>>Hello from Las Vegas. It's the cube live at AWS reinvent 2021, Lisa Martin and Dave Nicholson here. We're in our fourth day, Dave, we have two live sets of the kid. There's a dueling set right across from us, kind of like dueling pianos, only a little bit louder. We have had about a hundred guests on the program at AWS reinvent this year. And we're pleased to welcome back. One of our alumni, Richard Potter joins us the CEO of peak. Richard. Welcome back to the cube. >>Great to be here. Talk to >>Us. So we haven't seen you in a couple of years. Talk to us about what's going on at pink. I know there's some news. >>Yeah, yeah. Loads of things going on at peak. I mean, we've been growing really quick. So since the last time you saw us, which was yeah, in London a few years ago, uh, we've grown to be the, sort of essentially the global leader in decision intelligence systems. Um, us as an AI company, we specialize in putting artificial intelligence right into the heart of how companies run their businesses and make their day-to-day decisions, which is why we call it decision intelligence. We think it's the biggest thing in software and, uh, probably the biggest new category of software. Um, we will see this decade. So it's super exciting to be in that position and great to be back chatting to you guys on the cube. When were you based founded? We were founded in 2016. Uh, and, uh, yeah. And you can probably tell by my accent English company headquartered in Manchester, but we're global. Now we have operations in India. We have a couple of development centers in India. We have a growing customer base in Asia and a growing customer base in the U S as well. Uh, so yeah, we're kind of international, but born out of, uh, Northern English roots. >>I like it. Talk to me about back in 2016, what were some of the gaps in the market that you saw from a, because you know, as, as here we are in almost 20, 22, every company is a data company. They have to be being able to extract intelligence timely hard. What gaps did you see back in 2016 >>Back then a read on the market was really simple, which was the companies that are going to harness data to run themselves well, we'll win, but the most companies were struggling to make that change to be data-driven. So our rich was, you know, as founders, there's three of us who started the business was trying to explore that problem. Like what, what, what stops companies running on data? And there's loads of reasons, right? Tech ones, uh, skills, ones, even just like business people using data in their day-to-day decision-making rather than say their gut-feel, which I think is also a data-driven decision. They just don't understand that necessarily. Uh, so we really honed in on that problem and we grew quite quickly to be the leading business in that sort of applied data space in the UK, you know, a market leader in, uh, helping companies perform better with data. And over time that has taken us on this journey to be the sort of global leader in decision intelligence, which is really cool. But the itch we were scratching was that, Hey, you know, there's something in this, we think companies that do this and do it well are gonna win, but no one's doing it. So why is that? And then, and then we've built software that effectively responds to that opportunity. >>You mentioned harnessing data. Yeah. How do you balance the harnessing of data successfully with being harnessed by data? Because, because if you're talking about the concept of Dai yeah. Who's making the decision. If the machine is making the decision, I better trust it. Why should I trust it? So how do you, how do you strike that balance to get people to trust what you're doing? The work you're doing for them behind the scenes? Yeah, >>I think it's, it's really important that humans trust the machines that they're working alongside. And I think that's the big change we're seeing, right? So this is a new industrial revolution, the intelligence era that we're in, but all previous industrial revolutions have all amplified human potential. They've amplified like a physical potential, whether it was, you know, machinery, steam, power and so on, or computers have amplified our cognitive capability, but humans have always controlled those machines. If you think about it now in the intelligence era, our machines can think with us, they can think alongside us. So we have to learn how to, as people, how to co-exist with those machines and then let those machines amplify us and essentially make us superhuman and what we do. And that's a part of the challenge we face at peak as to how do we make, how do we humanize that? >>How do we make it such that everyone trusts the machine? Uh, and we always have that human in the loop is the way we think about it. Uh, decision intelligence empowers us to be awesome at our jobs, make the great decisions all the time. If we trust the machine so much that we just want it to make the decision for us, we can let it, but we're always in control and we're in control of how it thinks and what it does. And it's our job as a software company to build software that lets you understand why that recommendation or that decision is being suggested to you. So I think, I think the coexistence of our machines alongside people in a new way that a human to machine interface is going to completely change with artificial intelligence and decision intelligence and, and us as people we're going to have to relearn how we, how we work with our technology. >>You just mentioned a couple of really good words in terms of, of the people, part of people, process and technologies, amplify and empower. Those are two things that stuck out at me is that's what you're giving people in any, whether they're an operations or finance or marketing, it's the amplification to do their jobs, empowering them to do their jobs with data that will help make them more skilled and better able to make decisions that benefit themselves, the company. >>That's exactly right. Yeah, because if you, if you redact doing business to its basics, it's, it's actually just making decisions, right. Companies are make great decisions. They win and those decisions could be anything, you know, they could be product decisions, they could be pricing decisions, operational supply chain decisions, but it's a sequence of decisions that creates value for my company. And so that's why I believe this technology is so empowering because as people we're, we're actually great at making those decisions. What we're not great at is making those decisions 24 by seven really, really quickly, very consistently. So, you know, humans are awesome at forecasting. They're awesome at choosing pricing that would appeal to other people, but alongside this technology, we can have machines that do a lot of that thinking for us, speed us up and help us make more, um, quick, great consistently awesome decisions. And then that just makes us great at our jobs. If you're a marketeer or in finance or in supply chain, you, you become awesome. And I think that that, that empowerment is key to the sort of humanization of AI in business. And actually that's what it means in practice. It isn't AI coming for peoples' jobs or replacing jobs. It's it's AI helping us all be gray. And our companies grow faster with wider profit margins when we do that, which creates more jobs for people, which is really cool. >>So, um, we talk about people trusting machines to do things for them. Uh, it's, it's not necessarily a new concept. We just sort of take some of those things for granted. Um, I trust my refrigerator at home to measure the internal temperature and make adjustments as necessary. Turn the compressor on, turn the compressor off. And I'm sorry, I you're from England refrigerators, this thing, it's a box. We use it to refrigerate our beer, which I took to make it >>Cold, which I know. >>So it's kind of a, you know, got to love those cliches, but so can you give us an example of a situation where a customer is trusting something that it's gotten from DEI from peak, where if you, as the CEO heard that anecdotal story, you would be absolutely delighted. >>Well, I think the earth is loads of great examples of that. So, um, the reason we call it decision intelligence decision intelligence is because it's the, it's applying AI into the active decision making, right? Uh, artificial intelligence or machine learning is making a prediction or a categorization over a huge data set. Right? But that on its own is kind of useless. You need to take that prediction that forward looking view and then effectively infuse it with business logic constraints and like knowledge of how your company works to give you a recommendation. Right? So let's just say I'm a marketeer and I'm trying to work out who I should send a particular offer to on black Friday over email, or even not even over email over any channel. When, if I, if I was CEO and I heard one of my teams say, Hey, what I've done is I've used the decision intelligence platform to tell me who buy, who are my customers that are in market for X type of products at why kind of price and what channels do they like to be communicated to over? >>Uh, I would think that's awesome. And then that market here, we're typically infuse that message with the sort of language and content that would appeal to that customer. But they're using the artificial intelligence to be super targeted and really like deliver the message to that person in the way they want to consume it, which creates a really enjoyable experience as a customer. You don't feel spammed or you don't feel like it's effectively used. You feel like you're having a direct one-to-one personal communication with the brand or retailer. That's talking to you, which in itself creates loyalty and like increases the lifetime value of that relationship, which is great for the retailer. But I think using AI for those kinds of decisions is essentially like a great example of like amplifying the human potential of a marketing team for this. >>Absolutely. Because what we expect as consumers, regardless of what the product or service is, is that we want brands to know who we are, what we want. Don't if I just bought a tent on Amazon, don't show me more tests, show me other things that go with it. I want you to know that. And so we have this expectation that brands when whatever industry they're in, no, oh, Richard bought this. >>Exactly, exactly. So, and I think that it starts to really jar. Now you've got some retailers and brands doing this really well, and you get really enjoyable, uh, communications at the frequency you want with the offers and the promotions that were irrelevant to you. When you just start to get trapped, you know, effectively stalked around the internet for something you've already bought, it becomes really jarring and frustrating. And then that actually creates a negative brand effect for that particular brand. So it's super important that these retailers, CPG com everyone really moves to this way of thinking and tries to have a direct. And that's the beauty of AI and decision intelligence. I think for retail, if we get into retail specifically, it allows us to treat every individual customer individually because we can use the machine to make decisions on a per customer basis. And then our marketing can be amplified by that. Whereas in the past, we bucketed customers into groups and just treated them all the same, which does create a rather impersonal experience. >>Yeah. Which can be a negative for a brand, as you mentioned, but give them the ability to treat people individually, but at scale, and in real time, one of the things we learned in the pandemic is that real-time data access isn't no is not a nice to have. It's an essential one of the themes too, that Dave and I have been talking about the last few days is that we're hearing at re-invent is every company has to be a data company. Yep. Talk to me about with that in mind, are you talking to more chief data officers, chief digital officers, where are your customer conversations as we've we're in this explosion of data? >>It's a great question though. So if every company has to be a data company and a company that's powered by AI, that means you have to be talking to everyone really. So your chief data, chief chief information officers, chief data officers, CEO, CFOs, and every sort of head of business, head of line of business, it's really important. So what we do at peak is as a decision intelligence platform, peak itself, unifies everything you need in one cloud platform, into a single software product that gives you all the infrastructure for your technical teams to process data for your data scientists to create the intelligence, but then it gives you a place to work for your business teams. So unifies your whole business around a platform. And then that means our conversations. As you know, as the provider of that technology are with technical teams, they're with business teams, they're with business leaders because it has to permeate everything. So I think it's, I think that's the future companies will have to effectively run alongside they'll create their own intelligence, basically on a dedicated platform like peek. And that intelligence will then be distributed across the whole business, um, with w w you know, in the way we do it. So I think it's really cool and exciting. Yeah. >>Let let's say hypothetically, now this is something that would never happen, but just hypothetically say I'm an American goes to England to take over coaching, a British soccer, soccer, or football. Okay. I sounds crazy, but how would I, how would I use peak and Dai and BI to help improve my winning percentage if I cared about winning? Because it's possible that I would, I I'm really only interested in the personal development of my, of my team as individuals, but, but, but what would in athletics? Is that something that is a, >>I think possible? Yeah, for sure. I mean, you're seeing an explosion of data science and analytics and AI techniques being used in sport. Right. I mean, peak we're very much focused on the commercial application of AI with our platform. So we, we work with, uh, commercial businesses and so on, but in that space, yeah, absolutely. I mean, there's, if you think about it, what do you need to create that intelligence? You need data and you can see it on the back of every players share. They've got the little devices that are gathering data in training in matches, constantly monitored. Those data points, feed algorithms. Those algorithms can show us if a player is fatigued, you know, where they are, or they can even show us, uh, deep learning techniques can help us see patterns of play and understand like how should we better set our teams up? How should we get players to interact in for, you know, on a soccer field? Um, and yeah, and you're seeing premier league clubs use those sort of techniques all the time. We don't do that at peak, but yeah, I mean, I think, uh, I think those sort of things are readily available now for, uh, those kinds of clubs to do that kind of stuff. >>I think Dave is angling to be a consultant on Ted last. So I think what I'm hearing last question for you, you guys are from an AWS relationship perspective. Richard, you guys were announced just yesterday, you're named by AWS as an ISB partner, APN partner of the year for 2021 for UK. And I, congratulations. Talk to us a little bit about that. >>Yeah, it was really, I kind of, yeah, it's super exciting for us. It's a great recognition. Obviously they give one of those awards out every year, uh, as a global company, it's nice to have that sort of stamp of approval that AWS sees us as their independent software vendor partner of the year. It's a, it's a great recognition for us because we come from a heritage of, uh, starting peak as a consulting company, actually just to do whatever it took to help our customers be successful. And in doing that, we had an idea for a software platform. Uh, we got some venture funding to do that, and we've turned into a, you know, we became a software company a couple of years after we founded, uh, and to get to this point now a few years later where AWS are recognizing us as their software vendor partner of the year is, um, a huge team. Fantastic. It's a huge Testament to, uh, to our engineering teams and the, and the, and the technical teams at peak that we've built something so impactful. Yeah, >>Absolutely. That validation is really, really critical. And last question in our last 30 seconds or so what are some of the things on the roadmap that you're excited for for, for peak for 20 22, 22 >>Is going to be a huge year for us. Cause I think it's the year that, uh, our platform goes out there into the wild, into the mainstream. So we made a couple of big announcements in the last few weeks. Uh, we've launched some new products on the pig platform. So there's three big platform, product sets. Now, one very much geared around creating your AI ready data set. That's called doc, uh, one that's very much geared around creating your intelligence, which is factory. And then an area where our business like the business teams of our customers go to work, which is called work actually. So those three big feature sets are going to be available from January. And the platform is being totally opened up as a self-serve platform for anyone anywhere to build upon. So I think it's a huge moment for decision intelligence. Garner is saying decision intelligence is the big tech trend of next year. And we feel as the market leader, we've got the platform that can help everyone get on, get on that trend really. So I think we're really looking forward to 2022 and what it brings. And, um, we think that our platform and our company is in a great shape to help more and more businesses take that leap into being powered by decision Intel. >>It sounds exciting, Richard, so we'll have to follow up with you next year and see what's going on. We appreciate you joining us on the cube, talking about peep, what you're doing, your relationship with AWS and how impactful decision intelligence can be for everybody. We appreciate it. Thanks for Dave Nicholson. I'm Lisa Martin. You're watching the cube, the global leader in live tech coverage.

>> 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.