(upbeat music) >> Welcome everyone to theCUBE's presentation of the AWS Startup Showcase. Topic is MarTech: Emerging Cloud-Scale Experience. This is season two, episode three of the ongoing series covering the exciting startups from the AWS ecosystem. Talk about their value proposition and their company and all the good stuff that's going on. I'm your host, John Furrier. And today we're excited to be joined by Manyam Mallela who's the co-founder and head of AI at Blueshift. Great to have you on here to talk about the Blueshift-Intelligent Customer Engagement, Made Simple. Thanks for joining us today. >> Thank you, John. Thank you for having me. >> So last time we did our intro video. We put it out in the web. Got great feedback. One of the things that we talked about, which is resonating out there in the viral Twitter sphere and in the thought leadership circles is this concept that you mentioned called 10X marketer. That idea that you have a solution that can provide 10X value. Kind of a riff on the 10X engineer in the DevOps cloud world. What does it mean? And how does someone get there? >> Yeah, fantastic. I think that's a great way to start our discussion. I think a lot of organizations, especially as of this current economic environment are looking to say, I have limited resources, limited budgets, how do I actually achieve digital and customer engagement that helps move the needle for my key metrics, whether it's average revenue per user, lifetime value of the user and frequent interactions. Above all, the more frequently a brand is able to interact with their customers, the better they understand them, the better they can actually engage them. And that usually leads to long term good outcomes for both customer and the brand and the organizations. So the way I see 10X marketer is that you need to have tools that give you that speed and agility without hindering your ability to activate any of the campaigns or experience that you want to create. And I see the roadblocks usually for many organizations, is that kind of threefold. One is your data silos. Usually data that is on your sites, does not talk to your app data, does not talk to your social data, does not talk to your CRM data and so forth. So how do I break those silos? The second is channel silos. I actually have customers who are only engaging on email or some are on email and mobile apps. Some are on email and mobile apps and maybe the OTT TV in a Roku or one of the connected TV experiences, or maybe in the future, another Web3 environments. How do I actually break those channel silos so that I get a comprehensive view of the customer and my marketing team can engage with all of them in respect to the channel? So break the channel silos. And the last part, what I call like some of the little talked about is I call the inside silo, which is that, not only do you need to have the data, but you also have to have a common language to share and talk about within your organizations. What are we learning from our customers? What do we translate our learning and insight on this common data platform or fabric into an action? And that requires the shared language of how do I actually know my customers and what do I do with them? Like either the inside silo as well. I think a lot of times organizations do get into this habit like each one speaks their own language, but they don't actually are talking the common language of what did we actually know about the real customer there. >> Yeah, and I think that's a great conversation because there's two, when you hear 10X marketer or 10X conversations, it implies a couple things. One is you're breaking an old way and bringing in something new. And the new is a force multiplier, in this case, 10X marketer. But this is the cloud scale so marketing executives, chiefs, staffs, chiefs of staffs of CMOs and their staffs. They want to get that scale. So marketing at scale is now the table stakes. Now budget constraints are there as well. So you're starting to see, okay, I need to do more with less. Now the big question comes up is ROI. So I want to have AI. I want to have all these force multipliers. What do I got to do with the old? How do I handle that? How do I bring the new in and operationalize it? And if that's the case, I'm making a change. So I have to ask you, what's your view on the ROI of AI marketing, because this is a key component 'cause you've got scale factor here. You've got to force multiplier opportunity. How do you get that ROI on the table? >> I think that as you rightly said, it's table stakes. And I think the ROI of AI marketing starts with one very key simple premise that today some of the tools allow you to do things one at a time. So I can actually say, "can I run this campaign today?" And you can scramble your team, hustle your way, get everybody involved and run that campaign. And then tomorrow I'd say like, Hey, I looked at the results. Can I do this again? And they're like, oh, we just asked for all of us to get that done. How do I do it tomorrow? How do I do it next week? How do I do it for every single week for the rest of the year? That's where I think the AI marketing is essentially taking your insight, taking your creativity, and creating a platform and a tool that allows you to run this every single day. And that's agility at scale. That is not only a scale of the customer base, but scale across time. And that AI-based automation is the key ROI piece for a lot of AI marketing practitioners. So Forrester, for example, did a comprehensive total economic impact study with our customers. And what they found out was actually the 781% ROI that they reported in that particular report is based on three key factors. One is being able to do experiences that are intelligent at scale, day in and day out. So do your targeting, do your recommendations. Not just one day, but do it every single day. And don't hold back yourself on being able to do that. >> I think they got to get the return. They got to get the sales too. This is the numbers. >> That's right. They actually have real dollars, real numbers attached to it. They have a calculator. You can actually go in and plug your own numbers and get what you might expect from your existing customer base. The second is that once you have a unified platform like ours, the 10X marketer that we're talking about is actually able to do more. It's sometimes actually, it's kind of counterintuitive to think that a smaller team does more. But in reality, what we have seen, that is the case. When you actually have the right tools, the smaller teams actually achieve more. And that's the redundant operations, conflicting insights that go away into something more coherent and comprehensive. And that's the second insight that they found. And the third is just having reporting and all of the things in one place means that you can amplify it. You can amplify it across your paid media channels. You can amplify it across your promotions programs and other partnerships that you're running. >> That's the key thing about platforms that people don't understand is that you have a platform and it enables a lot of value. In this case, force multiplier value. It enables more value than you pay for it. But the key is it enables customers to do things without a line of code, meaning it's a platform. They're innovating on top of it. And that's, I think, where the ROI comes in and this leads me where the next question is. I wanted to ask you is, not to throw a wet blanket on the MarTech industry, but I got to think of when I hear marketing automation, I kind of think old. I think old, inadequate antiquated technologies. I think email blasting and just some boring stuff that just gets siloed or it's bespoke from something else. Are marketing automation tools created equal? Does something like, what you guys are doing with SmartHub? Change that, and can you just talk about that 'cause it's not going to go away. It's just another level that's going to be abstracted away under the coverage. >> Yeah, great question. Certainly, email marketing has been practiced for two or three decades now and in some form or another. I think we went from essentially what people call list-based marketing. I have a list, let me keep blasting the same message to everybody and then hopefully something will come out of it. A little bit more of saying, then they can, okay, maybe now I have CRM database and can I do database marketing, which they will call like, "Hey, Hi John. Hi Manyam", which is the first name. And that's all they think will get the customer excited about because you'll call them by name, which is certainly helpful, but not enough. I think now what we call like, the new age that we live in is that we call it graph-based marketing. And the way we materialize that is that every single user is interacting with a brand with their offerings. So that this interaction graph that's happening across millions of customers, across thousands of content articles, videos, shows, products, items, and that graph actually has much richer knowledge of what the customer wants than the first names or list-based ones. So I think the next evolution of marketing automation, even though the industry has been there a while, there is a step change in what can actually be done at scale. And which is taking that interaction graph and making that a part of the experience for the customer, and that's what we enable. That's why we do think of that as a big step change from how people are being practicing list-based marketing. And within that, certainly there is a relation of curve as to how people approach AI marketing and they are in a different spectrum. Some people are still at list-based marketing. Some people are database marketing. And hopefully will move them to this new interaction graph-based marketing. >> Yeah and I think the context is key. I like how you bring up the graph angle on this because the graph databases imply there's a lot of different optionality around what's happened contextually both over time and currently and it adds to it. Makes it smarter. It's not just siloed, just one dimensional. It feels like it's got a lot there. This is clearly I'm a big fan of and I think this is the way to go. As you get more personalization, you get more data. Graphic database makes a lot of sense. So I have to ask you, this is a really cutting edge value proposition, who are the primary buyers and users in an organization that you guys are working with? >> Yeah, great question. So we typically have CMO organizations approaching us with this problem and they usually talk to their CIO organizations, their counterparts, and the chief information officers have been investing in data fabrics, data lakes, data warehouses for the better part of last decade or two, and have some very cutting edge technology that goes into organizing all this data. But that doesn't still solve the problem of how do I take this data and make a meaningful, relevant, authentic experience for the customer. That's the CMO problem. And CMO are now challenge with creating product level experience with every interaction and that's where we coming. So the CMO are the buyers of our SmartHub CDP platform. And we're looking for consolidating hundreds of tools that they had in the past and making that one or two channel marketers. Actually, the 10X marketer that we talk about. And you need the right tool on top of your data lakes and data warehouses to be able to do that. So CMO are also the real drivers of using this technology. >> I think that also place the ROI equation around ROI and having that unified platform. Great call out there. I got to ask you the question here 'cause this comes up a lot and when I hear you talking, I think, okay, all the great stuff you guys have there. But if I'm a company, I want to make my core competencies mine. I don't really want to outsource or buy something that's going to be core to my business. But at the same time as market shifts, the business changes. And sometimes people don't even know what business they're in at the end of the day. And as it gets more complicated too, by the way. So the question comes up with companies and I can see this clearly, do I buy it? Do I build it? When it comes to AI because that's a core competency. Wait a minute, AI. I'm going to maybe buy some chatbot technology. That's not really AI, but it feels like AI, but I'm a company, I want to buy it or build it. That's a choice. What do you see there? 'Cause you guys have a very comprehensive platform. It's hard to replicate, imitates, inimitable. So what's your customers doing with respect buy and build? And where do they get the core competency? What do they get to have as a core competency? >> Fantastic. I think certainly, AI as it applies to at the organization level, I've seen this at my previous organization that I was part of, and there will be product and financial applications that are using AI for the service of that organization. So we do see, depending upon the size of the organization having in-house AI and data science teams. They are focused on these long term problems that they are doing as part of their product itself. Adjacent to that, the CMO organization gets some resources, but not certainly a lot. I think the CMO organization is usually challenged with the task, but not given the hundred people data science and engineering team to be able to go solve that. So what we see among our customer base is that they need agile platform to do most of the things that they need to do on a day to day basis, but augmented with what our in-house data science they have. So we are an extensible platform. What we have seen is that half of our customers use us solely for the AI needs. The other half certainly uses both AI modules that we provide and are actually augmented with things that they've already built. And we do not have a fight in that ring. But we do acknowledge and we do provide the right hooks for getting the data out of our system and bringing their AI back into our system. And we think that at the end of the day, if you want agility for the CMO, there should not be any barriers. >> It's like they're in the data business and that's the focus. So I think with what I hear you saying is that with your technology and platform, you're enabling to get them to be in the data business as fast as possible. >> That's right. >> Versus algorithm business, which they could add to over time. >> Certainly they could add to. But I think the bulk of competencies for the CMO are on the creative side. And certainly wrangling with data pipelines day in and day out and wondering what actually happened to a pipeline in the middle of the night is not probably what they would want to focus on. >> Not their core confidence. Yeah, I got that. >> That's right. >> You can do all the heavy lifting. I love that. I got to ask you on the Blueshift side on customer experience consumption. how can someone experience the product before buying? Is there a trial or POC? What's the scale and scope of operationalizing and getting the Blueshift value proposition in them? >> Yeah, great. So we actually recently released a fantastic way to experience our product. So if you go to our website, there's only one call-to-action saying, explore Blueshift. And if you click on that, without asking, anything other than your business email address, you're shown the full product. You're given a guided tour of all the possibilities. So you can actually experience what your marketing team would be doing in the product. And they call it Project Rover. We launched it very recently and we are seeing fantastic reception to that. I think a lot of times, as you said, there is that question mark of like, I have a marketing team that is already doing X, Y, Z. Now you are asking me to implement Blueshift. How would they actually experience the product? And now they can go in and experience the product. It's a great way to get the gist of the product in 10 clicks. Much more than going through any number of videos or articles. I think people really want to say, let me do those 10 clicks. And I know what impression that I can get from platform. So we do think that's a great way to experience the product and it's easily available from the main website. >> It's in the value proposition. It isn't always a straight line. And you got that technology. And I got to ask from between your experience with the customers that you're talking to, prospects, and customers, where do you see yourself winning deals on Customer Engagement, Made Simple because the word customer engagement's been around for a while, and it's become, I won't say cliche, but there's been different generational evolutions of technology that made that possible. Obviously, we're living in an era of high velocity Omni-Channel, a lot of data, the graph databases you mentioned are in there, big part of it. Where are you winning deals? Where are customers pain points where you are solving that specifically? >> Yeah, great question. So the organizations that come to us usually have one of the dimensions of either they have offering complexity, which is what catalog of content or videos or items do they offer to the customers. And on the data complexity on the other side is to what the scale of customer base that I usually target. And that problem has not gone away. I think the customer engagement, even though has been around for a while, the problem of engaging those customers at scale hasn't gone away and it only is getting harder and harder and organizations that have, especially on what we call the business-to-consumer side where the bulk of what marketing organizations in a B2C segments are doing. I have tens to millions of customers and how do I engage them day in and day out. And I think that all that problem is only getting harder because consumer preferences keeps shifting all the time. >> And where's your sweet spot for your customer? What size? Can you just share the target organization? Is it medium enterprise, large B2C, B2B2C? What's the focus area? >> Yeah, great question. So we have seen like startups that are in Silicon Valley. I have now half a million monthly active users, how do I actually engage them to customers and clients like LendingTree and PayPal and Discovery and BBC who have been in the business for multiple decades, have tens of millions of customers that they're engaging with. So that's kind of our sweet spot. We are certainly not maybe for small shop with maybe a hundred plus customers. But as you reach the scale of tens of thousands of customers, you start seeing this problem. And then you start to look out for solutions that are beyond, especially list-based marketing and email blast. >> So as the scale, you can dial up and down, but you have to have some enough scale to get the data pattern. >> That's right. >> If I can connect the dots there. >> I would probably say, looking at a hundred thousand or more monthly active customer base, and then you're trying to ramp up your own growth based on what you're learning and to engage those customers. >> It's like a bulldozer. You need the heavy equipment. Great conversation. For the last minute we have here Manyam, give you a plug for the company. What's going on? What are you guys doing? What's new? Give some success stories, your latest achievements. Take a minute to give a plug for the company. >> Yeah, great. We have been recognized by Deloitte as the fastest growth startup two years in a row and continuing to be on that streak. We have released currently integrations with AWS partners and Snowflake partners and data lake partners that allow implementing Blueshift a much streamlined experience with bidirectional integrations. We have now hundred plus data connectors and data integrations in our system and that takes care of many of our needs. And now, I think organizations that have been budget constraint and are trying to achieve a lot with a small team are actually going to look at these solutions and say, "Can I get there?" and "Can I become that 10X marketing organization? And as you have said, agility at scale is very, very hard to achieve. Being able to take your marketing team and achieve 10X requires the right platform and the right solution. We are ready for it. >> And every company's in the data business that's the asset. You guys make that sing for them. It's good stuff. Love the 10X. Love the scale. Manyam Mallela, thanks for coming on. Co-founder, Head of AI at Blueshift. This is the AWS Startup Showcase season two, episode three of the ongoing series covering the exciting startups from the AWS ecosystem. I'm John Furrier, your host. Thanks for watching. >> Thank you, John. (upbeat music)

(upbeat music) >> Welcome, everyone, to this CUBE Conversation here in Palo Alto, California. I'm John Furrier, host of the CUBE. We're here to talk about the state of MarTech and AI. We're here with the co-founder and head of AI for Blueshift, Manyam Mallela. Welcome to the CUBE, thanks for coming on. >> Thank you, John. Thank you for having me, excited to chat with you. >> Blueshift is a company you've co-founded with a couple other co-founders and you guys have a stellar pedigree going in data AI back before it was fashionable, in the old days, Web 1.0, if you want to call it that. So, you know, we know what you guys have been doing in your careers. Now you got a company on the cutting edge, solving problems for customers as they transition from this new, new way of doing things where users have data and power and control, customers are trying to be more authentic, got walled gardens emerging everywhere but that we're supposed to be away from walled gardens. So there's a whole set of new patterns, new expectations and new behaviors. So all this is challenging, but yet it's an opportunity. So I want to get into it. What is your vision? And what's your view on the MarTech today and AI, and how do you guys fit into that, that story? >> Yeah. Great question, John. We are still in the very early innings of where every digital experience is informed, both creatively from the marketing side of our organization, as well as the AI doing the heavy lifting under the herd to be able to create those experience at scale. And I think today every digital customer and every user out there are leaving a trail of very rich, very frequent interaction data with their brands and organizations that they interact with. You know, if you look at each of us, many, many moments and hours of our digital lives are with these interactions that we do on screens and devices, and that leaves a rich trail of data. And brands that are winning, brands that we want to interact with more, have user privacy and user safety at the center of it. And then they build that authentic connection from there on. And, you know, just like when we log into our favorite streaming shows or streaming applications, we want to see things that are relevant to us. They, in some sense, knowing kind of intimately our preferences or changing taste. And how does a brand or organization react to that but still make room for that authentic connection? >> It's an awesome opportunity. And it's a lot of challenges, and it's just starting, I totally agree. Let me ask you a question, Manyam, if you don't mind. How did you guys come up with Blueshift? I know you guys have been in this game before it was fashionable, so to speak, but you know, solving Web 1.0, 2.0 problems. And then, you know, Walmart Labs, everyone knows the history of Walmart and how fast they were with inventory and how they used data. You have that kind of trajectory. When you saw this opportunity, was it like the team was saying, wow, look at this, it's right in our wheelhouse, or, how did you guys get here, and then how did it all come together? >> Yeah, thanks for offering me an opportunity to share our personal journey. You know, I think prior to starting Blueshift with my co-founders, who I worked with for almost the past 20 years of my life, we were at a company called Kosmix, which was a Silicon Valley, early AI pioneer. We were doing semantics search, and in 2011, Walmart started their Silicon Valley innovation hub, Walmart Labs, with the acquisition of Kosmix. And, you know, we went into Walmart Labs, and until then they were already an e-commerce leader. They had been practicing e-commerce for better part of 12 years prior to that, but they're certainly you know, behind, compared to their peers, right? And the peers to be named! (laughs) But, they saw this lack of what it is that they were doing so well in brick and mortar that they're not able to fully get there on the digital side. And, you know, this was almost a decade ago. And when they brought in our team with a lot of AI and data systems at scale, building things at the cutting edge, you know, we went into it a little bit naively, thinking, you know, hey, we are going to solve this problem for Walmart scale in three months. (laughs) But it took us three years to build those systems of engagement. Despite Walmart having an enormous amount of resources being the number one retailer in the world and the data and the resource at their disposal, we had to rethink a lot of assumptions and the trends that were converging were, you know, uses for interacting with them across multiple formats and channels. And both offline and online, the velocity and complexity of the data was increasing. All the marketing and merchandising teams said even a millisecond delay for me is unconscionable. And how do you get fresh data and activated at the moment of experience, without delay, this significant challenge at scale? And that's what we solve for our organizations. >> It really is the data problem. It's a scale problem. It's all that. And then having the software to have that AI predictive and, you know, it's omnichannel when you think about it, in that retail and that brick and mortar term used for physical space and digital converging. And we saw the pandemic pull forward this same dynamic where events and group behaviors and just interactions were all converging. So this line between physical and digital is now blurred, completely blended, the line between customer experience and marketing has been erased, and you guys are the center of this. What does it mean for the customer? Because the customers out there, your customers, or potential customers. They got problems to solved. They're going all digital cloud-native applications, the digital transformation. This is the new normal, and some are on it, are starting it, some are way behind. What are they- What's the situation with the customers? >> Yeah, that's certainly the maturity of, you know, the, each brand and organization along that, you know, both transformation and from transformation to actually thriving in that ecosystem. And how do we actually win, you know, share of mind and then share of, like, that market that they're looking to does take a while. And, and many are, you know, kind of midway through their journey. I think, there was, initially there is a lot of, you know, push towards let's collect all the data that we can but then, you know, how does the actually data becomes something useful that changes experience for Manyam versus John is really that critical moment. And that moment is when, you know, a lot of things come into place. And if I look at, like, the broader landscape, there are certainly lines of powers like Discovery, like Udacity and LendingTree, and Zumper car pods across all these industries. Who would've thought like, you know, all these industries who you would not think of actually as solving a digital engagement problem are now saying that's the key to our success and our growth. >> Yeah. It's absolutely the number one problem. This is the number one opportunity for all businesses, not just verticals here and there, all verticals. So walk me through your typical customer scenario. You know, what are the challenges that they face? You're in the middle of it, you're solving these problems, what are their challenges that they face and how do you guys solve them? >> Absolutely. So I'll talk through two examples, one from a finance industry, one from online learning, you know, o One of our great customers that we partner with is LendingTree. They offer tens of millions of customers' finance products that span from home loans, students loans, auto loans, credits, all of that. And, and let these people come into their website and collect information that is relevant to the loan that they're considering, but engage them in a way for the next period of time. So if you typically think about engagement, it's not just a one interaction, usually that follows a series of steps an organization has to take to be able to explain all their offerings in a way that is digestible and relevant and personalized to each of those millions of customers and actually have them through the funnel and measure it and report on it and make sure that that is the most relevant to them. So in a finance setting that is about consuming credit products, consuming loan products, consuming reporting products in an online context. I'll give you an example of one of our customers, Udacity. Imagine you are a marketing team of two people, and you are in challenged with, how do you engage 20 million students. You're not going to write 20 million communications that are different for each of those students, certainly. I think you need a system to say what did actually all these students come for? How do I learn what they want at this moment in time? What do they want next? If they actually finished something that they started two months ago, would they be eligible for the right course? Maybe today we are talking about self-driving cars. That's the course that I should bring in front of them. And that's only a small segment of the students but someone else maybe on the media and the production side. How do I personalize the experience so that every single step of the way for that student is, you know, created and delivered at scale? And that's kind of the problem that we solve for our brands, which is they have these millions of touchpoint that are, that they have, how do they bring all their data, very fresh and activated at the moment of action? >> So you guys are creating the 10x marketer. I mean, kind of- >> That's right. That's a very (indistinct)- >> 10X engineer, the famous, you're 10X engineer. >> Right. >> You guys are bringing a lot of heavy lifting to short staffs or folks that don't have a data science team or data engineering team. You're kind of bringing that 10x marketing capability. >> Absolutely. I think that's a great way to put it. I call it the mission impossible, which is, you know, you're signing up for the mission impossible, for every marketing team, it's like, now they're like, they are the product managers they're the data scientists, they're the analysts. They are the creator, you know, author, all of that combined into a role. And now you're entrusted with this really massive challenge. And how do you actually get there? And it's that 10x marketer who are embracing these technologies to get there. >> Well, I'm looking forward to challenging though because I can imagine you get a lot of skeptics out there. I don't believe you. It sounds too good to be true. And I want to get to that in the next segment, but I want to ask you about the state of MarTech and AI specifically. MarTech traditionally has been on Web 2.0 standards, DNS, URLs. It's the naming system of the internet. It's the internet infrastructure. So- >> Right. what needs to change to make that scale higher? Does, is there any new abstraction or any kind of opportunities for doing things in just managing you know, tokens that need to be translated? It's hard to do cross to- I mean, there's a lot of problems with Web 2.0 legacy that kind of holds back the promise of high availability of data, privacy, AI, more machine learning, more exposure of data. Can you share your vision on this next layer? >> Absolutely. Yeah, I think, you know, there's a lot of excitement about what Web3 would bring us there in the very early innings of that possibility. But the challenge of, you know, data that leads to authentic experience still remains the same whichever metaverse we might actually interact with a brand name, like, you know, even if I go to a Nike store in the Metaverse, I still need to understand what that customer really prefers and keep up with that customer as they change their preferences. And AI is the key to be able to help a marketer. I call it the, you know, our own group call it like IPA you know, which is ingest all possible data, even from Metaverse, you know, the protocols might change, the formats might change, but then you have to not only have a sense of what happened in the past. I think there are more than enough tools to know what happened. There are only emerging tools to tell you what might happen. How do I predict? So ingest, predict, and then next step is activate. Actually you had to do something with it. How do I activate it, that the experience for you, whether it's Web3 or Web2 changes, and that IPA is kind of our own brew of, you know, AI marketing that we are taking to market. >> And that's the enablement piece, so how does this relate to the customer's data? You guys are storing all the data? Are they coming in? Is there a huge data lake involved? Can I bring in third party data? Does it have to be all be first party? How is that platform-level enabling this new form of customer engagement? >> Absolutely. There's a lot of heavy lifting that the data systems that one has to you know, bring to bear upon the problem, data systems ranging from, you know, distributed search, distributed indexing, low latency systems, data lakes that are built for high velocity, AI machine learning, training model inference, that validation pipeline. And, you know, we certainly leverage a lot of of data lake systems out there, including many of the components that are, you know, provided by our preferred partner, AWS and open source tools. And these data systems are certainly very complex to manage. And for an organization that, with a, you know, 5 to 10 people team of marketers, they're usually short staffed on the, the amount of attention that they get from rest of the organization. And what we have made is that you can ingest a lot more raw data. We do the heavy lifting, but both data management, identity resolution, segmentation, audience building, predictions, recommendations, and then give you also the delivery piece, which is, can I actually send you something? Can I put something in front of the user and measure it and report on it and tell you that, this is the ROI? How do, if all this would be for nothing, if actually you go through all this and there's no real ROI. And we have kind of, you know, our own forester did a total economic impact study with us. And they have found, they have found 781% ROI for implementing Blueshift. And it's a tremendous amount of ROI you get once you are able to reorient your organizations towards that. >> You know, Manyam, one of the problems of being a visionary and a pioneer like you guys are, you're early a lot. And so you must be scratching your head going, oh, the hot buzzword these days is the semantic layer, in Khan, you see snowflake and a bunch of other people kind of pushing this semantic layer. It's basically a data plane essentially for data, right? >> Right. >> And you guys have done that. Been there, done that, but now that's in play, you guys have this. >> That's right. >> You've got all this semantic search built in into the system, all this in data ingestion, it's a full platform. And so I need to ask you how you see this vectoring into the future state of customer engagement. Where, where do you see this intersecting with the organizations you're trying to bring this to? Are they putting more investment in, are they pulling back? Are they, where are, where are they and where are you guys relative to this, this technology? And, and, and, and first of all let's get your reaction to this semantic layer first. >> Right, right. It's a fantastic, you know, as a technologist, I love, you know, kind of the ontology and semantic differences, you know, how, how, you know, data planes, data meshes, data fabrics are put together. And, you know, I saw this, you know, kind of a dichotomy between CIO org and CMO org, right? The CO says like, you know, I have the best data plane, the data mesh, the data fabric. And the CMO says like, but I'm actually trying to accomplish something for this campaign. And they're like, oh, that, does it actually connect the both of pieces? >> So I think, the- >> Yeah? >> The CMO org certainly will need purpose-built applications, on top of the data fabric, on top of the data lakes, on top of the data measures, to be able to help marketing teams both technical and semi-technical to be able to accomplish that. >> Yeah. And then, and the new personas they want turnkey, they want to have it self-service. Again, the 10x marketer is someone with a small staff that can do the staff of hundred people, right? >> That's absolutely- >> So that's where it's going. And this is, this i6s the new normal. >> So, we call them AI marketers. And I think it's a, it's like you're calling a 10x marketer. I think, you know, over time we didn't have, you know this word, business intelligence analyst, but then once the tool are there, then they become business intelligence analysts. I think likewise, once these tools are available then we'll have AI marketers out in the market. >> Well, Manyam, I'd love to do a full, like, one-hour podcast with you. You can go for a long time with these topics given what you guys are working on, how relevant it is, how cool it is right now, and with what you guys have as a team and solution. I really appreciate you coming on the CUBE to chat. For the last minute we have here, give a quick plug for the company, what you guys are up to, size, funding, revenues, what you're looking for. What should people pay attention to? Give the plug. >> Yeah. Yeah, we are a global team, spanning, you know, multiple time zones. You know, we have raised $65 million to date to build out our vision and, you know, over the last eight years of our funding, we have served hundreds of customers and continuing to, you know, take on more. I think, you know, our hope is that over time, the next 10,000 organizations see this as a very much an approachable, you know, problem to solve for themselves, which I think is where we are. AI marketing is real doable, proven ROI. Can we get the next 10,000 customers to embrace that? >> You know, as we always used to say in the kind of web business and search, it's the contextual and the behavioral, you got to bring 'em together here. You got all that technology for the, for the sites and applications for the behavior and converting that contextually into value. Really compelling solution. Thanks for sharing your insight. >> Yeah. Thank you John, really appreciate this. >> Okay, this is CUBE Conversation. I'm John Furrier here in Palo Alto. Thanks for watching. (upbeat music)

>> Welcome to today's session of theCUBE's presentation of the AWS Startup Showcase, The Next Big Thing in AI, Security and Life Sciences. Today featuring Lacework for the security track. I'm your host Natalie Erlich. Thank you for joining us. And we will discuss today how LendingTree automates AWS security for DevOps teams and stays compliant with Lacework. Now we're joined by Adam Leftik the VP of Product at Lacework as well as a Arun Sankaran, CISO of LendingTree. Thank you both very much for joining us today. >> Thank you for having us. >> Well, wonderful. Adam, let's start with you. Lacework positions itself as, "cloud security at the speed of cloud innovation." What does that mean to you and how are you helping your customers? >> Great question, Natalie. I think one of the things that's really important to understand about Lacework really comes back to essentially what's happening at cloud speed, which is customers are aggressively moving more and more of their applications to the cloud, but they're doing so with the same number of resources to secure that environment. And as the cloud continues to grow, both in terms of complexity, as well as overall ability to unlock new styles of applications that were never before even possible without this new technology landscape. Fundamentally, Lacework is designed to enable those builders to go faster without worrying about all the different intricacies and threats that they face out there on the internet. And so the core mission of Lacework is really about enabling builders to build those applications and leverage those cloud resources and new cloud technologies to move quicker and quicker. >> Natalie: Fascinating. >> Yeah, thanks. If you go back to the sort of foundation of the company there we took a very different approach to how we think about security. Often, you know, security approaches in the past have been a rules driven model where you try and think of all the different vectors that attacks can come at. And fundamentally, you end up writing a series of these rules that are impossible to maintain, they atrophy over time, and that you can't possibly think ahead of all these nefarious actors. So one of the things that Lacework did from the very beginning was take a very different approach which is leveraging security as a data problem. And the way we do this is through what we refer to as our polygraph. And the polygraph essentially looks at all the exhaust telemetry that we're able to ingest both from your cloud accounts as well as the underlying infrastructure. And we take that and we build a baseline and a behavioral model for how the application should behave when it's normal. And this baseline represents the state of normalcy. And so then we leverage modern data science techniques to essentially build a model that can identify potential threats without requiring our users to build rules and ultimately play catch up to all the different threats that they face. And this is a really, really powerful capability because it allows our customers both to identify misconfigurations and remediate them, monitor all the activity to reduce the overall overhead on their security organization, and of course help them build faster and identify threats as they come into the system. And we differentiate in lots of different ways as well. So one of the things we're looking to do as part of the overall cloud transformation is really meet the DevOps teams and the security teams where they are. And so all of the information that Lacework captures, synthesizes, and produce through our automation ultimately feed into the different channels that our users are really leveraging that skill today. Whether that's through their ChatOps windows or ultimately into their CICD pipeline so that we give broad coverage both at build time as well as run time and give them full visibility and insights and the ability to remediate those quickly. You know, one of the other things that we're really proud of and this is core to our product philosophy is building more and more partnerships with our customers and LendingTree is really at the forefront of that partnership and we're super excited to be partnering with them. And that's certainly something that we've done to differentiate our product offering and I'd love to hear from Arun, how have you been working with Lacework and how has that been going so far? >> Yeah, thank you, Adam. You know, frankly I think that's a huge differentiator for us. There's a lot of players that can solve technology problems but what we've really appreciated is that as a smaller shop and a smaller organization, the level of connectedness that we feel with the development teams at Lacework. We raise a opportunity. You know, this can make things more efficient for us or this can reduce our time to triage, or this visualization or this UI could be modified to support certain security operations center use cases, maybe that's not what it's designed for. And we've enjoyed just a lot of success in kind of shaping the product in order to meet all the different use cases. And as Adam mentioned, you know, as a CISO, my primary responsibility is security, but frankly there's a lot of DevOps and tech use cases within the polygraph visualization tool, and understanding our environment and troubleshooting has frankly it saved us quite a bit of time and we're looking forward to the partnership to continue to grow out the tool. As we, as a company, scale in today's world, it's very important that we're able to scale our capability 2-3X without a corresponding 2-3X in staff and resources. I think this is the kind of tool that's going to help us get there. >> Well, speaking to you Arun, Lacework has recently grown tremendously and gotten a lot of industry attention but you saw something before everyone else. Can you tell us what really caught your attention? What stood out to you and why you decided to become an early adopter? >> Yeah, great question. Honestly, I wish it was a super tricky kind of answer but the real honest answer is it was a very easy decision because we had a need. We knew that we needed robust monitoring capability and detection of threats within containerized environments. And, you know, there are other players in the space but we have a very diverse environment. We're a combination of multiple container technologies and multiple cloud platforms. And we needed something that had the greatest diversity of coverage across our environments. And this was really the only solution that would work for us. I'd love to be able to say that it was like an aggressive bake-off and there's all these different options. But really, from a capability, and scope, and coverage, it was a fairly easy decision for us. >> And how has your threat detection and investigation process changed since you brought on Lacework? >> Yeah, it certainly has. Our environment within 24 hour period, it might generate 300, 400 million events and that's process level data from hosts and network data access. It's just a very noisy amount of alerts. With the Lacework's platform, those 300, 400 million get reduced to about a hundred alerts a day that we see and of those, five are critical and those tend to all be very actionable. So from an alert fatigue perspective, we really rely on this to give us actionable data, actionable alerts that teams can really focus on and reduces that noise. So I would say that's probably the number one way that our detection process has changed and frankly, a lot of it is what Adam mentioned as far as the underlying self-learning, self-tuning engine. There's not a whole lot of active rules that we had to create or configuration that we had to do. It's kind of a learning system and I think it's really, probably, I would estimate maybe 50-60% reduction in triage and response time for alerts as well. >> And Adam, now going to you, while 2020 was a really rough year for a lot of people, a lot of businesses, Lacework realized 300% revenue growth. So now that the economy is bouncing back and seemingly so in full force, what are your expectations for Lacework in the next year? >> Great question. I think one of the things we're seeing broadly across the industry is an acceleration, a realization that companies that are going through digital transformations have accelerated their pace and so we anticipate even faster growth. Additionally, you know, the companies that may have not been on that trajectory are now realizing that they need to move to the cloud. There's not a lot of folks right now thinking that they're going to be racking and stacking in physical data centers going forward. So we fully expect a continuation of massive growth. And increasingly as customers are moving into the cloud, they're looking for tools to help them build a secure footprint but also enable them to go faster. So, we have a point of view that we're going to continue to see this massive growth and if not, how to accelerate from here. >> Well, you're also the man behind the product. So could you go behind some of the key features that it offers? >> Sure. So, if you think about our overall product portfolio, we really have both breadth and depth. So, first and foremost, most customers who are moving to the cloud or have a large cloud footprint, the first concern they have is, do I have a series of misconfigurations? We really help our customers both identify best practices with those configurations in the cloud, and then also help them move quickly towards potential compliance standards that they need to adhere to. Everyone's operating in a regulated environment these days. And then of course, once you've got that footprint to a place where it's healthy, you really, really want to be able to monitor and track the changes to the configurations over time to ensure you're continuing to maintain that footprint. And so we provide a polygraph based model that essentially identifies potential behavioral risks that we're observing through our data clustering algorithms to help you identify potential holes that you may have created over time and help you remediate those things. And then of course, you know, every customer faces a significant challenge when it comes to just keeping up with the overall landscape changes in terms of overall vulnerability footprint in their environments. And so we have a great capability with what we call vulnerability discovery, which enables our customers to understand where they're vulnerable and not simply tell them how many vulnerabilities they have, but help them isolate, leveraging all the run time and bill time contexts we have so that they can really prioritize what's important to them and what represents the highest risk. And then of course, lastly, you know, where the company really got started is in helping customers protect their cloud workloads. And we do this by identifying threats that we're able to leverage our machine learning and data clustering algorithms so that once we have those baseline behaviors identified and modeled, we can leverage all of our threat intelligence to identify anomalies in that system and help customers really identify those risks as they're coming into the system and deal with those in a really timely manner. So those are kind of the overall key capabilities that they really help teams scale and drive their overall cloud security programs. >> And Arun, really quickly from your perspective, what is a key feature that is really beneficial to LendingTree? >> It's kind of what Adam mentioned with the kind of the self-tuning capability, the reduction of alerts and data based on behavioral-based detection versus rule-based. A lot of people have, you have fancy words, they call AI and machine learning, this and that, but I've rarely seen it work effectively. I think this is a situation where it does work really effectively and does free up time and resources on our side that we can apply to other problems we're trying to solve so I think that's the number one. >> Okay, terrific. Well, I'm really curious Adam. Got to ask you this question. I mean, we saw a really big software IPO last year. What do you think is in store for Lacework? >> Yeah, well, you know, the IPO is just a point in time as opposed to it's part of the journey. Lacework's continuing to invest and really focus on fundamentally changing the security landscape. One of the reasons why I joined Lacework and continue to be really excited about the opportunity comes back to the fundamental challenge that all security tools have. We do not want to create a platform that drives wet blanket behavior, but really fundamentally enables teams like Arun's to move faster and enable the builders to build the applications that fundamentally drive great business outcomes for our customers. And so that's what gets me out of bed. And I think everyone at Lacework is really focused on helping drive great outcomes for our customers. >> Fascinating to hear how Lacework is securing cloud around the world. Lovely to have you on the show. Adam Leftik, the VP of Lacework, as well Arun Sankaran, the CISO of LendingTree. I'm your host for the AWS Startup Network here on theCUBE. Thank you very much for watching.