpetual inventory. Is that what this is, or is that something different?
[00:23:49] Speaker A: Yes. So the next generation, there's CGO, which if you go into any shop right now, they have computer generated ordering, and it works in the way I just described. Then they started to get a little fancier. And so Walmart uses perpetual inventory system. They kind of pioneered it. They called it Oscar on shelf customer availability. And it basically goes like this. The problem that I was explaining earlier, where I order 100 cans of coke, I start with zero. I order 100 cans, I sell 50, I must have 50. The problem with that, let's say that you solve all the ingress issues and all of the egress issues, like shrink. Like shrink. The thief isn't saying, hey, by the way, I took 20.
You don't know that until later on. Breakage, all that kind of stuff, miss scans at the till, all causing error. But let's say you solve both those problems. Now, you do have 50. Where is it? Is it in the back room? Is in the sales floor, is in the customer's cart. Is it in the EcoM collections area? Is it in return, is it in the end of aisle?
All you're trying to know is answer the question, is coke available in the soda aisle?
It can't do it. So even if you solve all these things that you can't solve, then you still don't know. And so then Oscar came out with this idea. What? Well, you know what? If it's in the back room and it's not selling, then I probably am out of stock on the sales floor. And that's the logic. But this is why it doesn't work. And there's just a leak memo from Walmart on Business Insider about how bad these systems are and how much. Basically, there's a bunch of Walmarts that are turning the system off because it's over ordering so much. The reason why this doesn't work is because the median movement of a UPC in a Walmart, Home Depot Lowe's, is zero a day.
So to grab a product in a grocery store and look at its movement, very likely over half the time it sells zero a day, maybe two a week.
Now, I tell you it didn't sell today. Are you out of stock or just didn't sell? So then you have to wait and you have to wait a second day, third day, fourth day, fifth day, 6th day, 7th day to get to statistical significance for you to make the order. And so then they wait a week and then they're going to make, hey, you know what? We looked at the tstat tables. We're at 90% confidence interval. We'll order. Cool. So not only did you just wait a week of being potentially out of stock, you also missordered 10% of the time. Because if you're at 90% confidence interval, statistically 10% of the time, you're going to missorder and you're going to result in a huge amount of backstock. And that accumulates every single day. So it's crazy that this actual system is the best that they got. Once you have our cameras, we know within 30 minutes, the thing is versus a week. Right. That's a significant speed up. And the accuracy is not 90%, it's 99%.
[00:26:43] Speaker B: Right.
[00:26:44] Speaker A: And then the cool thing about the cameras is they're taking an image every hour, so it allows you to do that, which is great.
[00:26:49] Speaker B: So a couple of places I want to go, I was going to talk a little about reconfiguring stores, but it doesn't matter.
The people are going to walk down all the aisles. We talked about that earlier. I think we could just.
You don't see that as an ROI or an end result of using your technology?
[00:27:07] Speaker A: No. I think if you look at the economics and buyer behavior, I think if you permuted all the aisles, I think you'd piss people off for about three months and then they'd forget about it.
[00:27:15] Speaker B: Got you.
[00:27:17] Speaker A: I think there's a lot of store layout people that would disagree with that. Obviously you want a pretty looking store, but I would hard press them. I'm a scientist, like, prove that. You'd have to actually prove that to me.
[00:27:28] Speaker B: The other one, then I want to go down the route. And this may not be for supermarkets, it may be more for department stores. But there's an interesting integration for stores that have a very strong brick and mortar physical presence with ecommerce.
[00:27:43] Speaker A: So this is one of the three kind of what we call retail myths that we hear about all the time, is that we don't need grocery stores anymore. Online shopping plus these dark stores is all you need. And in 2015, when we tried to fundraise initially, I pitched some of the most well known venture capitalists in Silicon Valley, and it was remarkably hard. Over half of them told me the following. Why are you trying to automate brick and mortar retail? There won't be any grocery stores in the US in the next five years. We'll just have dark stores and autonomous delivery drones or buggies or self driving cars. They'll drop off to your front door in ten minutes. And this misconception is still common today. I mean, I have to kind of really show people the economics of why that is so wrong. And it's just completely in the face of every single data point that you can possibly find. The first piece there is exactly like you said, let's say 85% of all ecom orders are fulfilled in a traditional grocery store and going up, not going down, going up. And the reason why is because Amazon used to fulfill Amazon fresh from a dark store. They switched. They bought Whole Foods. The biggest ecom player in the world bought a brick and mortar retail and then put all fulfillment inside the Whole foods. Now in the Whole foods, kroger.com, safeway.com, shop right from home, Instacart, Doordash, uber eats are all going into. They're not doing the dark store model. While doordash, I think, has, like, three of them. They're all picking from a traditional grocery store. And the reason why is because they don't have to pay for rent. They don't have to pay for any of that. You got to have a node in the graph anywhere. And so it's either going to be a dark store or light store.
[00:29:22] Speaker B: You're paying for a picker. Yeah.
[00:29:24] Speaker A: What's the node in the graph going to be? Okay, fine. Let's say that right now, we'll go through a thought experiment. Let's say right now you're the CEO of a thousand dark stores across America right now, and you make, let's call it 100 billion a year right now. And business is great. People order. They come in, you have some pickers, you're paying, and you're paying rent for these stores. You got to pay the electricity bill, you got to pay the lease. You got these people that are picking for them, putting it in cars, and people are driving off Kroger. You look at their ten k, there's $130,000,000,000 a year that you're not getting because some people don't want that. They want to walk into the store for whatever reason. We don't need to hypothesize about smell their mangoes or whatever it is. Whatever it is. I don't know and I don't care. But they have the choice, and they're choosing 130,000,000,000 to go to Kroger right now. That's, I think, their revenue last year. Why would you not open up your doors to go get that customer too? You're already paying the lease. It's not any more lease. You don't need any more square footage. You might need more stocking because you have so much more volume. Oh, no, you have more sales. What a conundrum. Are you kidding me? So basically, this thought experiment concludes with the right answer is a light store is what we say, like an automated brick and mortar store with an e commerce option. That's the model that Amazon has now decided which is the leader in e comm, right? They definitely know more about dark stores than maybe anyone because they have fulfillment centers. This is the model that Walmart now has converted.
Everybody is pushing towards that model, and I think that's the right model.
[00:30:58] Speaker B: Let's talk a bit more about ordering online and having this human being pick your order from the store before shipping it to you. Is this something the focal addresses?
[00:31:07] Speaker A: One of the biggest issues in EcOM right now is substitution rate. So if you're Kroger, 25% of your order will get substituted out. On average. That's like industry average.
[00:31:19] Speaker B: Meaning I make the decision that I don't want French's mustard, I want Gouldon's mustard. When you say substituted out, is that what you mean?
[00:31:25] Speaker A: That you buy 100 products on kroger.com or safeway.com or Instacart, right? And of those 100 products, 25 will not show up. 25, maybe half of them will get substituted out. You get refunded. The other half leads to a huge disappointment. And the reason why substituted?
[00:31:44] Speaker B: Oh, not by me, by the picker. So I order on safeway.com, I order 100 items, and you're saying 25 of those items are not the ones I ordered. They grabbed something else off the shelf.
[00:31:54] Speaker A: They'Ll text you and they'll say, hey, that's not here. What else do you want? Right and they'll say, here's your other options. Sometimes they might up level you. So that's a common thing. This is actually a big issue right now in ECoM. Let's say that I ordered French's mustard and I ordered twelve ounce. There's no twelve ounce, but it's 24 ounce. And so I will put 24 ounce, and I will charge you for it.
Are you kidding me? I mean, the analog equivalent of this, I said this to a retailer, and they almost threw me out of the room. But I think it's really true. The analog equivalent of this is imagine you went into a grocery store and you bought 100 products. You're pushing your cart to your car and you're stocking it, and you're putting it into the trunk. Cashier runs out after you takes 25 out, puts it back into the cart, brings it back in the store, comes back out with 25 other products that you didn't want, and then gives it back to you and then gives you a bill for it for the difference.
Are you kidding me? I mean, this is happening in every single ecom transaction that's happening today. It's crazy and focal. We have the solution for it.
[00:32:59] Speaker B: This should be something that will just go away because you'll be able to know that you need the 12oz of mustard, and it'll be there and you'll solve these problems very quickly, I would think. Right?
[00:33:10] Speaker A: Yeah. I mean, we have the cameras, right? So we know exactly what before you buy it, before you hit buy and pay it for it with your credit card, on Instacart, on Doordash, on whatever, we know that they cannot fulfill it in this store. Maybe they'll divert the picker to a different store so that they can fulfill it. And if they can't, even then, then maybe they say, like, before you even click submit, they say, hey, listen, we just checked the store. Within 30 seconds, we checked all the focals, cameras. These are the eight products that we can't deliver. Do you want to sub out? Do you want to try something else right then and there before you buy it, rather than after you buy it, which maybe could be three days later, it could be an hour later, which is really inconvenient.
[00:33:49] Speaker B: Francois, fantastic discussion. I learned so much. I really appreciate you giving us the time.
[00:33:54] Speaker A: Thank you so much. Appreciate it.
