>> live from London, England. It's the queue covering a ws summat. London twenty nineteen, Brought to you by Amazon Web services, >> come to the A. W s summit in London's Excel Center. I'm Susanna Street, and David Aunty is my co host today on the Cube. This means so much to talk about here at the summit today to do with machine learning and a I. And I'm really pleased to say that we have to really key people here to discuss this. We've got time. Tom Summerfield, who is head off commerce, a foot asylum on also Richard Potter, who is the CEO of Peak. Now you guys have really formed a partnership. Haven't you put asylum? Is a leisure wear really? Retailer started in bricks and mortar stores. Really moved online on Peak is a pioneer for artificial intelligence. System's really well to get together. What What sparked? Really your demands. Ready for their services, Tom. >> Yeah, well, so way knew that we needed to be doing something with data on A and we didn't really know exactly what it would be Way were interested in personalization, but then also in a bigger picture, like a wider digital transformation piece for the business where well established bricks, a martyr business, but then a fast growing online business. And we're interested to know how way could harness the momentum of the stores to help the digital side of the business and also vice versa. On we thought data would be the key, and we ended up having a conversation with the guys at Peak, and that's exactly what we've been able to do. Actually, on the back of that deliver, we're delivering a hyper personal experience for our consumers Now. >> I was one of the statue that I notice when looking into what you be doing, a twenty percent increase in email revenue. So that's quite remarkable, Really. So Richard, tell us, you know how you're able to do this? What kind of services that you lean on? T make those kind of result. >> It's a combination of a lot of things, really. You know, you obviously need people who know what they're doing from a returning a business perspective. Married with technical experts, data science algorithms, data. Um, I think specifically how we've done it is a pig's built, a fairly unique A I system that becomes almost like the central brain within our customers. Businesses on off that algorithms help automate certain business processes and deliver tangible uplifts in business performance like the twenty eight percent uplifting sales here, Um, in order to do it. So it's quite a long journey, I suppose. The outlook we took when we started collaborating was was that if we could deliver that hyper personalized shopping experience, we were always going to be ableto show customers the right product at the right time. And if we were doing that that we would lead Toa High brand engagement, higher loyalty higher on higher lifetime values of customers. And that's and that's what's shown to be the case in silent example. >> Yeah, definitely that echo that. You know that the high profits hypothesis wass If you can show the right custom of the right product at the right time, then their purchase frequency average order Volumetrics all start to move positively and ultimately than affecting their long term engagement with our brand, which increases revenue on also delivers a more, you know, a frictionless consumer experience, hopefully for the customer, >> because I suppose your experience is the same. So many companies out there they're sitting on this huge pile of data, yet they don't know how to best optimize that data. When did you first realize, Richard that there was this kind of gap in the market for Pete to grow? >> Yeah, I think data and analytics have come on a bit of a journey away from common sense reporting tio more advanced analytics. But when you get a I and machine learning what you're talking about, his algorithms being our self learning make predictions about things that actually fundamentally changes the way businesses can operate on DH. And in this case, a great example is you know, we're sending hyper personalized marketing communications, Teo, every single for silent customer. They don't realize necessarily that they are tailored to them, but they just become more relevant. But it doesn't require a digital marketing to create every single one of those campaigns or emails and even trigger the sending of those materials. Brain takes care of that. It can automate it. And what the marketer needs to do is it's faded, engaging content and set up digital campaigns. And then and then and then you're left with this capability where eyes saying you might be a market for this product. Let's let's send you something that might appeal to you on DH that just gives that gives a marketing team scale. And then, as we move into other use cases like in the supply chain for film and delivery of product the same thing the team's just get huge scale out of letting algorithms do those things for them. Andi, I suppose the realization for us that there was that gap in the market was just that you can see the out performance of certain cos you can see that Amazon attributes five percent of their sales to their machine learning recommendation systems. I think Netflix says eighty five percent of all content is consumed >> because it's Al Burns. Andi. Companies >> like that can harness machine learning to such a great degree. How does how did howto other businesses do it? Who can't access that talent pool of Silicon Valley or along the global? You know, the global talent leaders in tech and that's that's where we had the insight that his peak way could create a company that gave our custom is that that technology and that capability Teo deliver that same kind results that the Amazon and Netflix >> so before the Internet brand's had all the power you could price however you wanted if you overprice, nobody even even knew. And the Internet was sort of like the revenge of the consumer. Aye, aye, And data now gives the brands the ability to learn more about its customers. But you have to be somewhat careful, don't you? Because their privacy concerns obviously DPR etcetera. So you have to have a value proposition for the customer, as you were saying, which they made are you know that machine is providing these offers, but they get value out of it. So how do you guys think about that in terms of experience for the customer? And how do you draw that balance? >> I think from my angle, that Richard touch on a couple of bits there to do it scale first and foremost across the entire alarm on Thai network of consumers is killer element to it. But to deliver that personal experience, I think consumers nowadays are so they're more expectant of this. Really. We would have considered it innovation a couple of years ago, but now actually it's expected, I think, from the consumer. So it's actually in the name ofthe You have to move forward to stand still. So but way think where we're right at the front of this at the moment. And we're really looking now how we optimize the journey for the consumer so that actually we know if we're from some transactional data that we have in a little bit of over behavioral data that, you know, we're really conscious of the whole GDP, our peace and stuff, and that's really, really relevant and super important. Andi, I'm pleased to say that you know, we have that. We know that by a peek, it's completely on lock down from that perspective as >> well. Where did the data's where the data source of comfort. You mentioned some transaction data. Where is the other day to come from using show social data and behavioral data? Where does that come? >> So those elements of social data, some of it is a little bit black box. You can't always access it, and that's a GDP, our peace there, and rightly so. Actually, in some cases we have a loyalty scheme which allows us to understand our Kashima's better in our bricks and mortar retail, which is really cool that we've got some of that transactional data on a customer level from the stars. We know that some people in our sector maybe don't have that, so that so that allows us to complete sort of single customer view, which then we can aggregate in peaks brain, then transaction data on the website in the app and bits off browsing, you know, just within our own network. You know where customs potentially being and reacted with somethin. A piece of content. Janet within the website, that's that's how we build that view. >> Do you think this is the way that more bricks and more two stores Khun survive? Because so many are closing in high streets up down the UK and in other countries because simply they're not really delivering what the customer wants? >> Yeah, I think so. We rich now. Both feel quite strongly now that wear so onto this now a little bit. It's a really As as our relationship for the two businesses has evolved, it's become clearer and clearer that actually we've armed with this. You know this data, our fingertips, we can actually breathe fresh life into the stores, and it's in the eye of proper true Omnichannel retailing way. Don't mind where the cost consumer spends the money. We just need to be always on in a connected environment so that A Z said before pushing the right product at the right time. And when they're when they're in market, we turn up the mark the message a little bit. But then understanding when they're not in market and maybe to back off him and maybe we warn them what with a little bit of a different type of message then and actually we're trapped with one challenge ourselves to send but less better marketing communications to our consumers. But absolutely that store piece is now, so we tail back. Our store opening strategy is a business to focus more on the digital side of things, but now it's possible that way might open some more stores now, but it will be with a more reform strategy of wet, wet where, why we need to do that? >> Isn't this ironic? The brick and mortar marketplaces getting disrupted by online retailers, obviously Amazons, that big whale in the marketplace, and your answer to that is to use Amazons, cloud services and artificial intelligence to pave the way for your future. Yeah, I mean, that's astounding when you think about >> me. Yeah, this sort of unified commerce approach, Tio, you know, there's a place in the world for shops. It's like it's not Romance isn't completely dead and going shopping. It turns out, you know so on. Actually, yeah, we're using honesty in the eight of us, but we'LL hire our friends at Peak. Yeah, it's it's some irony there. I think it's really cool. >> And that decision that you made obviously wasn't made made lightly. But you saw the advantages of working with the clouds outweighing the potential trade offs of competition. >> Yeah, I mean, that's not that was never really, really no, I'm certainly not know. I think this is something that is happening, that data, and on harnessing it in a safe, responsible, effective way, I believe, is the future of all commerce. So >> that as far as security is concerned because, of course, we have had data breaches your customers, credit card details, access. How do you ensure that it's as secure as possible in the way that you you you choose the services I think >> that come that just comes down to best practice infrastructure on the way we look at it, a peak is there's no bear tools in the world to do that, then the same technologies that Amazon themselves use. It's to do with how you configure those services until ls to make it secure, you know, And if you have an unsecure open database on a public network, of course that's not secure. But you could have the same thing in your own infrastructure, and it wouldn't be secure. So I think the way we look at it is exactly the same thing on actually, being in the Amazon prime for us gives us a greater comfort, particularly in terms of co location of date centers and like making sure that our application fails over into different locations. It gives us infrastructure we couldn't afford otherwise, and then on top of that, we get all these extra pieces of technology that can make us even more secure than we could do. Otherwise we'd have to wait, have to employ an army of infrastructure engineers, and we don't have to do that because we run on Yes. >> Okay, so we were able to eliminate all that heavy lifting. That same goes. You've got this corpus of data. I'm interested in how long it took to get through. A POC trained the models how much data science was involved. How much of a heavy lift was that? Yeah, well, I think for >> us we better be pretty rapid. Actually, we started working together in January last year, so we're only just sort of year into that. >> And in that faith in that entire >> sofa length of of our relationship, we've gone from high for personalizing digital campaigns to recommendation systems on a website to now optimizing customer acquisition on social media and then finally into the supply chain and optimizing demand and so on it. And I think there's >> a lot of reasons >> why we've been able to do it quickly. But that's fundamental to the technologies that the peak is built. There's two. There's two sides to it. Our technologies cut out a lot of the friction so way didn't run a proof of concept. We were able to just pick it up, run with it and deliver value. And that's to do with I think, the product that peak is built. But then you obviously need a a customer who's who's going on a transformation journey and is hungry to make that make that stick in London on. Then when the two come together, >> I think that it's an interesting point that, though, because while suite for asylum, we always I always say it's that we're not. We're not massive, but we're not tiny, but it's the sort place you Khun turn upon a Monday and say, I've had an idea about something and we're not doing it by Friday. That's That's a nice, agile culture. It can create some drama as well. Possibly. I think it's really straightforward to get straight into it. And I think this is where some of the bigger, um, sleepier high street retailers that Amar, fixed in a in a brick from our world, needs to not be too afraid to come out and start embracing it, because I think some of them are trying now. I think it might be a little bit late for some now, but it's just it's just it just wasn't that hard really to get going >> and you've seen the business results, can you share any measurements? or quantification. We've >> got a really a really good one that we're just talking about at the moment. Actually, Way were able to use segmentation tools within within the peak brain Teo to use them on Social than Teo. Create lookalike audiences. So Facebook Custom tools, Right? We'LL help you create audiences that it thinks you're the right buyer. It's complex algorithms itself, but we almost took a leap ahead of their algorithms by fire, our algorithms uploading our own segments to create a more sophisticated lookalike audience. We produced a row US results or return on that spend. People are not familiar with that of eight thousand four hundred percent, which Wei would normally be happy as a business, we've sort of seven, eight hundred percent. If you're running that that we've say on AdWords campaign or something like that, that's quite efficient campaign. So it's at zero. We were a bit like it felt like it's a mistake that, you >> know that is >> not the right, >> Yeah, but not so that's super cool. And that's really that's really opened our eyes to the potential of punishing that the, you know, our sort of piquet I brain to then bring it onto Social on. Do more outward. Advertise on there. >> So moving the goal post meant that your teeth are really high school. Thank you. Thank you very much for telling us all about that time someone feels on which floor. Sir. Thank you for joining me and David Auntie here at the eight of US Summit in London. Merchant to come on the King.

>> live from London, England. Q. Covering A Ws summat. London twenty nineteen Brought to you by Amazon Web services >> to the A. W s Summit in London's Excel Center home. Susanna Street and David is my co host today on the Cube. They mean so much to talk about here at the summit today to do with machine learning an A I and I'm really pleased to say that we have to really key people here to discuss this. But we've got some Tom Summerfield who is head off commerce, a foot asylum on also Richard Potter, who is the CEO of Peak. Now, you guys have really formed a partnership, haven't you? Foots asylum is a leisure wear really. Retailer started in bricks and mortar stores. Really moved online on Peak has been a pioneer for artificial intelligence systems really well to get together. What what comes? Sparked Really your demands ready for their services, Tom? >> Yeah, well, so way knew that we needed to be doing something with data on A and we didn't really know exactly what it would be way were interested in personalization, but then also in a bigger picture, like a wider digital transformation piece for the business where well established bricks, a martyr business but a fast grow in online business. And we're interested to know how we could harness the momentum of the stores to help the digital side of the business and also vice versa. On we thought data would be the key, and we ended up having a conversation with the guys at Peak, and that's exactly what we've been able to do. Actually, on the back of that deliver, we're delivering a hyper personal experience for our consumers Now. >> I was one of the statue that I notice when looking into what you be doing, a twenty percent increase in email revenue. So that's quite remarkable, really. So Richard, tell us you how you're able to do this. What kind of services that you lean on? T make those kind of result. >> It's a combination of a lot of things, really. You know, you obviously need people who know what they're doing from a returning a business perspective. Married with technical experts, data science algorithms, data, I think specifically how we've done is picks built a fairly unique A I system that becomes almost like the central brain within our customers businesses on off that algorithms help automate certain business processes and deliver tangible uplifts in business performance like the twenty eight percent up lift in sales here, Um, in order to do it. So it's quite a long journey, I suppose. The outlook we took when we started collaborating was was that if we could deliver that hyper personalized shopping experience, we were always going to be ableto show customers the right product at the right time. And if we were doing that that we would lead Tio Hi brand engagement, higher loyalty, higher on higher lifetime values of customers. And that's and that's what's shown to be the case in a silent example. >> Yeah, definitely that echo that. You know that the hypothesis hypothesis, wass. If you can show the right custom of the right product at the right time, then their purchase frequency average order Volumetrics all starts move positively and ultimately than affecting their long term engagement with our brand, which increases revenue on also delivers a more, you know, a frictionless consumer experience, hopefully for the customer, >> because I suppose your experience is the same. So many companies out there they're sitting on this huge pile of data, yet they don't know how to best optimize that data. When did you first realize, Richard that there was this kind of gap in the market for Pete to grow? >> Yeah. I think data and analytics have come on a bit of a journey away from common sense reporting Thio more advanced analytics. But when you get a I and machine learning what you're talking about, his algorithms being our self learning make predictions about things, and that actually fundamentally changes the way businesses can operate on DH. And in this case, a great example is you know, we're sending hyper personalized marketing communications, Teo every single foot silent customer. Um, they don't realize necessarily that they are tailored to them, but they just become more relevant. But it doesn't require a digital marketed to create every single one of those campaign or emails and even triggered the sending of those materials. The brain takes care of that. It can automate it. And what the marketer needs to do is feed it engaging content and set up digital campaigns. And then and then and then you're left with this capability where eyes saying you might be a market for this product. Let's let's send you something that might appeal to you on DH that just gives that gives a marketing team scale. And then, as we move into other use cases like in the supply chain for film and delivery of product the same thing that teams just get huge scale out of letting algorithms do those things for them. Andi, I suppose the realization for us that there was that gap in the market was just that you can see the out performance of certain cos you can see that Amazon attributes thirty five percent of their sales to their machine learning recommendation systems. I think Netflix says eighty five percent of all content is consumed >> in prison. It's Al Burns. Andi. Companies >> like that can harness machine learning to such a great degree. How does how do you know howto other businesses do it? Who can't access that talent pool of Silicon Valley or along the global? You know, the global talent leaders in tech and that's that's what we have. The insight that is Peak Way could create a company that gave our system is the that technology and that capability Teo deliver that same kind results that the Amazon and Netflix >> So before the Internet Yeah. Brand's had all the power you could price however you wanted if you overprice, nobody even even knew. And the Internet was sort of like the revenge of the consumer. Aye, aye. And data. How gives the brands the ability to learn more about its customers. But you have to be somewhat careful, don't you? Because your privacy concerns, obviously. Gpr etcetera. So you have to have a value proposition for the customer, as you were saying, which they may not even know that machine is providing these offers. Yeah, but they get value out of it. So how do you guys think about that in terms of experience for the customer? And how do you draw that balance? >> I think from my angle that Richard touch on a couple of bits there to do it scale first and foremost across the entire all on on Thai network of consumers is killer element to it. But to deliver that personal experience, I think consumers nowadays are so they're more expectant of this. Really? We would have considered it innovation a couple of years ago, but now Actually, it's expected, I think, from the consumer. So it's actually in the name ofthe You have to move forward to stand still. So but way Think we're We're right at the front of this at the moment. And we're really looking now how we optimize the journey for the consumer so that actually we know if we're from Simpson transactional data that we have in a little bit of over behavioral data that, you know, we're really conscious of the whole GDP, our peace and stuff, and that's really, really relevant and super important. Andi, I'm pleased to say that you know, we have that backed by a peek. It's completely on lock down from that perspective as >> well. Where do the data's where the data source of comfort. You mentioned some transaction data. Where is the other data come from? Using show social data and behavioral data? Where does that come >> with these elements of social data? Some of it is a little bit black box, so you can always access it. And that's the GPR piece there. And rightly so. Actually, in some cases we have a loyalty scheme which allows us to understand our Kashima's better in our bricks and mortar retail, which is really cool that we've got some of that transactional data on a customer level from the store's way know that some people in our sector maybe don't have that, so that so that allows us to complete sort of single customer view, which then we can aggregate in peaks brain, then transaction data on the website in the app and bits off browsing, you know, just within our own network. But you know where customers potentially being and reactive of somethin, a piece of content on journeys within the website, That's that's how we build that view. >> Do you think this is the way that more bricks and more two stores Khun survive? Because so many are closing in high streets up down that you can in other countries, because simply they're not really delivering what the customer wants? >> Yeah, I think so. Rich Now, both feel quite strongly now that wear something to this now a little bit. It's a really As as our relationship for the two businesses has evolved, it's become clearer and clearer that actually we've armed with this. You know this data, our fingertips, we can actually breathe fresh life into the stores, and it's in the eye of proper true omnichannel retailing way. Don't mind where the cost consumer spends the money. We just need to be always on in a connected environment. So that way said before pushing the right product at the right time. And when that when they're in market, we turn up the mark the message a little bit. But then understanding when they're not in market and maybe to back off him and maybe we warn them what with a little bit of a different type of message then and actually we're trapped would want to challenge ourselves to send but less better marketing communications to our consumers. But absolutely that store piece is now, so we tail back. Our store opening strategy is a business to focus more on the digital side of things, but now it's possible that way might open some more stores now, but it will be with a more reform strategy of wet, wet where, why we need to do that? >> Isn't this ironic? The brick and mortar marketplaces getting disrupted by online retailers, obviously Amazons, that big whale in the marketplace and your answer to that is to use Amazon's cloud services and artificial intelligence to pave the way for your future. Yeah, I mean, that's astounding when you think about >> coming. >> Yeah, sort of unified commerce approach, Tio. You know, there's a place in the world for shops. It's like it's not Romance isn't completely dead and going shopping Friends out, you know so on. Actually, yeah, we're using honest in the eight of us, but we'LL hire our friends at Peak. Yeah, it's it's some irony there. I think it's really cool. >> And that decision that you made obviously made made lightly. But you saw the advantages of working with the clouds outweighing the potential trade offs of competition. >> Yeah, I mean, that's not that was never really, really no, I'm certainly not know. I think this is something that is happening, that data, and on harnessing it in a in a safe, responsible, effective way, I believe, is the future of all commerce. So >> that as far as security is concerned because, of course, we have had data breaches. Yeah, customers, credit card details, access. How do you ensure that it's as secure as possible in the way that you you you choose the services. I think >> that come that just comes down to best practice infrastructure on the way we look at it, a peak is there's no bear tools in the world to do that, then the same technologies that Amazon themselves use. It's to do with how you configure those services until ls to make it secure. You know, if you have an unsecure open database on a public network, of course that's not secure. But you could have the same thing in your own infrastructure, and it wouldn't be secure. So I think the way we look at it is exactly the same thing on actually, being in the Amazon plan for us gives us a greater comfort, particularly in terms of co location of data centres, like making sure that our application fails over into different locations. It gives us infrastructure we couldn't afford otherwise, and then on top of that, we get all these extra pieces of technology that can make us even more secure than we could do. Otherwise we'd have to wait, have to employ an army of infrastructure engineers, and we don't have to do that because we run on Yes, >> okay, so we were able to eliminate all that heavy lifting. Same goes. You've got this corpus of data. I'm interested in how long it took to get through. A POC trained the models how much data science was involved. How much of a heavy lift was that? Yeah, well, I think for us >> we better be pretty rapid. Actually, we start working together in January last year, so we're only just sort of year into that. >> And in that faith in that entire >> sofa length of of our relationship, we've gone from high for personalizing digital campaigns to recommendation systems on a website to now optimizing customer acquisition on social media and then finally into the supply chain and optimizing demand. And so on and on. I think there's a lot of reasons why we've been able to do it quickly, but that's fundamental to the technologies that that peak is built. There's two. There's two sides to it. Our technologies cut out a lot of the friction so way didn't run a proof of concept. We were able to just pick it up, run with it and deliver value. And that's to do with I think, the product that peak is built. But then you obviously need a a customer who's who's going on a transformation journey and is hungry to make that make that stick in London on. Then when the two come together, >> I think that it's an interesting point that, though, because while suite for asylum, we always I always say it's that we're not. We're not massive, but we're not tiny, but it's the sort place you Khun turn upon a Monday and say, I've had an idea about something and we're not doing it by Friday. That's That's a nice, agile culture. It can create some drama as well. Possibly. I think it's really straightforward to get straight into it. And I think this is where some of the bigger, um, sleepier high street retailers that Amar fixed in a in a brick Samara world need to not be too afraid to come out and start embracing it. Because I think some of them are trying now. I think it might be a little bit late for some now, but it's it was just it was just wasn't that hard really to get going here >> and you've seen the business results. Can you share any measurements or quantification. We've >> got a really a really good one that we're just talking about at the moment. Actually, Way were able to use segmentation tools within within the peak brain, too to use them on social than Teo. Create lookalike audiences. So Facebook custom tools, right? We'LL help you create audiences that it thinks will be wrapped pirates complex algorithms itself. But we almost took a leap ahead of their algorithms by fire, our algorithms uploading our own segments to create a more sophisticated lookalike audience. We produced a row US results or return on that spend People are not familiar with that of eight thousand four hundred percent which we we would normally be happy as a business. We've sort of seven, eight hundred percent. If you're running that that we've say on AdWords campaign or something like that, that's quite efficient campaign. So it's at zero. We were a bit like it felt like it's a mistake that, you >> know, that is >> not the right Yeah, but not so that's super cool. And that's really that's really opened our eyes to the potential of punishing that the, you know, our sort of piquet I brain to then bring it onto Social on. Do more outward. Advertise on there. >> So moving the goal post meant that your teeth have really high school. Thank you. Thank you very much for telling us all about that time someone feels on Richard for so thank you for joining me and David Auntie here at the age of Lou s summit in London. Merchant to come on the King.

>> live from London, England. It's the queue covering a ws summat London twenty nineteen, brought to you by Amazon Web services >> Hello and welcome to the A W s summit here in London's Excel Center. This is the Cube. Is my co host a Dilantin also. Now we're joined by present Chandrasekhar, who is the senior vice president and general manager act rack space and everything. If you're here to talk about really the next generation of cloud services, what are they on? What do you communicating to you? Partners here at the >> conference? Absolutely. Thank you, Susanna and day, for having me back on the show. Big fan of the Cube. Eso No, >> really, I >> think Rackspace next generation Cloud services absolutely foundational to what we do for our customers. And so, you know, ultimately what we're trying to deliver is a utility based model of service is very similar to how Amazon thinks about the cloud and what you know, they were effectively lead over the mass passed many years. So I think that the world we believe the world of traditional I t services of large, monolithic contracts where you got traditional size that are going and working with companies to say, Let us transform you with little transformation and you know, what about so services? I think those days are effectively gone and they're dead. So from our perspective, customers are on this journey from one platform to another. They're moving from traditional workloads through the public cloud. There's that hybrid journey that's underway, and we've talked about how Amazon has, you know, really acknowledged that through its working outposts, etcetera. But the idea is for us to say Listen, customers are in a very bespoke journey. Everyone's in a different journey. Individual journey. Let's feed them exactly where they are in that journey. Whether that's you know, right now moving, uh, traditional I t work loads to the public cloud. So let's go on architect and deploy them and migrate them based on best practices that we've gained from thousands of these engagements. Or, you know, if they're further along and they're actually did need to manage and operate these in a very you know, container centric or Cuban Eddie centric world, we can help them. They're too, or if they're already know several years in and there you see, the costs are getting hard to control because they've got sprawl within the organization. We can help them with cast optimization and governance. And all this is enabled through what we call a service walks model attract space, which really stitches together various of the's no peace part, if you will, of services across the infrastructure, security applications across the whole stack. And so that's the idea. So how would you categorize first? Not the rackspace strategy people remember. Of course. You guys catalyzed in incubated the open stack movement, which was kind of a Hail Mary against eight of us. And then others chimed in. And then you realize that Wow, we're going to step away. Yeah, it was great. Open source project. Amazing on DH. Now you partnering on Amazon? What's the strategy? How would you describe that? Yes. You know, I think if you've learned anything over the past, you know, ten, twenty years and that practice has been around for now, twenty one years, you know that it's an extremely dynamic market and is driven by customers ultimately and their pace of change and so on. So when we started as a company, you know, twenty years ago, we started manage hosting business and services is the foundation element of what we do and support and expertise for customers enabled by technology. And so that really helped us, you know, take us to the first ten years of our journey. And then the cloud movement enabled a lot by Amazon really took off and where it was really a mainstream consideration or an early consideration to say its more mainstream now, obviously. But back then, So we competed with the open stack from the cloud business on. Then, very soon we realised our customers were all also operating in Amazon, and so that really said, Listen, we've always historically said, Lets go where customers want to go and we've always been a services technology serves this company at heart, so it doesn't make a lot of sense for us to do move away from that DNA and that ethos. So it's no different from fasten it, saying, uh at a high level, you know, Windows O. R. Lennox. We can have a very kind of, you know, dogmatic view about one of the other. We just have to say this and what the customers want to work on based on what their various various factors that the take in consideration so no different. Here. Platforms are just platforms, their choices that customers have. And so we started saying, You know what? If customers want help on Amazon, there's still asking us for it. Lets go in partners with Amazon to do exactly that. So that's exactly what we did in twenty fifteen. >> So where do you fit in that value change? How do you help customers and weirdos? Rackspace add unique value. >> Yeah, so I think ultimately, you know there's various elements of value along the way, and I sort of describe the service rocks model is the way in which we really bring it together. So customers are either looking for help to get to the cloud. And they're asking us, You know, what is the best way for me to get there, given my current state. And so there's a deep, you know, assessment that's done from a kind of way, have a lot of expertise, and Laxmi is over a thousand data be a certified experts on certification. So we bring those experts to the customer, talk about you know why they're trying to go. Hey, they're trying to really reduce your meantime to recovery. You're trying to increase your release cycles on a kind of, you know, per you know, a certain rate that's very aggressive operate with the devil's principle and mindset. You know all those things are the object of the customers has and then be then enable them to go and say Okay, given all that here, the workloads we'd would enable you to kind of, like move or to kind of like build from scratch, bring an entire set of services with their infrastructure, security or applications services, start with the value added set of workloads, and then build from that effectively prove the case and then move on. To >> date, the very fact that Amazon websites its growth has bean so rapid. And there are so many new services coming online. You know, every bump that's actually helping you because people need help to navigate. >> Indeed. I mean, that's a that's a phenomenal point. I mean that ultimately, you know, bar the reason why customers in our install base we're reaching out to us and saying, Hey racked with you, done a phenomenal job helping us in our first evolution of our journey. Can you help us now in this new world where it's actually quite complicated? You know, that's sixteen hundred features on average of forty hundred features on average are being launched by Amazon on a yearly basis. And that's just, you know, despite what we hear in the headlines where cloud first companies and us, the startups of today are absolutely leveraging. You know, Lambda out of the gate or containers out of the gate, you know. But there there's a whole host of companies that are going through this massive digital disruption, trying to compete with these startups that >> need >> a lot of help to re skill their workforce, to change the way they think about process within the within their organization, between their business development and technology and operations teams. And then, ultimately, you know, how do they actually build out much more agile? We have respond to customers so that work requires a company like Rackspace to come and help them navigate through that. Really, really, you know, large, you know, set of features. >> I suppose that it's a space that you certainly didn't forsee ten years ago. >> Oh, absolutely, No. That's what's so dynamic about the space where I think that nobody, I think, could have predicted, You know, even today we're seeing this's a ton of kind of like, you know, momentum with concepts that were very nascent only a few years ago. The Cuban Eddie's There's a concept, you know, almost every one of our eight of us customers at Rackspace, what we call fanatical A W s eyes absolutely looking for help on communities. And so, you know, when we think about Doctor A few years ago on Doc Enterprise on, we think about communities and there was that, you know, battle today, you know, the battle has been won Carbonetti XYZ pretty much pretty much the defacto orchestration engine. So nobody could have predicted that a couple years ago tomorrow. Somebody else. Exactly. So it's fascinating, And that's why customers need help navigating. >> You know, all those guys are. The experts carried people through the journey. It's mentioned hybrid before customers want choice. You know, even the Amazon wants everybody to put their data. Their cloud. Yeah, customers sometimes have multi clouds and absolutely as a hybrid. And Marty, I think, >> is a is becoming a lot more. I think even Amazon is very much acknowledging that the big opportunity is high. Isn't hybrid Cloud Because if you think about where we are and the technology adoption curve and the trillion dollars have spent that ultimately going to move, there's no doubt that it's a class for cloud First World. Their destination is the cloud, but the vast majority. The workloads exists in traditional i t. And so how do we take that hybrid moment? You know, and outposts? It's a great acknowledgement of that on. So they're very aggressively investing. We're investing with them and helping our customers along that money effectively. >> Okay, Present for a second. Thank you very much for talking to us from Iraq Space. And my co host, David Lynch has been helping us. Navigator, What's happening here had the A W s Web something. I'm Susanna Street. Thanks for watching the Cube.