 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 M Particle. James, welcome to the program. Great to have you on. Thanks for having me. Tell us a little bit about M Particle. What is it that you guys do? Sure, so we're M Particle. 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 organize 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 and you mentioned 2013, or even now that M Particle is really resolving as 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 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 for a brand to deliver that experience, they have to know how the customers are 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 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 of 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 feel some of the gaps that have been there in customer 360? And do you say, hey, we actually deliver customer 360? Yeah, absolutely. 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, 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, be able to run data science models, predictive models on it. But the marketers and the people who want to draw insights on it are asking, how do I get it 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 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 average order value of a customer within the tool themselves. The other main thing that MParticle does is we ensure the quality of that data. We know that activation is only as good when you can trust that data, right? When there's no mismatching 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 picture, kind of a topology of a customer data platform. And what are some of the key components that it contains? And then I kind of want to get into some of the use cases. Yeah, so 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 a computer model from a warehouse. And 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 to be able to activate that data, is able to push that data again to those relevant downstream systems where the business users are actually using that data to do their targeting or to do more interesting things with it. So for example, if I go to the next Warriors 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'm a seasoned 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 downloading the official Warriors app, right? And I may be browsing from my mobile phone, but I get distracted. I 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 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, let's say I'm a marketer 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 Western conference finals and you can pre-book, you know, the Warriors championships jersey across our fingers and target that particular user with that promotion. It's much more likely because we have that contextual data that that user is 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 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 the good point. 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 back end. Essentially all the data points that we collect and send to those downstream destinations that happens in milliseconds. So the moment that that user again clicks a button or add something to their shopping cart or even abandon that shopping cart, that downstream tool, whether it's a market or whether it's a business analyst looking at the data for intelligence, they get that data within milliseconds. And our audience computations also happens within seconds. So again, if you have a targeted list for a targeted campaign, those updates happen in real time. You gave, you ran with the Warriors example that I threw at you. I love absolutely. Talk to me, you must have though a favorite real world customer example of MParticles 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 most well-known is we were actually behind the scenes of the Whopper Junior campaign. So a couple of years ago, Berg King ran this really creative ad where the effect of their goal was to get their mobile app out as well as to train all of us back before COVID days. How to order on our mobile devices and 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? Most apps get downloaded and get deleted right away. So they ran this really creative promotion where if you drove towards McDonald's, they would actually fire off a text message saying, hey, how about a Whopper for 99 cents instead? And you would receive a text message personally 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 a partner of ours called Radar, which handles geofencing and then send it back to a marketing orchestration vendor to be able to fire that targeted message. Very cool. I know I'm hungry, but there's a fine line there between knowing that, okay, Lisa's driving towards McDonald's. Let's target her with an ad for a Whopper in privacy. How do you guys help organizations and any industry balance that? Because 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 is 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. And most that second third party data is from the large world gardens, such as like a Facebook or a TikTok or Snapchat. They're literally just saying, hey, find someone that is going to fit our target profile and that data is from people, all their activity on those apps. But with a first party data strategy, because the brand owns that data, we can guarantee that or the brands can guarantee to their customers ethically sourced, meaning it's from their consent. And we also help brands have governance policies. So for example, if a 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 of my information sold, right? That's something that California recently passed with CPRA, then brands can use and particle 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 sale Facebook, which potentially could resell that data. Got it, okay. So you really help put sort of the reins 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, and data is the lifeblood of an organization. If it can really extract the 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 somebody buying something on a e-commerce site 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 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 probabilistic or predictive. And the problem with that is, let's say Lisa, your email changed over the years and a 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 being used to target another user, which obviously should not happen, right? So because we're giving our customers complete control, it's not a black box that'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 have drawn a more high confidence identifier like a hash credit card number or even a customer ID. They have that choice. The second part about ensuring data quality is we actually built in schema management. So as those events are being collected, you could say that, for example, when it's an add to cart events, 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 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 can get a queue, you know, queue is meaningless data, right? We can then enforce that and flag that as a data quality violation and brands can quickly correct that mistake to make sure again, all the data that's flowing through is of value to them. That's the most important part 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 end user side. Where should customers start in terms of working with you guys? 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, maybe we have a CDP, it's been underdelivering, 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 and phase it, right? So if you're coming from a world where you've deployed an all-in-one marketing suite like a sales force in Adobe, but you're looking to maybe modernize pieces of a platform and MParticle can absolutely help with that initial step. So 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 in 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 the new tool with a couple of button clicks and bam, the data is now flowing into the new tool. So MParticle really, because we kind of sit in the middle of all these tools and we have over 300 product ties pre-built integrations allows you to move away from kind of a locked in, you know, strategy where you're committed to a vendor 100% to more of a best of breed agile strategy. And where can customers that are interested go? What's your go-to-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 AWS preferred partner. And we have a couple of product ties 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 product ties integrations with AWS personalized. For example, you can take events, feed them into personalized and personalized will come up with the next best kind of content recommendation or the next best offer available to the customer and MParticle can ingest that data back and you can use that for personalized targeting. In fact, Amazon personalized is what Amazon.com themselves used 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 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 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.