Mister Beacon Episode #77
Leveraging Accurate Indoor Location in RetailJune 27, 2018
ThinkInside is working with some of the most successful retailers and manufacturers in the world. Making physical space “smart” is their vision. CEO Iacopo Carreras discusses how his platform captures large volumes of very precise location data, which is used for operational advantage and to unlock insights into behavior. Problems with flow, layout, engagement, and sales can all be uncovered when looking at the complete buyers’ journey in the store, starting form when they pick up their basket, till after their purchase is made. The benefits extend beyond category management, product placement, and asset optimization. With Bluetooth technology integrated into the infrastructure of the building, wayfinding, interactive shopping lists, and proximity marketing all become available to anyone with a smartphone.
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Steve Statler 0:17
Welcome to the Mr. Beacon podcast. This episode we are in Finland in Helsinki at the Cooper partner event. And I am delighted to have with me Iacopo Carreras who is the CEO of ThinkInside, Iacopo. Oh, thanks very much for spending some time with us.
Iacopo Carreras 0:36
Thank you, Steve. It's a pleasure to be here.
Steve Statler 0:38
Yeah. Well, we spend quite a bit of time together, haven't we? We have, we have a joint customer that's in common. I was a consultant for Japanese car parts manufacturer, we were looking at RTLs solutions. And you guys were one of actually good dozen companies that bid and one. So I've done an amazing job. So let's let's explain a bit about your business and what you do. And then we can we'll we'll be talking about maybe a little bit about the manufacturing side, but I think retail is probably the theme for the day. So So what does think inside do?
Iacopo Carreras 1:12
Yeah, so thinking side is, is specialize in, let me say location intelligence, which in other words, means creating value out of location data streams. So we work with various and different RTLs technologies, although actually we have been specializing with Coupa. That's also one of the reasons why we're here. And, and our solution takes an RTLs data stream. And then in transforms this into into knowledge on what is actually happening in a given physical space. So you mentioned retail is an example. So in the retail sector, our solution is about understanding customers behavior, we track how the shopping carts and baskets are moving, we analyze all this data with our platform. And we transform this into knowledge on you know, what people do, where the shop, where they don't shop, where they stop for how long? And there's a number of BI tools that we use to deliver value out of this of these data streams.
Steve Statler 2:09
Okay. So just explain to me a bit about the demarcation between what Cooper does, and what think inside does, because we've had Cooper on before and we know they do angle of arrival. And it's very accurate and very good for real time. What where does that platform stop? And where does your start?
Iacopo Carreras 2:32
Yeah, so cool. By altering the way they used to present themselves, they provide a.on, the map, a very good.on, the map, but that's actually where Cooper stops. So there's no, there's no data being stored by Koopa, there's no data being processed, there's no semantic on that given.on, the map is just an xy coordinate. And that's it. So we take it from there, we provide a semantic to this dot, it's a cart or is like an asset in a in a manufacturing plant. We, we process this tag this information from one asset from many different assets. And then with our tools and technology, we extract knowledge and value out of this data. So we take a lot of a massive number of data points from from Koopa. And we make sense of it. This is actually what we do and what we are specialized in. Okay.
Steve Statler 3:25
So what they're doing is essentially horizontal, and what you're doing is quite vertically orientated. I'm sure there's some Yeah, and reuse across. Right, let's tweeze the latter part. So So what are the so you do work in manufacturing? And you do work in retail? What are the things that are common to those? Right?
Iacopo Carreras 3:46
This is a very good question. And, you know, upfront, sometimes it creates confusion, because you know, we're working in different sectors. But you know, historically we started from from retail that actually where is where we have been building our technology. And in retail is pretty much the case I was describing before it's about customers behavior. And it's about analyzing what what shoppers do in a physical in a grocery store. By chance, actually, we ended up in a given opportunity in in the manufacturing industry. And while while working on that opportunity, we realize that the underlying infrastructure, which is the platform that is processing, all this data and big volumes, and providing a number of services is pretty much the same. So of course what is changing is the application that is running on top of it, which means the application that is consuming the data that is being processed, and is associating a given semantic interpretation to that given data. But the then the line core engine is pretty much shared across these different verticals. And this is also the reason how we actually are addressing different markets and different market needs. The platform is pretty much shared. This is probably the key IPR of our company where we have been investing a lot of work in unilateral efforts. It's it's a, it's a big data platform that it's receiving large volumes of location data streams. It provides a number of tools and services for associating a semantic on this data. It provides a number of techniques and algorithms to kind of segment these data, different tracking traffic strategies, and in many different things, eventually provide a rich set of API's, through which applications can pull the data and visualize the data and give specific interpretation to this data.
Steve Statler 5:33
So what are some examples of the functionality that you offer to both of those application areas that is beyond what is offered by by Cooper? So I'm thinking you know, on one hand, you're tracking shopping carts. On and the other hand, you're tracking pallets, what what are some of the things that you would want to do that are the same for a shopping cart in a, in a pallet?
Iacopo Carreras 6:07
Well, so there's a number of real time services that are kind of shared to make an example, you want to know how many people are shopping in the store, or how many people are waiting at the checkout areas, or how many people are waiting at the serve area. On the retail space, if you move to a smart manufacturing plant, you want to know how many pallets are waiting in a given deposit, or how many trolleys are being queuing up on front of a high temperature oven. So there's, there's a number of real time services that are kind of shared. And it's mostly about, you know, interacting in real time, this realization of, you know, a status of the system. You can have different asset types and many different visualization that you can actually embed and create one thing. Another thing is really about analytics. If it's about the retail case, you know, it's about measuring the dwell time of customers in the store and the different areas and you know, making sense of this, of course, there's a number of things that are specific to the retail, so you want to know and analyze the sequence according to which you go across these different areas. And you want to measure how much time are you spending in one area compared to another and then you might also have some sort of hierarchical organization of areas. This is pretty much specific to retail. But if you look at from the location analytics point of view, it's about different ways of handling dwell time in the store. If you shift this, on the Smart Manufacturing, it's again, how much time does a given trolley takes for moving from production phase one to production, phase two, and how long is basically waiting before it gets into the fouling phase and how much is waiting in this given deposit? Also, there, there's a lot of value on you know, analyzing, you know, dwell time, dwell time is, is a key is a key element on our platform. So as you can see this, there's many things that are kind of shared, at least at the core level, but then, of course, bring to a different semantic and a different repetition in use when you move it to the application one got it.
Steve Statler 8:08
Last question about manufacturing, and then we'll focus on retail, as I promised. So with what is the ROI for a manufacturer in putting the your system in place? How do they justify it to the CFO?
Iacopo Carreras 8:23
This is this is this is very good question is typically the most recurring question. And I guess you also have some idea, since you also have NDK, in building these areas, but there's many, many different variables depending on use case. So if it's if it's like the specific project that we have been working together in this mainly labor cost, so that given client actually, they ended up in a in an industrial plant that was not designed to handle this, this kind of production load. And this ended up in these 1000s of pallets being deployed anywhere in the industrial plant, with a lot of effort in just searching pilots. And this was a problem to the point that there was one person, at least two person dedicated just to this 24/7 Every single day of the year. So in that case, the possibility to simply save these labor costs, actually was was good enough to justify an ROI over three years. And then the interesting thing is, and this is something kind of shared across many different projects, is the fact that once you realize you have an infrastructure there and you realize that your your minimum ROI is being met in a way, then you also realize that there are so many other things you can actually do on top of that single infrastructure. So So you start tracking pilots, but then you realize okay, but then I can also track forklifts and then maybe I can prove safety and then I can also track and manage vehicles moving around and so I can save time when I need to find them for for the maintainer. So, the thing is that you start with an idea of an ROI over three years, then you realize that when you sum up There are many different things that come to your mind while running the project probably what used to be three years, then it's kind of reduced to maybe a year and a half. Yeah, less,
Steve Statler 10:09
I think is fascinating. Some of these concepts we associate with outdoor location services, like geofencing have applications in a manufacturing plant, can you? I'm going to ask more questions about manufacturing, why would you want to geofence in a factory?
Iacopo Carreras 10:27
Well, there's there's many different reasons. The thing is that manufacturing processes are pretty complex. And, and you need to make sure that things happen, or the way they are designed to happen. And many times since this is there's a lot of labor, manual labor involved, this is always not met. So to make an example, in this specific case, actually, they have some semi finished goods that are moving from from one phase to second phase, and they cannot go back. Because if they, if they go back, this, these goods are being contaminated, and they need to be thrown away. And the thing is that this this specific products, it takes quite some time actually to go from one phase to another and to another and to another, which means that if you throw away a product, which actually is at the very end of the process, you have been throwing away something like weeks of work. And this is a problem, it's a problem, because there's this time wasted this product wasted. And maybe you're actually dealing an order that you're not able to deliver in so it's a problem. So what you can do in this case, of course, is you have you have location you have you're tracking pilots, and you can know and alert if a pilot is basically going in the wrong direction. And he's basically going into a contamination area. And if that's the case, then you can easily raise an alarm, and, you know, make sure that it gets blocked beforehand. This is a simple example. But there's many others that you can think of simply like another example is like workers, maybe working in a in a dangerous zone, maybe you want to make sure that they do not stay more than 20 minutes that given zone before leaving. And if you know, the work is there for more than 20 minutes, you raise an alarm? Yeah, the thing is that everything is something that in a way you can manage somehow, maybe with a lot of attention a lot of procedures. The point is that when it comes to humans, you know, you have to do a given task, you have to do it on time. And many times you just don't care, just do it. And you realize afterwards that you have gone through a serious risk, having a system that actually supports you, and provide you all the necessary services and tools for making this automatic in a way is what you know, geofencing and you know, location awareness and location based services is about and I believe this is a really great value for manufacturing companies, it can really help them to save a lot of money, provide a better place to work for workers, and improve a lot of efficiency and many other things happening there.
Steve Statler 12:56
Yeah, I remember hearing about a use case from one of the company big companies that makes fizzy drinks, and who would have thought it but they actually have dangerous chemicals involved in this. And so there was an issue with wanting to make sure that only people that had been trained, could be at a certain station. And so they could put a geofence and say, Oh, Jesse can work there. But Fred can't. And I want to know if Fred goes into an area that
Iacopo Carreras 13:24
This reminds me also about another use case that we have been working on in this was like large food manufacturing company. And they're we're also working with chemicals. Their major concern was to properly handle the some emergency situation that might happen in the industrial plant, which means if something happens, I want to know, in real time, where are the workers, how many of them are still in industrial plant, and if that's the case where they are. So they really want to have a way to better support emergency situations. And of course, this has many implications, of course, you can really save lives. This is the first thing if you do things right, you can also have benefits in terms of insurance costs, you reduce basically your liability and many different things. So there's many different things involved in this. And many different benefits. First of all, you saved lives and people. That's the the key one. And from the financial standpoint, you can also you know, get better conditions and yeah, and kind of Yeah, it's so yeah.
Steve Statler 14:23
Okay, let's try and keep our promises. Let's talk about retail. What first of all, just the the nuts and bolts of that. What are you actually tracking in a retail store? You have these Cooper locators? Are you sticking tags on people as they come through the door? Or what's the
Iacopo Carreras 14:40
No no, that's not the case. It's a bit too much though. So now our basically gateway to people are the shopping carts in basket. Okay, so this is actually what we're tracking. So we place some small tags on the shopping carts and baskets in the grocery store. And then with the coupon locators and coupon technology we are able to fully read construct the shopping journey of customers. The accuracy of COPPA as it's pretty good, it's pretty good. So we're in the order of half a meter in a wheel setting, and even large deployments. So it's like, perfect, because in a grocery store, of course, the important thing is to distinguish whether you are in a given URL or another. And that's the key point for getting some value out of those kinds of settings.
Steve Statler 15:23
Some of these aisles can be big, you being asked what side of the aisle someone's on, because that can make a difference on one side, you might have spaghetti, and on the other side, you could have bread, you know,
Iacopo Carreras 15:36
Yeah, this, this really a lot of analysis you can actually do on this, especially because given the accuracy of the data we get, we're also able to link when, of course data is available, we're also able to link the shopping journey of customers with sales data, which means that we know where the customer was, we know what the customer was buying. And even more importantly, we know what the customer was not buying.
Steve Statler 15:59
So they dwelled in spaghetti, and then they didn't buy it. Yeah, is useful information is
Iacopo Carreras 16:04
Useful and is completely unknown information. To retailer, as of today, the best they can do is to analyze sales, but sales is just about what people are buying, but there's no clue and no information about what people are not buying inside the supermarket.
Steve Statler 16:19
Well, one, kind of not a technical question, but it's a fundamental question is, our retailers ready to use this information? I mean, you can provide some amazing insights. But you know, if this guy is just trying to focus on making sure that enough people have turned up for their shift, then maybe it's not gonna be able to use it. And that's a, that's a problem in terms of getting the value that justifies the investment.
Iacopo Carreras 16:46
So this is, this is a very good question that there's no, there's no straight answer, meaning that there's this few pioneers in the space that are really experimenting in through experimenting, so what they're trying to do is really, you know, to kind of bring the online concepts into the physical world. Some of them are even, you know, implementing, you know, a B testing with different stores, compare one with the other, and pretty much pretty advanced. In general, the feeling that I have, you know, we have been, you know, fighting in this area for quite a few years now, the perception that I have is that actually, it's it's getting mature is not yet mature, but is getting mature. So there's more and more interest in this kind of technology, there's a lot of pressure from the market, it's not just about the online, there's also the you know, big players investing a lot of money in technology. And, and, you know, those kind of big supermarkets that that kind of are, you know, doing the same, or doing the same thing for, you know, have been doing the same thing for for many different years in that pretty much in the same way, and they're feeling a lot of pressure. So they need to invest in technology, they need to start changing, they need really to introduce a new way of running their business. We have all seen, you know, the, you know, the Amazon Go store, you know, very innovative and futuristic concept on how you know, shopping might look like in the future. I believe there's still some time before this really gets into like, large supermarkets, but you know, it will come and and, you know, big, big players also start to feel this and start to feel that they need to change and to do something.
Steve Statler 18:15
So, can you elaborate on? What's the role of the people that are using the information that you're providing? And what what are some of the insights that you're providing to those people? I mean, you've got, I'll let you flesh out who's who in the retail world? Yeah.
Iacopo Carreras 18:33
So in general, you know, we were kind of really modeling in depth, the funnel. So you know, in a given store, you know, how many people are getting in, you know, let's take in as an example, a specific Ale, you know, that is of interest for that given given store, because maybe it's not performing well, for that
Steve Statler 18:49
It's just for some reason, the sales are not good, right?
Iacopo Carreras 18:52
Right. So for that, given L, what do we say is like you, we model the funnel, so we know how many people are getting into the ale, how many of them are being engaged, meaning, you know, stopping there for more than 15 to 22nd, or just going straight? How many of them are buying. And depending on where the funnel, you know, goes goes down, you can decide to plan specific interventions for the given URL. If it's about traffic, people not getting there. So you really need to make sure that the layout is designed in a way that the flows of people eventually reach that given spot on the store. And so you might think of reorganizing the overall layout of the store, at least part of it. If it's about engagement, then you might think of maybe adding some promise there, you really need to slow down the traffic in that given URL. And you can do this with promise maybe putting some very interesting products, maybe some interesting brands, maybe some interesting brands at the beginning of the sale. There are many things that you know, people specialize in merchandising can think of or slowing down the traffic there. And if it's about sales, so people are getting there, they're stopping but they're not buying probably the products of dead given category or not the one that that specific target is, is interested in. And this could be, as an example, a problem of the demographics, maybe the store, the shoppers in that predicament stores are mostly students. And maybe the products that you're displaying in that given URL is a bit too high end for that given, you know, specific social demographic segments. So you might want to think, to sell maybe still passed out, but have a different brand, a cheaper brand, or, or something like this. And depending on how the funnel is kind of evolving, they're given a URL, you can plan specific interventions. And then of course, if you really want to do things, right, it's really about you know, implementing the intervention measuring the effect exactly in the same way you do the online and then, you know, keep on improving and improving.
Steve Statler 20:45
Yeah, we talk a bit about this in the beacon technologies, but but I just love what you've described, because it's really taking the concepts that have been made possible in the online world, the funnel and, and looking at click path, and you're just mapping it on to the physical world, and you're really doing it. That's cool.
Iacopo Carreras 21:06
That's exactly the objective. Its objective, or our technology, our solution, we still need to work hard to, you know, sell, send this message across the retailers, because this is something they can really do is not something that, you know, they can think of this is something that nowadays, if they want to do it, they can actually do it.
Steve Statler 21:25
So let's wrap up and come back to the question that I asked about manufacturing will apply to retail, where is the ROI? You've described some of the the opportunities and advantages and tools that you can give? Where are you seeing the biggest payback?
Iacopo Carreras 21:42
So I think also here applies the same concept that I was describing before, meaning that, you know, once you have the infrastructure there, the number of installed services that you can deliver are kind of endless. So, to me, the the great promise of ROI in retail is if you really think in large on the how the many different things you can actually do with this kind of infrastructure and technologies. So we have been talking about ROI in terms of, you know, category management in terms of product placement, but this is just one single service. So what you could potentially do, if you think about asset optimization, so it can help retailers in identifying the optimal number of shopping carts and baskets, even this simple use case actually can have a major impact in terms of ROI, you can just, you know, move, I don't know 20% of your baskets, or cards in a different store, because no one is using them, they're just enough. Or you can simply think of, you know, just replacing those shopping baskets, because they're the most used asset, but they're too small for for your store. And simply replacing, you know, baskets, and with bigger ones might provide already a lot of value for a store. So the thing is that even simple things, but based on data, because this is actually the main difference, you know, retailers usually have store managers that have very important intuitions. But the thing is that if you see things on data, this makes a difference. So coming back to this example of the of the basket, actually, it's a real case. So there was a store. And of course, there was like the intuition that people were using more baskets than cars. But with data, you actually demonstrated that, you know, 75% of the people were using more baskets than data. So it's, it's not just more baskets, it's almost all the people are using a basket. And the basket was very small. By simply replacing the basket with the bigger one actually, this led to some interesting that
Steve Statler 23:38
that's really pretty funny because they retailers describe, you know, how much you buy as basket size. And the problem with getting the basket size was the basket is too small. basket was too small. Okay, so you can lift sales, that's good news.
Iacopo Carreras 23:55
But there are many other services, you know, be exploiting the Cooper infrastructure is based on Bluetooth. So of course, there's any kind of service you can deliver on a smartphone that can be location me. So it's about it's about Wayfinding. It's about shopping list. It's about of course, proximity marketing. But in this case, it gets really personalized, I can really send you a message because you're for 20 seconds on front of the pastor. And that's where I'm going to trigger interaction. So it's very personalized and very contextualized. In a similar way, I can think of many different operational services for the store. We know that planograms actually get pretty rapidly outdated in terms of where the products are. By integrating locations in you know, in the many different operations happening the store, I can help you to keep the planogram updated.
Steve Statler 24:41
So the planogram is the the map that the merchandiser uses to specify. It's not the store manager's decision necessarily, to where they're going to put Cheerios versus Froot Loops. It'll be in the planogram. Yeah, exactly. And that's really developed by someone in headquarters.
Iacopo Carreras 25:00
Usually that's the case. But usually it's also the case that, you know, after some time, you know, there's a misalignment on how the, the store is truly organized in terms of product placement, and what used to be the very beginning.
Steve Statler 25:13
And so how can you help solve that problem,
Iacopo Carreras 25:15
Because as an example, there's a number of operations in terms of refill that are done on a daily basis. And when you refer you do the scan operation, you have a scanning device that you scan a product, you see how many of these products are available in stock, and then in case you do and reordering? Now, if this operation is annotated with location, you know that that given that given spot
Steve Statler 25:36
So you put a tag on the on the barcode reader
Iacopo Carreras 25:39
You put a tag or now modern barcode readers are Android devices. And you can think of really integrating that, given the vibe
Steve Statler 25:46
Could just broadcast the Cooper payload. And you know where it is? Yeah, very cool. So one more thing that just occurs to me. So you're working with some extremely large retailers who are very sophisticated. They're not new on the block, they've looked at all the other technologies that are out there, why Bluetooth angle of arrival versus, you know, machine vision, you've got these security cameras, they can produce heat maps, and that sort of thing. What's the so well, it's the advantage of Bluetooth?
Iacopo Carreras 26:21
The advantage of Bluetooth is, well, we know the precision half a meter and less, even over big areas, it's real time. So you can actually get this data with real time in in one second latency. This actually is kind of, you know, different from from video and these kind of things. It's Bluetooth, meaning that you know, you can track assets, but you can track smartphones, and you can think of, you know, in the future also to create interactive services over smartphones. So this is a very rich technology that provides a number of features that it's allows you to really expand from analytics to location based services to customers and into many different things. So in a way for for grocery stores, I believe is really the optimal one, meaning that it's the one that provides all you need now and probably also in the in the in the future.
Steve Statler 27:12
Yeah, you were talking earlier about linking point of sale data with a specific cart. So you're really able to tie those things closely. Yeah. And that's a big deal if you're doing that integration, and that shows that the retailer has got some belief in this because it's typically a nightmare to integrate with the point of sale system and work for you guys to
Iacopo Carreras 27:37
No, I'd say it's great. Yeah, it's a it's a nightmare, and typically are not information that they share so freely. At the same time, that's actually when you you truly get, you know, most of the value out of the system, because then you really map very precisely the complete shopping journey of a customer, you know exactly everything on the customer where it was, you know, where stopping what buying what not buy, that's actually when you get most of the value. Otherwise, of course, you have to two data sources, you know, sales and foot traffic, they don't talk to each other. And, of course, you get some value from both but you know, something athletes, it's not the sum of the two, it's more than that.
Steve Statler 28:13
Well, the thing I love about the approach to taking is you unlike trying to get people to download an app, you catch almost everyone that's shopping in the store, most people either have the bar, the cart or the basket. And that's always been the thing that's, you know, we have to have this act of faith. Yeah, of course, they're gonna download the app. And then they never
Iacopo Carreras 28:34
They did this very good point. That's also one of the reasons why we believe this technology is very useful. Meaning that you know, even after one week, you really have 1000s, of shopping journeys of customers already, after one week, you have a lot of evidence on what is actually happening there. Because I mean, in a supermarket of at least 2000 square meters, everyone is using either a cart or a basket for shopping, there's just a small portion that are, you know, maybe buying, you know, bread, and maybe just a few things and just leave, but those notes are not really the one that you know, they're targeting in a win in this kind of analysis. And so this is a very good point, because from the zero, you start to collect plenty of information.
Steve Statler 29:11
So how many beacons you need to cover a big grocery store then? So for all those cars
Iacopo Carreras 29:17
yeah, it's, it's not too many. Also, of course, you know, it's angle of arrival. So there's a bit of variables including the height of the ceiling, but to give you an idea, so if we take like, you know, a medium sized supermarket in Europe, like 2500 square meters, maybe 3000, then you will need something like 2025 antenna to get it all. Okay. And then of course, you have one tag for a single cart and trolley. So it might be you know, three 400 of them.
Steve Statler 29:42
Okay, so that's, that's pretty modest. And what about batteries? And I mean, that's, I know with our manufacturing customer, that was a major concern. And tell us, give us a little bit of insight about how you solve the battery life problem.
Iacopo Carreras 29:58
So nowadays, the There's a lot of things happening the Bluetooth Low Energy. And there's a lot of advancements especially also on the chipsets of radio with the new Nordic and so on. So now it's even, there is more tags actually can reach can easily reach like a lifetime of three to four years. And this is pretty much acceptable for like a retailer. So for a retailer, usually the, the minimum they actually are looking for is like three years, this kind of, you know, the time range for also planning maintenance operations and be below that kind of threshold might look a little bit too much in terms of investments. Three years is okay, and three years actually is acceptable and unreachable with today's Bluetooth Low Energy tags, especially the ones like Cooper, you have an accelerometer. So if the car is not moving, the tag goes to sleep. And you can define many different strategies in order to properly preserve preserve the battery. And in general, you can, you can always think of embedding a tag in the handle of the of the car. In that case, you have plenty of space. And if you think of using alkaline batteries, then you can easily reach five to six years without problems.
Steve Statler 31:04
Very cool. So we've covered a lot we've talked about ROI strategy and different markets and technology. It's been fascinating. Iacopo both thanks very much.
Iacopo Carreras 31:15
Thank you, Steve was a pleasure.
Steve Statler 31:16
So the most important question and the hardest one is, what three songs would you take on a journey?
Iacopo Carreras 31:28
Okay, so that's the first one.
Steve Statler 31:31
Yeah, it seems like it comes at the end. But actually, it's a warm up question. And I'm not sure it's supposed to relax us, but I think it's probably does the opposite. Oh, my God, I have no idea.
Iacopo Carreras 31:42
No, so I was thinking about three songs. usually associate songs to moments or you know, something that is worth remembering. So the first song I was thinking to bring with me, Maurice is basically another brick in the wall from Pink Floyd was like, you know, the 90s and he remembers Me or some good time ahead, and a lot of things changing. And so that's the first song.
Steve Statler 32:07
So what were you doing at that time in your life? Where were you
Iacopo Carreras 32:10
I was basically a high school and high school and also very energetic period, a lot of things happening and started traveling to see the world and things like that. So it was a there was a kind of the the kind of song I associated that period of my of my life. Yeah, three parts of your kind of that was also part of this. Yeah. Memories. Okay, so this is the first one. The second one is a Beautiful Day from U2 You choose one of my favorite bands,
Steve Statler 32:40
And amazing life. Have you seen them?
Iacopo Carreras 32:42
Yeah. Yeah. I see them last year in Rome. In it was a fantastic concert. So it was super cool. Yeah. And a lot of it was in the Olympic Stadium in Rome. So it was it was really fantastic. Super Show. And so beautiful. My day is one of my favorite songs of the band. And then I was thinking of you know, I seen some Italian I cannot just bring with me, you know, English songs. So I need to come up with some Italian song and then and so the third song I will bring with me is like Donna canonet from Virginia. He's one of the most popular outers in Italy. And it's, it's like any, you know, it's just about also Italian traditions and a bit of Italy as it used to be. Especially in these days.
Steve Statler 33:33
Yeah. So whereabouts in Italy. Do you live?
Iacopo Carreras 33:37
I live in the actually I'm originally from Tuscany from Pisa. Although I moved to Trento since 15 years now. So now currently, I live in Trento, which is very nice region and the very north of Italy is kind of a mountaineering region. It's a perfect spot spot for outdoors and for enjoying you know, the the outdoor environment and sports and all these kinds of activities. Yeah.
Steve Statler 34:01
Beautiful. Very good. Thanks very much.