>> Hey everyone, welcome to theCUBE's coverage of the AWS startup showcase. This is season two, episode three of our ongoing series featuring AWS and its big ecosystem of partners. This particular season is focused on MarTech, emerging cloud scale customer experiences. I'm your host, Lisa Martin, and I'm pleased to be joined by James Fang, the VP of product marketing at mparticle. James, welcome to the program. Great to have you on. >> Thanks for having me. >> Tell us a little bit about mparticle, what is it that you guys do? >> Sure, so we're mparticle, we were founded in 2013, and essentially we are a customer data platform. What we do is we help brands collect and organize their data. And their data could be coming from web apps, mobile apps, existing data sources like data warehouses, data lakes, et cetera. And we help them help them organize it in a way where they're able to activate that data, whether it's to analyze it further, to gather insights or to target them with relevant messaging, relevant offers. >> What were some of the gaps in the market back then as you mentioned 2013, or even now, that mparticle is really resolving so that customers can really maximize the value of their customer's data. >> Yeah. So the idea of data has actually been around for a while, and you may have heard the buzzword 360 degree view of the customer. The problem is no one has really been actually been able to, to achieve it. And it's actually, some of the leading analysts have called it a myth. Like it's a forever ending kind of cycle. But where we've kind of gone is, first of all customer expectations have really just inflated over the years, right? And part of that was accelerated due to COVID, and the transformation we saw in the last two years, right. Everyone used to, you know, have maybe a digital footprint, as complimentary perhaps to their physical footprint. Nowadays brands are thinking digital first, for obvious reasons. And the data landscape has gotten a lot more complex, right? Brands have multiple experiences, on different screens, right? And, but from the consumer perspective, they want a complete end to end experience, no matter how you're engaging with the brand. And in order to, for a brand to deliver that experience they have to know, how the customers interacted before in each of those channels, and be able to respond in as real time as possible, to those experiences. >> So I can start an interaction on my iPad, maybe carry it through or continue it on my laptop, go to my phone. And you're right, as a, as a consumer, I want the experience across all of those different media to be seamless, to be the same, to be relevant. You talk about the customer 360, as a marketer I know that term well. It's something that so many companies use, interesting that you point out that it's really been, largely until companies like mparticle, a myth. It's one of those things though, that everybody wants to achieve. Whether we're talking about healthcare organization, a retailer, to be able to know everything about a customer so that they can deliver what's increasingly demanded that personalized, relevant experience. How does mparticle fill some of the gaps that have been there in customer 360? And do you say, Hey, we actually deliver a customer 360. >> Yeah, absolutely. So, so the reason it's been a myth is for the most part, data has been- exists either in silos, or it's kind of locked behind this black box that the central data engineering team or sometimes traditionally referred to as IT, has control over, right? So brands are collecting all sorts of data. They have really smart people working on and analyzing it. You know, being able to run data science models, predictive models on it, but the, the marketers and the people who want to draw insights on it are asking how do I get it in, in my hands? So I can use that data for relevant targeting messaging. And that's exactly what mparticle does. We democratize access to that data, by making it accessible in the very tools that the actual business users are are working in. And we do that in real time, you don't have to wait for days to get access to data. And the marketers can even self-service, they're able to for example, build audiences or build computed insights, such as, you know, average order value of a customer within the tool themselves. The other main, the other main thing that mparticle does, is we ensure the quality of that data. We know that activation is only as as good, when you can trust that data, right? When there's no mismatching, you know, first name last names, identities that are duplicated. And so we put a lot of effort, not only in the identity resolution component of our product but also being able to ensure that the consistency of that data when it's being collected meets the standard that you need. >> So give us a, a picture, kind of a topology of a, of a customer data platform. And what are some of the key components that it contains, then I kind of want to get into some of the use cases. >> Yeah. So at, at a core, a lot of customer data platforms look similar. They're responsible first of all for the collection of data, right? And again, that could be from web mobile sources, as well as existing data sources, as well as third party apps, right? For example, you may have e-commerce data in a Shopify, right. Or you may have, you know, a computer model from a, from a warehouse. And then the next thing is to kind of organize it somehow, right? And the most common way to do that is to unify it, using identity resolution into this idea of customer profiles, right. So I can look up everything that Lisa or James has done, their whole historical record. And then the third thing is to be able to kind of be able to draw some insights from that, whether to be able to build an audience membership on top of that, build a predictive model, such as the churn risk model or lifetime value of that customer. And finally is being able to activate that data, so you'll be able to push that data again, to those relevant downstream systems where the business users are actually using that data to, to do their targeting, or to do more interesting things with it. >> So for example, if I go to the next Warrior's game, which I predict they're going to win, and I have like a mobile app of the stadium or the team, how, and I and I'm a season ticket holder, how can a customer data platform give me that personalized experience and help to, yeah, I'd love to kind of get it in that perspective. >> Yeah. So first of all, again, in this modern day and age consumers are engaging with brands from multiple devices, and their attention span, frankly, isn't that long. So I may start off my day, you know, downloading the official warriors app, right. And I may be, you know browsing from my mobile phone, but I could get distracted. I've got to go join a meeting at work, drop off my kids or whatever, right? But later in the day I had in my mind, I may be interested in purchasing tickets or buying that warriors Jersey. So I may return to the website, or even the physical store, right. If, if I happen to be in the area and what the customer data platform is doing in the background, is associating and connecting all those online and offline touchpoints, to that user profile. And then now, I have a mar- so let's say I'm a marker for the golden state warriors. And I see that, you know, this particular user has looked at my website even added to their cart, you know, warriors Jersey. I'm now able to say, Hey, here's a $5 promotional coupon. Also, here's a special, limited edition. We just won, you know, the, the Western conference finals. And you can pre-book, you know, the, you know the warriors championships Jersey, cross your fingers, and target that particular user with that promotion. And it's much more likely because we have that contextual data that that user's going to convert, than just blasting them on a Facebook or something like that. >> Right. Which all of us these days are getting less and less patient with, Is those, those broad blasts through social media and things like that. That was, I love that example. That was a great example. You talked about timing. One of the things I think that we've learned that's in very short supply, in the last couple of years is people's patience and tolerance. We now want things in nanoseconds. So, the ability to glean insights from data and act on it in real time is no longer really a nice to have that's really table stakes for any type of organization. Talk to us about how mparticle facilitates that real time data, from an insights perspective and from an activation standpoint. >> Yeah. You bring up a good point. And this is actually one of the core differentiators of mparticle compared to the other CDPs is that, our architecture from the ground up is built for real time. And the way we do that is, we use essentially a real time streaming architecture backend. Essentially all the data points that we collect and send to those downstream destinations, that happens in milliseconds, right? So the moment that that user, again, like clicks a button or adds something to their shopping cart, or even abandons that shopping cart, that downstream tool, whether it's a marketer, whether it's a business analyst looking at that data for intelligence, they get that data within milliseconds. And our audience computations also happens within seconds. So again, if you're, if you have a targeted list for a targeted campaign, those updates happen in real time. >> You gave an- you ran with the Warrior's example that I threw at you, which I love, absolutely. Talk to me. You must have though, a favorite cu- real world customer example of mparticle's that you think really articulates the value to organizations, whether it's to marketers operators and has some nice, tangible business outcomes. Share with me if you will, a favorite customer story. >> Yeah, definitely one of mine and probably one of the- our most well known's is we were actually behind the scenes of the Whopper jr campaign. So a couple of years ago, Burger King ran this really creative ad where the, effectively their goal was to get their mobile app out, as well as to train, you know, all of us back before COVID days, how to order on our mobile devices and to do things like curbside checkout. None of us really knew how to do that, right. And there was a challenge of course that, no one wants to download another app, right? And most apps get downloaded and get deleted right out away. So they ran this really creative promotion where, if you drove towards a McDonald's, they would actually fire off a text message saying, Hey, how about a Whopper for 99 cents instead? And you would, you would, you would receive a text message personalized just for you. And you'd be able to redeem that at any burger king location. So we were kind of the core infrastructure plumbing the geofencing location data, to partner of ours called radar, which handles you geofencing, and then send it back to a marketing orchestration vendor to be able to fire that targeted message. >> Very cool. I, I, now I'm hungry. You, but there's a fine line there between knowing that, okay, Lisa's driving towards McDonald's let's, you know, target her with an ad for a whopper, in privacy. How do you guys help organizations in any industry balance that? Cause we're seeing more and more privacy regulations popping up all over the world, trying to give consumers the ability to protect either the right to forget about me or don't use my data. >> Yeah. Great question. So the first way I want to respond to that is, mparticle's really at the core of helping brands build their own first party data foundation. And what we mean by that is traditionally, the way that brands have approached marketing is reliant very heavily on second and third party data, right? And most that second-third party data is from the large walled gardens, such as like a Facebook or a TikTok or a Snapchat, right? They're they're literally just saying, Hey find someone that is going to, you know fit our target profile. And that data is from people, all their activity on those apps. But with the first party data strategy, because the brand owns that data, we- we can guarantee that or the brands can guarantee to their customers it's ethically sourced, meaning it's from their consent. And we also help brands have governance policies. So for example, if the user has said, Hey you're allowed to collect my data, because obviously you want to run your business better, but I don't want any my information sold, right? That's something that California recently passed, with CPRA. Then brands can use mparticle data privacy controls to say, Hey, you can pass this data on to their warehouses and analytics platforms, but don't pass it to a platform like Facebook, which potentially could resell that data. >> Got it, Okay. So you really help put sort of the, the reigns on and allow those customers to make those decisions, which I know the mass community appreciates. I do want to talk about data quality. You talked about that a little bit, you know, and and data is the lifeblood of an organization, if it can really extract value from it and act on it. But how do you help organizations maintain the quality of data so that what they can do, is actually deliver what the end user customer, whether it's a somebody buying something on a, on a eCommerce site or or, a patient at a hospital, get what they need. >> Yeah. So on the data quality front, first of all I want to highlight kind of our strengths and differentiation in identity resolution. So we, we run a completely deterministic algorithm, but it's actually fully customizable by the customer depending on their needs. So for a lot of other customer data providers, platform providers out there, they do offer identity resolution, but it's almost like a black box. You don't know what happens. And they could be doing a lot of fuzzy matching, right. Which is, you know, probabilistic or predictive. And the problem with that is, let's say, you know, Lisa your email changed over the years and CDP platform may match you with someone that's completely not you. And now all of a sudden you're getting ads that completely don't fit you, or worse yet that brand is violating privacy laws, because your personal data is is being used to target another user, which which obviously should not, should not happen, right? So because we're giving our customers complete control, it's not a black box, it's transparent. And they have the ability to customize it, such as they can specify what identifiers matter more to them, whether they want to match on email address first. They might've drawn on a more high confidence identifier like a, a hash credit card number or even a customer ID. They have that choice. The second part about ensuring data quality is we act actually built in schema management. So as those events are being collected you could say that, for example, when when it's a add to cart event, I require the item color. I require the size. Let's say it's a fashion apparel. I require the size of it and the type of apparel, right? And if, if data comes in with missing fields, or perhaps with fields that don't match the expectation, let's say you're expecting small, medium, large and you get a Q, you know Q is meaningless data, right? We can then enforce that and flag that as a data quality violation and brands can complete correct that mistake to make sure again, all the data that's flowing through is, is of value to them. >> That's the most important part is, is to make sure that the data has value to the organization, and of course value to whoever it is on the other side, the, the end user side. Where should customers start, in terms of working with you guys, do you recommend customers buy an all in one marketing suite? The best, you know, build a tech stack of best of breed? What are some of those things that you recommend for folks who are going, all right, We, maybe we have a CDP it's been under delivering. We can't really deliver that customer 360, mparticle, help us out. >> Yeah, absolutely. Well, the best part about mparticle is you can kind of deploy it in phases, right. So if you're coming from a world where you've deployed a, all in one marketing suite, like a sales force in Adobe, but you're looking to maybe modernize pieces of a platform mparticle can absolutely help with that initial step. So let again, let's say all you want to do is modernize your event collection. Well, we can absolutely, as a first step, for example, you can instrument us. You can collect all your data from your web and mobile apps in real time, and we can pipe to your existing, you know Adobe campaign manager, Salesforce, marketing cloud. And later down the line, let's say, you say I want to, you know, modernize my analytics platform. I'm tired of using Adobe analytics. You can swap that out, right again with an mparticle place, a marketer can or essentially any business user can flip the switch. And within the mparticle interface, simply disconnect their existing tool and connect a new tool with a couple of button clicks and bam, the data's now flowing into the new tool. So it mparticle really, because we kind of sit in the middle of all these tools and we have over 300 productized prebuilt integrations allows you to move away from kind of a locked in, you know a strategy where you're committed to a vendor a hundred percent to more of a best of breed, agile strategy. >> And where can customers that are interested, go what's your good and market strategy? How does that involve AWS? Where can folks go and actually get and test out this technology? >> Yeah. So first of all, we are we are AWS, a preferred partner. and we have a couple of productized integrations with AWS. The most obvious one is for example, being able to just export data to AWS, whether it's Redshift or an S3 or a kinesis stream, but we also have productized integrations with AWS, personalized. For example, you can take events, feed em to personalize and personalize will come up with the next best kind of content recommendation or the next best offer available for the customer. And mparticle can ingest that data back and you can use that for personalized targeting. In fact, Amazon personalize is what amazon.com themselves use to populate the recommended for use section on their page. So brands could essentially do the same. They could have a recommended for you carousel using Amazon technology but using mparticle to move the data back and forth to, to populate that. And then on top of that very, very soon we'll be also launching a marketplace kind of entry. So if you are a AWS customer and you have credits left over or you just want to transact through AWS, then you'll have that option available as well. >> Coming soon to the AWS marketplace. James, thank you so much for joining me talking about mparticle, how you guys are really revolutionizing the customer data platform and allowing organizations and many industries to really extract value from customer data and use it wisely. We appreciate your insights and your time. >> Thank you very much, Lisa >> For James Fang, I'm Lisa Martin. You're watching theCube's coverage of the AWS startup showcase season three, season two episode three, leave it right here for more great coverage on theCube, the leader in live tech coverage.

(upbeat music) >> Hello and welcome to this Cube Conversation. I'm here in Palo Alto, California. I'm John Furrier host of theCUBE, and I'm here with mparticle. With Chee Chew, Chief Product Officer. Thanks for joining us today. Thanks for coming on. >> Thank you. It's great to be here. >> So mparticle's doing some pretty amazing things around managing customer data end to end as a data platform. A lot of integrations. You guys are state of the art cloud scale for this new kind of use case of using the data for customer value in real time. A lot of good stuff going on. So I really want to dig into this whole prospect. So what is the company about first? Take a minute to explain what is mparticle for the folks watching? >> Yeah, absolutely. Well, if you think about the world today where it's like cloud computing and businesses are getting a lot of data from customers as consumers go online. And they have these cloud services that are collecting all this data about the customer. How do you get it organized? How do you have all that data that's in different departments, reconcile them and like give it to your departments. So they can really personalize the experiences. We've all had these experiences where, you know, like we're this loyal customer of a brand, we shop there a lot. And then we go over to like the customer service and they act like they have no idea who we are. Our job is to help businesses really understand the customer and be able to treat them in a personal way. To do the very best for every experience. >> Well, Chee you're in a really big spot there with the company, Chief Product... You got the keys to the kingdom over there. You're overseeing all the action. You got a platform, a bunch of solutions you're enabling. Customer data has been around for a long time. We hear big systems in the past, oh got to leverage the customer data. But why is the customer data more important now than ever as developers and cloud scale are emerging in. Why is customer data becoming more and more valuable to organizations? >> No. Well, customer data has been around for like decades and decades. The amount of customer data being generated online has just accelerated. It's been exponential. There's been more data collected in the past four years than the past 40 years. And like businesses are just starting to realize, how much of a goldmine that could be for them. If they could really harness it. And especially in today's world where treating it properly, respecting people's privacy, really doing well by the customer, earning the right to use that data is ever so important. The combination that brings the need for solutions like mparticle. >> Talk about some of the enablement that you guys offer your customers. You got a platform, you got a lot of moving parts in there. A lot of key components, a lot of integrations. With all the best platforms to connect to. We're in an API economy. So trust is huge. You got to have the data governance. Everything's got to work together. It's a really hard problem. How do you guys enable value there? What is the key product value that you guys are enabling? >> Yeah, it is a hard problem. And with the data being so important to businesses and treating it well and collecting it from all the different aspects, there are many places where we... Our customers really value the services we bring. As you mentioned, we have a large set of integrations. We can get data in from pretty much any system that you have. Even if you built it yourself, we have ways of enabling you to collect that data from all around the company. Then we reconcile them. So we create one single view of the customer. We adhere to all the privacy regulations around the world to make sure that you're compliant with not only laws but with the trust with your consumers. We clean that data and then we distribute it to all the systems where you really want to create personalized experiences. So the collection, the reconciliation, the cleaning, the conformance, and then the distribution. Those are all key events that we do to bring value to customers. >> It's funny in all these major shifts, you're seeing all the same things. You got to be a media company. You got to be a data company. Got to be a video company. Got to be a cloud company. So in the digital transformation, you know with machine learning and AI really at the center of the application value now, you can measure everything in a company. So, smart leadership saying, hey, if we can measure everything, don't we want to know what's going on with respect to our customer. The journey they call it. So, you know, there's the industry taglines of customer best in class experiences, capturing the moments that matter. Describe how you do that. Because moments that matter to me feel like something that's real time or something that's super important, that's contextualized. You got to get that context with that journey. How do you guys do that? This is something I'm intrigued about. >> Yeah, absolutely. And you know, I... This hearken backs to my experience when I was at Amazon doing retail and we really focus on personalization and the notion of when you go to one page or one screen on your mobile device and then you go to the very next page. That very next page has to be personalized with the things that you did on... Just seconds ago on the previous one. That idea of being at the interaction speed, keeping up with the customers. That's what, we've... What we provide for our brands. It's not enough to just collect the data, churn on it, do a bunch of like calculations and then tomorrow figure out what to do. Tomorrow figure out how to personalize it. It has to be in interaction time with our customers. >> John: It's interesting too. You'll have experience in big companies, hyperscalers with large, you know, media business and data. Bringing that to normal companies, enterprises, and mid-market, they have to then stand up their own staff. They have to operationalize this in a large data strategy that maximizes the value. How do brands do this effectively? Can you share best practice of what's the best way to stand up and operationalize the team, the developers, the strategy. >> Chee: Yeah and this is a great question. And right now with the world... The way the world and the industry is developing, businesses don't all do it the same way. Like at Amazon, we built our own. Now we had several hundred engineers in my team who are collecting the data, analyzing it, and really cleaning it. Not every company can afford a couple hundred engineers just to do this... Solve this one problem. Which is why I'm super excited about what we're doing at mparticle, where we're trying to make that available to every company in the world. Whether you're a huge brand, like an NBC, or you're a smaller, medium size startup. Like you have a lot of data and we can help make it accessible for you. Now, many companies do start and build it from scratch and the problems early on, seem very tractable. But then as new laws come out, as the platform changes, as Apple and iOS change the rules on what you can collect and what data you can't collect. That puts you on this treadmill of always like reinvesting and reinvesting in the data collection. And not as much at innovating on your business. And then many companies turn around and decide, oh I understand why you want a company like an mparticle, providing that service. >> It's interesting. You guys do a lot of that... The key value proposition that we hear a lot for successful companies. You take care of that the heavy differentiate... Undifferentiated heavy lifting. So the customer can focus on the value. This seems to be the theme of of the data problem that companies want to solve. There's a lot of grunt work that has to get done. A lot of, you know, get down and dirty and work on stuff. If you can just automate it, make it go faster, then you can apply more creative processes and tools onto getting more growth or more value out of the use case. Can you... Is that something that's happening here? >> Oh yeah, absolutely. You know, the dirty secret that if you talk to any like machine learning scientist data engineer, what they'll tell you is it seems like the world is sexy when you talk to new like computer science students about like building models. But when they go to industry they spend like 80 or 90% of their time cleaning data, getting access to data, like getting the right permissions. And they spend like 10 to 20% of the time actually building models and doing the really interesting things that you want your data science to do. That's a really expensive way of getting to your models. And that's why you're right. Services like, mparticle, like our core business is to take that grunt work and that... Things that might be less exciting and bespoke to your business. Like that's the stuff that we get excited about. And we want to provide the best op... Best in breed experience for our customers. >> Yeah. There's no doubt, every company will have to have this really complex, hard to solve platform problem. You either buy or build it. I mean, you're not... Not everyone's Amazon, right? So not everyone can do that. So you got to have the integrations, you got to have the personalizations, you got to have the data quality and you got to have the data governance in there too. You can't forget the fact that you'd be dealing with potentially trusted parties that don't work for you. Right? So this is a huge connection point that I want to just quickly get into. Quickly, APIs connects companies but now also connects data. How do you view that? How should customers think about the connection points when they start to share customer data with other companies? >> Yeah, you're totally right in that. Not only is it important for you to do this in terms of saving your time in engineering and all the amount of work you have, but the risk is super high. If you treat customers data incorrectly, you can break trust with your consumers. It takes a long time to build that trust and just a moment to lose it. And so it is more than just engineering time savings but it is also a risk to the business. Now... Then you go to down to like, how do you do it? Why APIs? The reason for us, our push on really the API platform is to give power to developers. Within your company, you may have some innovation that you want, some way you want to really differentiate yourself from the rest of the field. If we provided only standard UI. Standard ways of doing it, then our customers would all behave and have the same capabilities as every other customer. But by us providing APIs it allows our customers to really innovate and make the platform bend to their will. To support the unique ideas that they have. So that's our approach of why we really focus on the customer data infrastructure. >> John: Yeah, it's a great opportunity Chee, I really appreciate your time. Real final question for you, as folks look at this opportunity to have a data platform and mparticle, one that you have. They're going to probably ask you the question of, hey I got developers too. I'm hiring more and more cloud native developers. We're API first, obviously we're cloud native. We love that direction. We're distributed computing. All that great stuff at the edge. I got machine learning. But I really want to integrate, I want to control the experience. I want to be agile and fast. Can you help us? What's your answer to that question? >> Absolutely. If you look at the things that your engines are doing, and you ask them how much of what they're doing is similar to what you expect from other similar companies and how much is really unique to your business. You'll probably find that a minority of the work is really unique to that business. And the majority are things that are common problems that other companies struggle with. Our job is to help take that away. So you can really focus on what's unique, bespoke, and innovative for you. >> John: Follow up to that real quick, as you're the Chief Product Officer. Talk to the folks out there who are watching, who may not know what goes on in a product organization. You're making all kinds of trade offs. You got a product roadmap, you've got the 20 mile stare. You have a North Star. What should they know about mparticle, about the product that they... That's important for them to either pay attention to or they may not know about. >> You know, my... When I think about mparticle, it's not just a product but it's the whole offering. And what you want to know about mparticle is we really work hard to empower our customers, whether it's through the API platforms. So that you have the full flexibility to do whatever you want or through our customer service and our support teams. We are... Have a great reputation with our customers about really focusing on and unblocking them, enabling whatever the heart desires. >> John: Yeah and building on top of it. Sounds great. Chee, thanks for coming on. Appreciate the update on mparticle. Thanks for your time. Great to see you. >> Absolutely. Thank you for your time. >> Okay. This is theCUBE conversation. I'm John Furrier, host of theCUBE. Thanks for watching. (upbeat music)