>>Well, good day. And thank you for joining us here on the cube, John Walls here, uh, bringing you to this conversation as part of the AWS startup showcase. And we're joined by Justin bough, who is the SVP of product for amplitude and Justin. Good to see you today. How are you? >>I'm doing great. Thank you for having me, John. >>No pleasure. Looking forward to it. Um, you know, personalization that everybody's talking about these days and then how do we better personalize our, our digital presence, our digital products, um, you know, how do we get much more acutely aware of the end user at the end of the day and grow? I know that's what Amplitude's all about. So maybe if you just give us a 30,000 foot, um, perspective on that, about your thoughts about personalization today and how amplitude tries to affect >>For sure. Yeah. So I think first off personalization matters because it actually works. I think we live in a world where, as you know, we're drowning in content and distraction, uh, and it's been proven that customers respond better to digital experiences that are more personalized, that are more relevant for them. And frankly just save them time. Um, and the nice thing about this is not only the customers benefit, but companies do too. Uh, we actually see that a big impact on a company's bottom line, if they're able to, uh, deliver a more relevant customer experience to them because that leads to better engagement, better return, higher loyalty and lifetime value, uh, for those customers. >>So, um, well, let's, let's just go right to an example then, uh, I know you worked with a lot of different people, um, but there's anybody in particular that stands out, um, maybe give us an idea of a case study here about what practices you put into place, the kind of evaluations that you do, and ultimately the service that you're providing that allows them to increase sales and, and get a little more stickiness with them. >>Yeah, that's great. That's great. So I think one, uh, company customer of ours we're working with right now on this is actually Chick-fil-A. Uh, so people probably familiar with Chick-fil-A. Their mission is to be the most customer caring company in the world, uh, which I love in personalization is critical to that strategy because it helps them create a more relevant and seamless experience for their customers. Um, and the experience itself, and the app is actually pretty simple, which is the magic of personalization. So you open the Chick-fil-A app, uh, you see a list of menu items and those items are relevant to you based on your previous behavior. Um, after you order your entree, you're then offered a list of personalized sides. And then after that Alyssa personalized drinks, um, and the great thing is that as new items, uh, get introduced to the menu by Chick-fil-A you see the ones that are most relevant to you based on predicted affinity and all of the machine learning that we're doing in the background. And so really now Chick-fil-A is actually they're able to deliver a customized menu for everyone that automatically updates based on your behavior and your preferences. Um, and I think the real beauty of this is that they're able to configure all of this by a marketer through a simple UI. This did not require an army of data scientists or engineers. Uh, they're able to use the amplitude platform, uh, to build out this entire experience for their customers. >>Right. Cause I mean, it seems like there'd be an enormous amount of analytics that you have to apply here, right. Um, because you got all this structured and unstructured data, uh, you know, it's, it's all over the place, right. And a lot of times people don't even know what they have on hand. Um, and so you gotta, you gotta help them sift through all this. Right. So let's talk about that process a little bit for somebody who's watching and thinking about, well, that's all sounds well and good, but, but how do you kind of automate this? How do you make it so that we don't have to invest a lot in a team dedicated solely to, you know, sipping through our data and making it valuable for us? >>Yeah. I mean, I think that's the beauty of, uh, of amplitude actually offering this in that that's actually our original first product product analytics. That's what we've done. Um, so we've actually made an out of the box system that can read from all your different data sources. Um, so whether those be your product sources, marketing channels, data that sits in your data warehouse, um, but it's not just piping that data. Uh, we then combine that into a unique identity, uh, profile for that customer, um, across all those different touch points, um, and also have out of the box data governance, um, so that you can make sure you maintain, uh, the quality of that data profile, uh, over time. And then that gets fed into, um, our, what we call our behavioral graph. It's our database, uh, that's actually built to both understand and predict future behavior. And so all of this happens effectively out of the box for our customer. They don't need to do any of this, uh, themselves. Uh, we're managing all this for them. And then what they experience is, uh, an analytics application. So they can analyze that user behavior understand kind of what the drivers of different things like engage in retention are, and then use that to actually personalize the product experience. >>And, and you mentioned machine learning, um, talk about that aspect of this. I mean, how much more capability you have now because of what I know can deliver and, and, um, in some ways it adds some complexity, um, but also obviously it delivers exponentially, I would think in benefit at the end of the day. >>Yeah, for sure. I mean, it's just not possible to do one to one personalization without machine learning. I think that's actually, when we talk about the benefits and the advantages of personalization, it's probably even worth taking a step back. Like there's a lot of different types of personalization. Um, I think when you want to do behavioral personalization where you truly getting to one-to-one experiences, you have to use machine learning. Now you compare that to maybe like demographic personalization, which is actually, I think when most companies talk about when they're doing personalization, they're actually doing demographic personalization. That's like, are you a male or female? Um, what's your, you live in a city or a suburb. Um, uh, but the reality is like that light segmentation, it's not really that effective. Like do all women who live in a city behave the same, obviously not. Uh, and so, uh, we want instead to use behavior because your past behavior is the best predictor of your future behavior. >>Um, and, uh, and you need machine learning to be able to actually come up with, for an individual. What is their likelihood propensity to actually engage on any piece of content of which think about for you think about Chick-fil-A, how many different items they have in a menu. Um, you can think about like, we work with, um, a content company that has millions of different articles and they want to figure out what's the right article to put in front of you. Like, that's just not possible to actually analyze that by hand, uh, nor actually work working straight that, uh, uh, in real time without actually leveraging machine learning. Um, and so that's the exciting thing that's happened with, uh, new advances in, uh, supervisor and supervised learning models that we can actually do those in generalizable ways, uh, for our customers, >>Wait, we've talked a lot about behavioral, so that's obviously metrics you've been tracked. Right. I saw something and I clicked on something and I acted on something or watch something. These are all very measurable activities. On the other hand, though, as you know, in the consumer space, a lot of it's emotion too, you know, I make decisions based on, on my feelings or my thoughts or whatever. Can you, can you do any kind of unpeeling of my motivation in this almost like empathetic, uh, investigation so that you have an idea of what social cues on emanating or sending off? So, Hey, yeah, we can, we can get John this way too. >>Yeah. So I think a lot of it is, I mean, we're talking a lot about the science of, uh, product development, uh, for sure. And how do you bring personalization leveraging data? There is then the art of actually understanding, like what are the emotional States that users are in and like this isn't to say that the ability to personalize the product means that you're not actually bringing the heart as well. Like you act, it actually is a, both about the art and the science coming together. Um, and so you still need to, like, you're still gonna talk to your customers. You're still going to understand, uh, them and kind of what their, uh, different need States are, but this is then taking what you have, which you've built as a great product, then how do you optimize that? So we call it an optimization system, um, and actually deliver, uh, the best experience, uh, based on that customer's behavior. >>So just to kind of flip this a little bit, then what are you doing? Amplitude? What are you doing that, um, that hasn't been done before? I couldn't, I didn't understand that a lot of people think personalization just hasn't has a great horizon, has a lot of great promise. Well, but we're not there yet. I mean, what haven't we delivered on yet that you think amplitude is improving on and refining this capability? >>Yeah. So I think there are a couple of things there as to why we haven't fully seen the promise of personalization deliver no way. And I would say we're really starting to see that chasm emerge, where there are some companies that, you know, you think of, um, you know, Netflix, like obviously Amazon and others, who've done, who've been really successful here, but they've done it through armies of people. Um, what hasn't happened is a self-serve way of doing this so that it does not require massive investments, uh, in technical resources. Um, and so what we've solved for three things, um, one we've already talked about it, but it's just so true. Like this actually in and of itself is not an ML problem. First, it's actually a trustworthy data problem. Do you actually have the behavioral data that you can trust? Can you actually capture that across the entire customer journey because you can't personalize a journey if you don't even know what your users are doing to begin with. >>So you have to start there at that foundational level. Um, and that is a big part of our secret sauce is that we've built a database specifically catered to helping you understand that journey of that customer across all the different platforms and channels that they do. That's not easy to actually unify behavior in that fashion and allow you to analyze that in real time. Um, so that's the first thing that we did, um, is build that, uh, that database. So that's number one. And that's just the foundation. You have to have that, like, I, I think so many companies fail because they think we can go hire ML engineers, but if you don't have the foundation, it's not going to work. Um, the second thing isn't necessarily technological. It's more cultural, but it is really critical. And I think our analytics applications helped, uh, helped a lot here, which is you gotta break down the silos between marketing product engineering and data science. >>You actually have, you have to have all of them working together, um, to really be able to fulfill the promise of personalization because you have to be aligned and what's the outcome we're trying to drive, but that's actually how I literally can walk you through like the, how the, how the actual product works. But the first starting point is what are we trying to accomplish? Like in the Chick-fil-A example, it is, we want people to buy more than one item. Okay. So that's your goal. Like you have to get alignment that that is the goal. Cause if everyone's arguing about different goals, it doesn't matter what ammo model, like the model needs to know what we're trying to actually focus in on. Uh, and so how do you bring people together? And you do that through shared understanding of data. You do that through, we call it a North star, like we're aligned in what is the North star that we're focused on. >>And can you measure that? And that's analytics is focused in on that. And then when you have both of those, you've got behavioral data, you understand the journey of a customer you're aligned in the goals and outcomes you care about. Then you can leverage machine learning to actually deliver that personalized experience. And the advances that we're making there are actually doing that in a generalizable fashion. And so that does not have to be custom built for every single use case. Um, and our models are now able that we can run a model basically, uh, every hour to update for a customer. Um, and that scales horizontally, >>Well, I know of Chick-fil-A certainly has a track record that, um, is an arguable, right? And, and, and you've had a lot to do with satisfying that appetite for success. So, uh, Justin, uh, congratulations to amplitude. It's been a real pleasure speaking with you and thanks for the time today. >>Of course. >>Excellent speaking with Justin Bauer, the senior vice president of product at amplitude, and you've been watching the AWS startup showcase here on the cube.

>>Well, good day, everybody. And it's great to have you with us here on the cube. As we continue our key conversations as a part of the AWS startup showcase, please welcome Jennifer Johnson. Insidery, Jennifer's the chief marketing and strategy officer at amplitude, which is a global leader in product intelligence, and she tells her friends collar JJ. And so today it's still all JJ, how are you doing? I'm doing great, John, how are you doing very well. Thanks for being with us. We appreciate the time. Um, first off, tell us a little bit about amplitude about your work and job for those who might not be familiar. And also, I like to hear a little more about product intelligence about that concept. It's certainly taken on probably a pretty different meaning in this digital world that we're in today. That's right. That's right. Well, so I've been at amplitude. >>I joined in October of 2020. So, uh, not that long. Uh, and let me tell you, I, anyone who knows me knows that I am a CMO, but I am also a category designer. So I look at, uh, I look at companies, I look at opportunities as market creation opportunities, and we're going to talk about that because that's a big reason why I joined amplitude and why I'm so excited for the future of amplitude. Um, and so when we think about our website today says product intelligence. If you read between the lines and I tell you I'm a category designer, you might understand that maybe that will evolve over time, but what product intelligence actually means is it, is it really connects digital products to revenue. And what do I mean by that? And we all know that everything is digital. I don't need to tell you that everything is digital. >>We have the whole world just moved to digital. Um, and it's interesting because we think about digital and we think about the door dashes and the Peloton of the world, but really it's every company and every industry, um, you know, are some of our largest customers are hundred-year-old companies, right? And they have had to not just because of the last year in the pandemic, but they've been really thinking about how do we disrupt ourselves. Really? It's not even about disrupting the industry. It's actually about disrupting their own business around digital. So digital really, isn't a nice to have anymore. It's existential. And we all, I think we all know that at this point. Um, but you know, if the whole world has moved to digital and I think I read something that IDC wrote, we're going to spend $6.8 trillion by 2023 on digital transformation. We're spending an enormous, I mean, I think enormous has even an understatement amount of money on digital. >>So what is the next thing that you have to do once you've spent all this time and money and effort and probably millions of dollars, billions per company actually transforming is you have to actually optimize it and you have to figure out what your, what digital products and digital investments you're making. You have to make sure that actually connect to business outcomes. Things like, uh, revenue, things like lifetime value of things like loyalty, things that drive your business forward. And that's really where product intelligence and the future where amplitude is going is so critical. Because if you think about actually one of our customers said it best the customers of yesterday or the companies of yesterday. They put a website in front of their old way of doing things, their old products, their old way of doing things and call it a digital, like we just put a website in front of it. >>So it's digital. That is no longer the case. Now it's about redesigning your business and transforming value through new digital products and services. So digital products are actually the future of how businesses will operate in the new era. And so what happens is companies say, okay, we need to go build all these new products and services. And we have these goals of growth and revenue, and we hope the revenue comes out the other end, but there's really no way for, or no really effective way for companies to actually figure out how to manage and measure that in between you build a product, you put it out to market. Revenue comes out the other end, but how do you actually know if you're building the right things in the first place? How do you know what, uh, what features, what behaviors, what actions, what combinations of those actually lead to things like engagement and revenue and loyalty, and then how do you actually go and double down on those? >>And what I mean by that is adapting the experience. If you know, something works and you know that every customer that looks like that person will do this and you can predict an outcome. Why wouldn't you serve that up to every single person that looks like that. And really that whole notion of prediction and understanding, and prediction and adapting, that's really where amplitude plays a role. And that's what got me really excited about joining amplitude and really excited about the future is every company is a digital company and really companies have to completely rethink how they manage digital because it isn't just putting a website in front of it anymore. >>Yeah. I mean, you you've hit on something to them. In fact, we've got a lot to unpack here, which is great. Um, but, but you, you talk about that. Digital's lost, right? You got to have it's existential now you're dealing with business, which I think is absolutely correct, but because it's everybody and it is everywhere and you've got a lot of categories, right. Um, as a chief strategy officer, uh, you can't be all things to all people. You can't go off in every which way, but, so how are you focusing then your efforts in terms of identifying the key categories of prime categories, as opposed to looking at this huge landscape, and that could be overwhelming, you know, in some respects, how are you focusing? >>Yeah. I mean, there's, there's two ways to look at it and it is, you know, every company is a digital company, but really any company that has any kind of a digital product or an app digital app, anything that's digital is a as a relevant target for, for amplitude. Um, traditionally we have focused with probably no surprise. We focused on the, probably the, what I'd say the digital native companies, the companies that are more mature, but really they grew up through digitally through digital native. Those are the door dashes, the Postmates, the Uber's, the Lyft's right. Um, and those companies were just built by design to think this way, right? We're building products. Our app is our business. Our product is our business. So we need to make sure that we deeply understand how the interactions with our customers through that experience actually translates. And how do we continue to tweak and test and optimize and digitally native companies tend to understand that inherently. >>So that's been a lot of the early adopters of amplitude have been those digitally native companies. Now what we're seeing and no surprise is there's a really long tail of companies and more traditional industries. I mean, everything from, uh, you know, hospitality and restaurants, obviously media is going through a huge digital disruption right now. Um, automotive, I mean, any, any company that's looking at, how do we build new ways to engage and provide experiences to our customers through any kind of a digital means digital, digital product and app. Those are relevant targets for amplitude. So I think, you know, people think, Oh, it's every, every, uh, industry looks very different, but the commonality is everyone needs to move to digital. And the great thing for amplitude and for the market at large is a lot of our customers are these digitally native, what I would call the thought leaders around digital. And so if we can help bring that, bring those best practices and bring that approach to some of the more traditional companies in traditional industries and help them become more like the Peloton and the door dashes of the world. Then that's great for everybody, >>You know, JJ, when you talk about this transformation that's going on and the spaces in which is going on, which is everywhere right now, I imagine there are still some folks who might be a little reluctant, right? And you talked about slapping the new website and the old material, and they think they're done and they wash your hands and they go away and it's not that simple. Right. Um, so what's that conversation like to people who maybe aren't willing to jump in to take that risk as they see it, whereas, you know, it's an essential to their business. >>Yeah. So, you know, I do think that every disruption, technologically speaking or other is really change management and digital is no different, right? It's not just about moving to digital, it's changing the way that you're organized, it's changing your business structure, your, your strategy priorities. So I think that that organizations know they have to go there now. And even the ones that are reluctant, I'd say, if they're reluctant, they're probably going to get disrupted. So I think everyone understands they need to go there. Our role is really to help organizations get there without, I mean, digital, the, the word that usually follows digital is transformation. And I think a lot of people think that digital transformation needs to be this, you know, three to five year strategic journey and costs millions of dollars with armies of consultants. And really what we're helping to do is help organizations just answer the question, how is our product tied to our revenue? >>And we do that by bringing the data to the teams that actually need it. And it was really, it was really surprising to me to understand the process and some of these really large enterprises around how product and marketing teams, uh, get data. And, and a lot of times, if you have a question about something, if you're a product, if you're a product manager, obviously you want to understand how is our product doing what features are resonating? What features are leading to things like engagement or revenue or subscriptions or loyalty or whatever it is, right. As a marketer, you also want to know that as a marketer, you also want to know what campaigns are we driving that are actually creating value. Are there things that we should be doing? Are there areas we should double down on? And so the process is if you have a question about something or a hypothesis that you want to answer, a lot of times you have to send this request to some centralized data team or a data science team. >>Uh, you know, organizations have, you know, large B2C organizations. Most of them have armies of data scientists and business intelligence platforms. And you send a request and you might get an answer back in a few weeks, maybe a month. And, uh, maybe it's the right answer or usually what happens. And I think we can all relate to this. As you ask a question and you get data back and then it sparks five more questions. And so that whole process is the cyclical thing that I always say, if by the time you actually figure out the answer to your question, it's enough time to get Amazon in the new digital era. And so what we're actually doing is helping to bring that data, which we all know is the crown jewel of any organization. We're bringing that data and we're democratizing it and bringing it to all the teams that actually need it, lock, unlock it from data scientists and BI and bring it to the teams that need it, whether it's product, whether it's marketing, whether it's sales, whether it's customer success. >>And the greatest thing is it's not as a tool for everyone. And then all of a sudden you have these silo tools marketing as their tool product has their tools. CS has their tool is you actually have one platform, one system, and one source of data that all those teams use. So marketing doesn't say, well, yeah, my mind says this and it looks at it from this lens. And product says, well, my data says this, but it looks at it from this lens. All of a sudden you've removed that entire conversation or that entire debate. And that changes everything. It changes the way that companies get insights into customer behavior. It changes the way that they build products. It changes the way that the teams work together, product and marketing can now work off of a common set of data. And so really amplitude is helping to drive that change. >>And you don't have to do it through a three year implementation with an army of consultants that come in. It's something that can be done very easily. And so, and it, you know, I know everyone wants an easy button. Um, it is quite easy though. It's not, it's not the, the three-year or even the one year transformation. It's actually a way to, to bring that data to the teams that need it quickly. Um, the other thing I'd say to it is it's bringing the right data to them. Um, I was reading something from Gardner that said 85% of marketing analytics tools. Now these are tools that usually track things like ad attribution, website visits and how that, you know, how that relates to revenue well in a customer acquisition scenario, while you just want to know what ads actually lead to a cart, uh, put someone going to a cart, someone purchasing that was probably sufficient, but in the, in the new world, that's just not answering the same question. >>Like if you need to add, answer a question of what features, what behaviors, what actions within the product actually drive business outcomes, knowing what ads people clicked on and what web visits that you know, that, that, you know people had. And that's not going to answer it. That's not, it's just answering a totally different question. And 85% of companies are using marketing analytics tools to actually answer questions like what features we need to build. So that's another key point here is companies need to answer this question. They know they do. They just don't have the tools to do it and the data to do it. So they're using tools that were designed for a completely different purpose. And so really that's another great thing about amplitude is we're actually giving them the actual, the right data to answer the questions. >>So if you're, if you're somebody who's headlights, you know, for down the road, then in terms of, you know, you're looking for behavior, straights and patterns, you're looking for increased customer engagements, right. They have all these wonderful tools now, you know, not that you're missing anything, but where do you think that you could even sharpen the pencil a little bit more so that down the road here, what, what do you think technologically, you are capable or you would like to be able to, there's a making that an even richer in case even a bigger, a deeper dig? >>Well, I mean, so we, we have this, this, uh, immense deep, fast, smart database of customer behavior. So if you think of it, it's almost like the possibilities are endless. Anything that you need to be able to know or any question you could ask of your data to know what combinations of features, what combinations of behaviors actually lead to things like retention or churn or revenue. And then you can actually start to model those into cohorts. If I know that a customer does these five things in this order, and they're five times more likely to churn, well, then any customer that actually doesn't just look like that based on your demographics, who you are, where you live, et cetera, but based on actually what you do in the product, we can start to cohort them and say, this person actually looks like this other person based on their behavior. >>And therefore we might actually personalize an experience for them. We might send them an offer if we think they're going to turn, because we know they're likely to turn base cause other people that look like them do, um, or we're not going to send them anything because we already know they're loyal. So they're already likely to buy. So it's answering more questions, but then it's also, how do you actually use that to really personalize experiences? And I, that word is so overused, but in this way, I mean, it's not about I'm going to serve you a piece of content because I know what industry you work in, or I know where you live. I'm actually going to personalize your experience because I know that you, John, as an individual, do these things. And therefore I know that you are either a loyal customer or you've got a high likelihood to churn, et cetera. >>And then I'm going to personalize an experience. That's a good experience for you, but also it could experience for the business. So I think there's more, um, types of analytics. There's more ways to personalize and build experiences. I think in the, in the modern way, not the old demographic way. Um, but also even every organization around the company, like everyone touches the customer. So, you know, customer experience, as we know, is, is, you know, I hate to call it, call it the buzzword. Of course, everybody wants a great customer experience, but everybody talks about customer experience. Anyone who touches the customer as part of customer experience, which is basically the whole company. And so if you think about today, there's obviously product teams, marketing teams are heavy users of, of amplitude, but going forward, I mean, imagine a world where, you know, anytime, you know, anytime you have a touch point with a customer, you can use this, this insight into what they're actually doing in the product to, to get some level of, of intelligence that you didn't have before and use it to proactively give them a better experience, right? >>Whether it's, you know, uh, you know, at renewal time or you know, that they're likely to do something. So you offer something that gives them a better experience or you're in customer service. And wouldn't it be great to actually know if someone's logging a support ticket, what they're actually doing in the product it's going to help you give them a better support experience, et cetera, et cetera. I mean, the options here I think are because of the data that we have and the way that we can, like you said, build these patterns and pattern match, what features and actions lead to outcomes. Uh, I think the options are limitless. And I think this is the new way, like customers, that companies that understand this is the Holy grail of the new way of, of digital and understanding your customers and having this intelligence into the product is the new way to engage the customers that get that are going to be the customers that win. >>What is the new game you're right. I think limitless is a really good word too, because the capabilities that you're developing and the product and services you're providing. Um, so thanks for sharing the time and the insight and pleasure to have you on the queue. Thanks for being here. It's been great. Thank you, John. You've got jumbles here on the cube to conversation on AWS startup showcase. In fact, we have Jennifer Johnson.

(upbeat music) >> Well, good day, everybody. And it's great to have you with us here on the theCUBE. As we continue our CUBE Conversations as a part of the AWS startup showcase. Pleased to welcome Jennifer Johnson in today. Jennifer is the Chief Marketing and Strategy Officer at Amplitude, which is a global leader in product intelligence. And she tells me her friends call her JJ. And so, today it's... Hello JJ, how are you doing? >> I'm doing great, John. How are you? >> I'm doing very well. Thanks for being with us, we appreciate the time. First off, tell us a little bit about Amplitude, about your work in general for those who might not be familiar, and also, I'd like to hear a little more about product intelligence and about that concept, if you will, and how that has certainly taken on probably a pretty different meaning in this digital world that we're in today. >> That's right. Well, so I've been at Amplitude, I joined in October of 2020. So not that long. And let me tell you, anyone who knows me knows that I am a CMO, but I am also a Category Designer. So, I look at companies, I look at opportunities as market creation opportunities. And we're going to talk about that 'cause that's a big reason why I joined Amplitude and why I'm so excited for the future of Amplitude. And so when we think about... Our website today says product intelligence. If you read between the lines and I tell you I'm a category designer, you might understand that maybe that will evolve over time. But what product intelligence actually means is, is that it really connects digital products to revenue. And what do I mean by that? And we all know that everything is digital. I don't need to tell you that everything is digital. The whole world just moved to digital. And it's interesting because, we think about digital and we think about the DoorDashs and the Pelotons of the world, but really it's every company in every industry. Some of our largest customers are 100-year old companies. And they have had to, not just because of the last year in the pandemic, but they've been really thinking about how do we disrupt ourselves, really. It's not even about disrupting the industry. It's actually about disrupting their own business around digital. So digital really, isn't a nice to have anymore. It's existential. And we all, I think we all know that at this point. But, if the whole world has moved to digital and I think I read something that IDC wrote, we're going to spend $6.8 trillion by 2023 on digital transformation. We're spending an enormous, I mean, I think enormous is even an understatement amount of money on digital. So what is the next thing that you have to do, once you've spent all this time and money and effort in probably millions of dollars, billions per company actually transforming, is you have to actually optimize it. And you have to figure out what digital products and digital investments you're making. You have to make sure that they actually connect to business outcomes. Things like, revenue, things like lifetime value, things like loyalty, things that drive your business forward. And that's really where product intelligence and the future where Amplitude is going is so critical. Because if you think about... Actually, one of our customers said it best. The customers of yesterday or the companies of yesterday, they put a website in front of their old way of doing things, their old products, their old way of doing things and called it digital. Like we just put a website in front of it so it's digital. That is no longer the case. Now it's about redesigning your business and transforming value through new digital products and services. So digital products are actually, the future of how businesses will operate in the new era. And so what happens is, companies say, "Okay, we need to go build all these new products "and services. "And we have these goals of growth and revenue "and we hope the revenue comes out the other end." But there's really no way for... Or no really effective way for companies to actually figure out how to manage and measure that in-between. You build a product, you put it out to market, revenue comes out the other end, but how do you actually know if you're building the right things in the first place? How do you know what features, what behaviors, what actions, what combinations of those, actually lead to things like engagement and revenue and loyalty. And then how do you actually go and double down on those? And what I mean by that is adapting the experience. If you know something works, and you know that every customer that looks like that person will do this, and you can predict an outcome, why wouldn't you serve that up to every single person that looks like that? And really that whole notion of prediction and understanding and prediction and adapting, that's really where Amplitude plays a role. And that's what got me really excited about joining Amplitude and really excited about the future is, every company is a digital company and really companies have to completely rethink how they manage digital because it isn't just putting website in front of it anymore. >> Yeah I mean, you've hit on something there. In fact, we've got a lot to unpack here, which is great. But you talk about that digital (mumbles) you got to have. It's existential now to doing your business which I think is absolutely correct. But because it's everybody, and it is everywhere and you've got a lot of categories, as a Chief Strategy Officer, I mean, you can't be all things to all people. You can't go off in every which way, so how are you focusing then in your efforts in terms of identifying maybe key categories or prime categories, as opposed to, looking at this huge landscape, and that can be overwhelming in some respects how are you focusing then? >> Yeah. I mean, there's two ways to look at it. And it is... Every company is a digital company, but really any company that has any kind of a digital product or a digital app, anything that's digital is a relevant target for Amplitude. Traditionally, we have focused with probably no surprise, we focused on the, probably what I'd say the digital native companies, the companies that are more mature, but really they grew up through digital native. Those are the DoorDashs, the Postmates, the Ubers, the Lyfts. And those companies were just built by design to think this way. "We're building products. "Our app is our business. Our product is our business." So we need to make sure that we deeply understand how the interactions with our customers through that experience actually translates, and how do we continue to tweak and test and optimize. And digitally native companies, tend to understand that inherently. So that's been a lot of the early adopters of Amplitude have been those digitally native companies. Now what we're seeing, and no surprise is, there's a really long tail of companies in more traditional industries. I mean, everything from, hospitality and restaurants. Obviously media is going through a huge digital disruption right now. Automotive. I mean, any company that's looking at how do we build new ways to engage and provide experiences to our customers through any kind of a digital means, a digital product, an app, those are relevant targets for Amplitude. So I think people think, "Oh, it's..." Every industry looks very different but the commonality is everyone needs to move to digital. And the great thing for Amplitude and for the market at large is a lot of our customers are these digitally native, what I would call the thought leaders around digital. And so if we can help bring that, bring those best practices and bring that approach to some of the more traditional companies, in traditional industries and help them become more like the Pelotons and the DoorDashs of the world, then that's great for everybody. >> You know, JJ, when you talk about, this transformation that's going on and the spaces in which is going on which is everywhere right now, I imagine there are still some folks who might be a little reluctant. And you talked about slapping a new website on the old material and they think they're done and they wash their hands and they go away. And it's not that simple. So what's that conversation like to people who maybe aren't willing to jump in, to take that "risk" as they see it, whereas you know, it's an essential to their business. >> Yeah. So, I do think that every disruption technologically speaking or other, is really change management. And digital's no different. It's not just about moving to digital, it's changing the way that you're organized. It's changing your business structure, your strategy, your priorities. So, I think that organizations know they have to go there now. And even the ones that are reluctant, I'd say if they're reluctant they're probably going to get disrupted. So I think everyone understands they need to go there. Our role is really to help organizations get there, without... I mean, digital, the word that usually follows digital is transformation. And I think a lot of people think that digital transformation needs to be this, three to five year strategic journey, and cost millions of dollars with armies of consultants. And really what we're helping to do is, help organizations just answer the question, "how is our product tied to our revenue?" And we do that by bringing the data to the teams that actually need it. And it was really surprising to me to understand the process in some of these really large enterprises, around how product and marketing teams get data. And a lot of times if you have a question about something, if you're a product manager obviously you want to understand how is our product doing? What features are resonating? What features are leading to things like engagement or revenue or subscriptions or loyalty or whatever it is. As a marketer you also want to know that. As a marketer you also want to know, what campaigns are we driving that are actually creating value. Are there things that we should be doing? Are there areas we should double down on? And so the process is if you have a question about something or a hypothesis that you want to answer, a lot of times you have to send this request to some centralized data team or a data science team. Organizations have, large B2C organizations. Most of them have armies of data scientists and business intelligence platforms. And you send a request and you might get an answer back in a few weeks, maybe a month and maybe it's the right answer or usually what happens, and I think we can all relate to this. Is you ask a question and you get data back and then it sparks five more questions. And so that whole process is the cyclical thing that I always say, by the time you actually figure out the answer to your question, it's enough time to get Amazoned in the new digital era. And so what we're actually doing is helping to bring that data which we all know is the crown jewel of any organization. We're bringing that data and we're democratizing it and bringing it to all the teams that actually need it. Unlock it from data scientists and BI, and bring it to the teams that need it, whether it's product, whether it's marketing, whether it's sales, whether it's customer success. And the greatest thing is it's not a tool for everyone. And then all of a sudden you have these siloed tools, marketing has their tool, product has their tool, CS has their tool. Is you actually have one platform, one system, and one source of data that all those teams use. So marketing doesn't say, "Well yeah, my mind says this "and it looks at it from this lens." And product says, "Well, my data says this, "but it looks at it from this lens." All of a sudden you've removed that entire conversation or that entire debate. And that changes everything. It changes the way that companies get insights into customer behavior. It changes the way that they build products. It changes the way that the teams work together. Product and marketing can now work off of a common set of data. And so really Amplitude is helping to drive that change. And you don't have to do it through a three-year implementation with an army of consultants that come in. It's something that can be done very easily. And so, I know everyone wants an easy button. It is quite easy though. It's not the three-year or even the one-year transformation. It's actually a way to bring that data to the teams that need it quickly. The other thing I'd say to it is, it's bringing the right data to them. I was reading something from Gartner that said, 85% of marketing analytics tools, now these are tools that usually track things like ad attribution, website visits, and how that relates to revenue. Well in a customer acquisition scenario, well, you just want to know what ads actually lead to a cart. Put someone going to a cart, someone purchasing that was probably sufficient, but in the new world, that's just not answering the same question. Like if you need to answer a question of what features, what behaviors, what actions within the product actually drive business outcomes, knowing what ads people clicked on and what web visits that people had, that's not going to answer... It's just answering a totally different question. And 85% of companies are using marketing analytics tools to actually answer questions like what features, do we need to build? So that's another key point here is, companies need to answer this question. They know they do. They just don't have the tools to do it and the data to do it. So they're using tools that were designed for a completely different purpose. And so really that's another great thing about Amplitude, is we're actually giving them the actual, the right data to answer the questions. >> So, if you're somebody's headlights, for down the road, then in terms of, you're looking for behavior, straights and patterns. You're looking for increased customer engagements, and you have all these wonderful tools now, not that you're missing anything, but where do you think that you could even sharpen the pencil a little bit more so that down the road here, what do you think technologically you are capable or that you would like to be able to deliver, because of making that an even richer engagement, even a bigger, a deeper dig. >> Yeah. Well, I mean, so, we have this immense deep, fast, smart database of customer behavior. So if you think of it, it's almost like the possibilities are endless. Anything that you need to be able to know or any question you could ask of your data to know what combinations of features, what combinations of behaviors actually lead to things like retention or churn or revenue. And then you can actually start to model those into cohorts. If I know that a customer does these five things in this order, and they're five times more likely to churn, well then, any customer that actually, doesn't just look like that based on your demographics, who you are, where you live, et cetera, but based on actually what you do in the product. We can start to cohort them and say, "this person actually looks like this other person "based on their behavior." And therefore we might actually personalize an experience for them. We might send them an offer if we think they're going to churn because we know they're likely to churn base 'cause other people that look like them do. Or we're not going to send them anything because we already know they're loyal. So they're already likely to buy. So it's answering more questions, but then it's also, how do you actually use that to, really personalize experiences? And that word is so overused, but in this way, I mean, it's not about I'm going to serve you a piece of content because I know what industry you work in, or I know where you live. I'm actually going to personalize your experience because I know that you, John, as an individual, do these things and therefore I know that you are either, a loyal customer, or you've got a high likelihood to churn, et cetera. And then I'm going to personalize an experience, that's a good experience for you but also a good experience for the business. So, I think there's more types of analytics. There's more ways to personalize and build experiences. I think in the modern way, not the old demographic way. But also, even every organization around the company, like everyone touches the customer. So, customer experience as we know is, I hate to call it the buzzword. Of course, everybody wants a great customer experience but everybody talks about customer experience. Anyone who touches the customer is part of customer experience, which is basically the whole company. And so if you think about, today, there's obviously product teams, marketing teams, are heavy users of Amplitude. But going forward, I mean, imagine a world where, anytime you have a touch point with a customer, you can use this insight into what they're actually doing in the product to get some level of intelligence that you didn't have before, and use it to proactively give them a better experience. Whether it's, at renewal time, or you know that they're likely to do something so you offer something that gives them a better experience or you're in customer service. And wouldn't it be great to actually know if someone's logging a support ticket. What they're actually doing in the product is going to help you give them a better support experience, et cetera, et cetera. I mean, the options here I think are, because of the data that we have and the way that we can, like you said, build these patterns and pattern match what features and actions lead to outcomes, I think the options are limitless. And I think this is the new way. Like companies that understand this is the Holy grail of the new way of digital and understanding your customers and having this intelligence into the product is the new way to engage, the customers that get that are going to be the customers that win. >> Well, it is a new game, you're right. I think limitless is a really good word too because the capabilities that you're developing and the product and services you're providing, really are limitless. So thanks for sharing the time and the insight, a pleasure to have you on theCUBE. Thanks for being here. >> Thank you. It's been great. Thank you, John. >> You've got John Walls here on theCUBE, CUBE Conversation on the AWS startup showcase. I'm talking with Jennifer Johnson from Amplitude. (soft music)

(upbeat techno music) >> Well, good day. And thank you for joining us here on theCUBE. John Walls here, bringing you this conversation as part of the AWS Startup Showcase. And we're joined by Justin Bauer, who is the SVP of Product for Amplitude. And Justin, good to see you today. How are you doin? >> I'm doing great. Thank you for having me, John. >> Oh, you beat, no, a pleasure. Looking forward to it. You know, personalization. That's what everybody's talking about these days, and how do we better personalize our our digital presence, our digital products, you know, how do we get much more acutely aware of the end-user at the end of the day and grow? I know that's what Amplitude's all about. So maybe if you'd just give us a 30,000 foot perspective on that, about your thoughts about personalization today and how Amplitude tries to affect that. >> For sure, yeah. So I think, first-off, personalization matters because it actually works. I think we live in a world where, as you know we're drowning in content and distraction and it's been proven that customers respond better to digital experiences that are more personalized, that are more relevant for them. And frankly just save them time. And the nice thing about this is not only the customers benefit, but companies do too. We actually see that a big impact on a company's bottom line, if they're able to deliver a more relevant customer experience to them, because that leads to better engagement, better return (audio crackling drowns out speaker) and higher loyalty and lifetime value for those customers. >> So, well, let's just go right to an example then. I know you worked with a lot of different people. If there's anybody in particular that stands out, maybe give us an idea of a case study here about what practices you put into place, the kind of evaluations that you do, and ultimately, the service that you're providing that allows them to increase sales and get a little more stickiness with their customer. >> Yeah, that's great, that's great. So I think one company, a customer of ours we're working with right now on this, is actually Chick-fil-A. So people probably familiar with Chick-fil-A. Their mission is to be the most customer-caring company in the world, which I love. In personalization, it's critical to that strategy because it helps them create a more relevant and seamless experience for their customers. And the experience itself in the app is actually pretty simple, which is the magic of personalization. So you open the Chick-fil-A app, you see a list of menu items, and those items are relevant to you based on your previous behavior. After you order your entree, you're then offered a list of personalized sides. And then after that, a list of personalized drinks. And the great thing is that as new items get introduced to the menu by Chick-fil-A, you see the ones that are most relevant to you, based on predicted affinity, and all of the machine learning that we're doing in the background. And so really now Chick-fil-A is actually, they're able to deliver a customized menu for everyone that automatically updates based on your behavior, your preferences. And I think the real beauty of this is that they're able to configure all of this by a marketer through a simple UI. This did not require an army of data scientists or engineers. They're able to use the Amplitude platform to build out this entire experience for their customers. >> Right? Cause I mean, it seems like there'd be an enormous amount of analytics that you have to apply here, right? That because you got all this structured and unstructured data, ya know, it's all over the place, right? And a lot of times people don't even know what they have on hand. And so you got to help them sift through all this, right? So let's talk about that process a little bit for somebody who's watching and thinking about, "Well, that's all sounds well and good, "but how do you, kind of, automate this? "How do you make it so "that we don't have to invest a lot "in a team dedicated solely to, ya know, "sifting through our data "and making it valuable for us?" >> Yeah. I mean, I think that's the beauty of of Amplitude actually offering this in that that's actually our original first product, Product Analytics. That's what we've done. So we've actually made an out-of-the-box system that can read from all your different data sources. So whether those be your product sources, marketing channels, data that sits in your data warehouse. But it's not just piping that data. We then combine that into a unique identity, a profile for that customer, across all those different touch points, and also have out-of-the-box data governance so that you can make sure you maintain the quality of that data profile over time. And then that gets fed into our, what we call our behavioral graph. It's our database that's actually built to both understand and predict future behavior. And so all of this happens effectively out of the box for our customer. They don't need to do any of this themselves. We're managing all this for them. And then what they experience is an analytics application. So they can analyze that user behavior, understand kind of what the drivers of different things like engagement retention are, and then use that to actually personalize the product experience. >> And you mentioned machine learning. Talk about that aspect of this. I mean, how much more capability you have now because of what ML can deliver. And in some ways it adds some complexity but also, obviously, delivers exponentially, I would think, in benefit and value at the end of the day. >> Yeah, for sure. I mean, you, it's just not possible to do one-to-one personalization without machine learning. I think that's actually, when we talk about the benefits and the advantages of personalization, it's probably even worth taking a step back. Like, there's a lot of different types of personalization. I think when you want to do behavioral personalization, where you're truly getting to one-to-one experiences, you have to use machine learning. Now, you compare that to maybe like demographic personalization, which is actually, I think, when most companies talk about when they're doing personalization, they're actually doing demographic personalization. That's like, "Are you a male or female? "What's, do you live in a city or a suburb?" But the reality is like, that light segmentation, it's not really that effective. Like, do all women who live in a city behave the same? Like, obviously not. (laughs) And so we want instead to use behavior, because your past behavior is the best predictor of your future behavior, and you need machine learning to be able to actually come up with, for an individual, what is their likelihood, propensity, to actually engage on any piece of content? Of which, think about, for, you can think about Chick-fil-A, how many different items they have in a menu? You can think about, like, we work with a content company that has millions of different articles, and they want to figure out what's the right article to put in front of you. Like, that's just not possible to actually analyze that by hand nor actually orchestrate that in real time without actually leveraging machine learning. And so that's the exciting thing that's happened with new advances in supervised and unsupervised learning models. That we can actually do those in generalizable ways for our customers. >> We've talked a lot about behavioral, so that's obviously metrics you can track, right?. I saw something, I clicked on something. I acted on something and watched something. These are all very measurable activities. On the other hand, though, as you know in the consumer space, a lot of it's emotionally driven too. Ya know, I make decisions based on my feelings or my thoughts or whatever. Can you, can you do any kind of unpeeling of my motivation in this? Almost like empathetic investigation so that you have an idea of what social cues I'm emanating, or I'm sending it off, say, "Hey, yeah, we can "we can get John this way too." >> Yeah. So I think a lot of it is, I mean, we're talking a lot about the science of product development, for sure, and how you bring personalization leveraging data. There is then the art of actually understanding. Like, what are the emotional states that users are in? And like, this isn't to say that the ability to personalize the product means that you're not actually bringing the art as well. Like you act, it actually is about both the art and the science coming together. And so you still need to, like, you're still going to talk to your customers. You're still going to understand them and kind of what their different need-states are, but this is then taking what you have, which you've built as a great product, then how do you optimize that? That's why we call it an optimization system. And actually deliver the best experience, based on that customer's behavior. >> So just to kind of flip this a little bit then, what are you doing, Amplitude, what are you doing that hasn't been done before? I can, I understand that a lot of people think personalization just hasn't, has a great horizon, has a lot of great promise. Well, but we're not there yet. I mean, what haven't we delivered on yet that you think Amplitude is improving on and refining this capability? >> Yeah. So I think there are a couple things there as to why we haven't fully seen the promise of personalization deliver. Though we, and I would say, we're really starting to see that chasm emerge, where there are some companies that you know, you think of, you know, Netflix, like, obviously, Amazon and others, who've done, who've been really successful here. But they've done it through armies of people. What hasn't happened is a self-serve way of doing this so that it does not require massive investments in technical resources. And so what we've solved for are three things. One, we've already talked about it, but it's just so true. Like, this actually in and of itself is not an ML problem first, it's actually a trustworthy data problem. (chuckles) Do you actually have the behavioral data that you can trust? Can you actually capture that across the entire customer journey? Cause you can't personalize a journey if you don't even know what your users are doing to begin with. So you have to start there at that foundational level. And that is a big part of our secret sauce is that we've built a database specifically catered to helping you understand that journey of that customer across all the different platforms and channels that they do. That's not easy to actually unify behavior in that fashion and allow you to analyze that in real time. So that's the first thing that we did, is build that database. So that's number one. And that's just the foundation. You have to have that, like I said I think so many companies fail because they think, "We can go hire ML engineers." But if you don't have the foundation, it's not going to work. The second thing isn't necessarily technological, it's more cultural, but it is really critical. And I think our analytics application has helped a lot here, which is you've got to break down the silos between marketing, product, engineering, and data science. You actually have, you have to have all of them working together to really be able to fulfill the promise of personalization because you have to be aligned on, "What's the outcome we're trying to drive?" Like, that's actually how, I literally can walk you through like the, how the actual product works. But the first starting point is, "What are we trying to accomplish?" (chuckles) Like, in the Chick-fil-A example, it is, "We want people to buy more than one item." Okay, so that's your goal. Like, you have to get alignment that that is the goal. Cause if everyone's arguing about different goals, it doesn't matter what ML model, like the model needs to know what we're trying to actually focus in on. And so how do you bring people together? And you do that through shared understanding of data. Like you do that through, we call it a North Star. Like, "We're aligned and what is the North Star that we're focused on?" And can you measure that? And that's analytics, is focused in on that. And then when you have both of those, you've got behavioral data, you understand the journey of a customer, you're aligned on the goals and outcomes you care about. Then you can leverage machine learning to actually deliver that personalized experience. And the advances that we're making there are in actually doing that in a generalizable fashion. So that does not have to be custom built for every single use case. And our models are now able, that we can run a model, basically, every hour to update for a customer, and that scales horizontally. >> Well, I know Chick-fil-A certainly has a track record. That is inarguable, right? And, and you've had a lot to do with satisfying that appetite for success. So Justin, congratulations to Amplitude. It's been a real pleasure speaking with you and thanks for the time today. >> Of course, no, it's been great, thank you for having me. >> Excellent, speaking with Justin Bauer, the Senior Vice President of Product at Amplitude. And you've been watching the AWS Startup Showcase here on theCUBE. (soft marimba-techno music)