The Data Stack Show - 126: Crossing the Product Analytics Chasm with Spenser Skates of Amplitude Analytics

Episode Date: February 15, 2023

Highlights from this week’s conversation include:Spenser’s journey to Co-Founding Amplitude (3:02)Looking back over the last decade of success at Amplitude (8:31)Going from Engineer to Sales (14:41)Comparing product analytics and general analytics (20:11)How cloud data warehousing has impacted analytics (31:38)Providing an out-of-the-box experience for consumers (41:12)Final thoughts and takeaways (54:27)The Data Stack Show is a weekly podcast powered by RudderStack, the CDP for developers. Each week we’ll talk to data engineers, analysts, and data scientists about their experience around building and maintaining data infrastructure, delivering data and data products, and driving better outcomes across their businesses with data.RudderStack helps businesses make the most out of their customer data while ensuring data privacy and security. To learn more about RudderStack visit rudderstack.com.

Welcome to the Data Stack Show. Each week we explore the world of data by talking to the people shaping its future. You'll learn about new data technology and trends and how data teams and processes are run at top companies. The Data Stack Show is brought to you by Rudderstack, the CDP for developers. You can learn more at rudderstack.com. Welcome to the Data Stack Show. We have an exciting episode. We're going to talk with Spencer Skates, the co-founder and CEO of Amplitude, arguably the most successful sort of SaaS product analytics company, you know, of that competitive set. They went public in late 2021, and it was a sort of a decade-long run for them. And Kostas,

I have so many questions. I think one of the big questions that I have is, as you think about sort of data infrastructure, you and I work at companies that provide sort of, let's say, like more core data infrastructure out of the hood. Amplitude has a, you know, provide sort of, let's say, like more core data infrastructure out of the hood. Amplitude has a, you know, the bread and butter of the product is an interface, yet there's a huge amount of infrastructure underneath it that is a product in its own right. And that we know from personal experience is pretty, pretty stinking hard to build and maintain. And so I want to ask Spencer about that, right? Because ultimately, the aha moment that I think they want their users to have hides like how much is actually happening under the hood. You know, and I imagine that's an interesting balance,

you know, when you're building a company. So if we can get to it, that's my burning question. How about you? Oh, I have plenty of burning questions, but I think it's the first time that we have a founder here who has gone code to the CEO, to the founder, to someone who successfully has taken like a company public, right? So yeah, outside of like, of course, we are going to have like a more like product and technical conversation with him. But I think like it's important to seize the opportunity here and also learn a little bit more about like the journey that he has because it's a pretty unique and rare one, so that's definitely something I'd like to learn more about. Henry Suryawirawan, Absolutely.

Well, let's dig in and find out all about it with Spencer. Spencer, welcome to the Data Stack Show. Privileged to have you on and so excited to chat. Eric, Kostas, fantastic to meet you both. Really excited to be here. I think it's an incredibly exciting time in the data space generally. We just love being part of it here at Amplitude. And so, yeah, really excited to talk about perspectives on where we think it's going and everything else.

Absolutely. Well, so much to discuss based on our quick touch base before the show. But first, give us your background. So what led to you co-founding the Implitude? Yeah, so I graduated from MIT in 2010 and was convinced that building a technology company was the best way to have an impact on the world and build the career and a whole bunch of other things. And so I ended up starting a few unsuccessful companies. One of them, Sonalite, was a voice recognition company that allowed you to talk to your phone, very similar to Siri, and send and receive text messages.

And one of the things that happened when we were building it was that we wanted to know how customers were using that product, what they liked and don't like, and what led to the long-term engagement on the product. And it was obvious to us that you should look at customer data in order to make that determination. And it was interesting at the time we looked at what was out there on the market and every analytics tool was very marketing centric and marketing focus is all about, you know, what traffic sources are, are leading to people to jump onto your webpage or where are they coming from? And very little of it or none of it allowed you to stand the journey from a product standpoint. And we said, Hey, we need to go build this. So we ended up building something in-house. And what was interesting is that ended up being much more successful than the product itself. Lots of

other companies we show that to be like, holy cow, I want these same exact insights for my product. At the same time, Facebook came out with a very famous study that they found the best predictor of long-term engagement on the platform was how many friends you added. So if you added seven or more friends in the first 10 days, you had an 85% chance of sticking around at least two months. And if you didn't get to that threshold, the chance was less than half that. So that was the single biggest determinant of your long-term success as a Facebook user. And what was interesting is nothing on the market could answer that same sort of question at the time. So we said, we need to go build this. And so that was the start of Amplitude in 2012.

Started it with my two co-founders, Curtis and Jeffrey. Launched the company in 2014. We've been growing like crazy since. We took it to the public markets in 2021 and then are continuing to grow. Today, we work with a lot of the best companies and most forward-thinking product-led companies in tech, whether that be Atlassian, Intuit, DoorDash, PayPal, Square. You know, you look at the list and we work with them. And so now, but it's still very early days in the category.

And so the question we're trying to answer and figure out is how does this whole thing play out over the next decade and what does the future look like? Sure. Okay, I have a question. So when you're at Sonolite and you sort of built this like MVP product analytics, you know, product in a sense to like try to answer your own questions, like just curiosity, like how did you build that? Were you just like, you know, lay some sort of interface on top of your production database? Or like, what was the like sort of MVP that people are like, wow, that's awesome. Yeah.

So we tracked a bunch of data through it. Believe it or not, we actually use SQLite. That's a database that many users. So it's the simplest to just set up and start tracking data. Sure, sure. And we started to answer all these questions around. We wanted to know that the key question that we had was, how much does an improvement in the voice accuracy,

the accuracy of the voice recognition lead to more long-term engagement? Turns out massively impactful. It's like, I think a 1% increase in voice recognition accuracy would translate to a 1% increase in long-term retention. So it's very important. And what was interesting though, is we were at fighting SQL in order to do this. SQL's really poor at answering

what I think of as time series or sequential questions because you have to do joins and you're kind of doing this, like it's like you have multiple levels of nesting for every single additional join you want to do. And so, but anyway, we managed to hack it together as a thing. And we've, you know, looked at a retention curve and we looked at how the accuracy of whether you are successful in your first match changes that it was, you know, it was very,

very impactful as we found out. And we, we showed kind of a few charts on this to other people and they're like, Oh, Holy cow. We want this exact same sort of thing. Cause this is the exact thing you want to answer. And so when it came time to actually build it for amplitude, we, you know, we ended up having to rebuild the whole thing and it was more generalized and interactive and works for any use case and they said to questions and all of that sort of stuff. But it really started by us seeing the power of those insights and seeing other people really wanted those exact same things from a product

lens. Yeah, yeah, that's super interesting. It is. Yeah, I mean, I think it says a lot about your motivation to answer those questions, because that's some pretty like gutsy sequel and like a lot of work on like what is probably ultimately like a fairly brittle system that that produce all these insights but uh that's some commitment my actually just a side note my challenge for anyone listening is if you think sql can be the long-term answer on this stuff just try building a basic funnel out of sql just try doing that let me know how it goes yeah you know and then get that but yeah sql is great for a whole bunch of things on transactional data. But when it comes to looking, mapping out user journeys, that there's a whole bunch where it falls down pretty quick.

Yeah, I guess pretty complex. Okay, we have so many topics to get to, but so congrats on the Amplitude IPO, you know, decade of, you decade of passionate work. I'm interested to know, as you reflect on that experience, what would you tell the Spencer Skates of 2012 when you first started the company, looking a decade back? What would you tell that version of Spencer Skates, sort of knowing what you know now? I actually, what's interesting, wouldn't do that much different. For sure, we could have saved some time in terms of moving on. We probably could have moved on from Zonelight faster. We could have. One of the things in the early days that I didn't do well was asking for money.

I ended up wasting a lot of time talking to people, customers who would use it for free, but not actually be interested in paying or not valued enough to pay us. We wasted about a year on that before I actually started. Before I felt confident enough in our product to ask for money, I would have done it maybe three or six months into that would have been the change. But other than that, you know, I wouldn't have changed that much in the really early days. I think one of the big, once I figured out that I should go ask money for the product, we ended up accelerating like crazy. So we launched in 2014. And right away, I would basically not spend time on a bunch of customers who didn't want to pay anyway. And you know, you're arguing about $50 a month with two customers who are willing to pay you thousands of dollars a month for the product. And it was like, that was a, such a great forcing function to get us to spend time on problems that mattered when customers that mattered and accelerated, really focused and accelerated our development tremendously. And so, so that was a big kind of moment in 2014, where I switched full time from

building and being the product person to being the first salesperson. So that was a really big change. And then, you know, after that, I think we hired executive team, we kind of grew it out. I think I definitely would have pushed myself to do that earlier and figure it out earlier. Hiring executives, there's so much knowledge about organization building and how to build, how to do that well that's out there that you don't have if you're a first-time founder CEO. And it's much more valuable if you can bring that in from the outside to leverage and then grow the company. Now that leads to all sorts of other problems. Like how do you make sure to maintain the culture instead of values you want?

You know, execs come with all sorts of problems like need to be highly paid and incentive as differently and all of that. Sure. It can be. It is a real accelerant. There is versus if you're trying to figure out how to build an organization yourself. The other comment I'd make is that, you know, at the rate a lot of these early companies are growing where you're doubling or tripling every year, that's a very unnatural thing for an

organization to go through. Like if you're growing maybe 10% every year, that's more natural. That's how most groups of people, you know, self-organizing and change over time. But if you're tripling, that's like no- It's a different company every three to six months. Exactly. New company. And so you have to get help to set that up for success. So I would have pushed myself to do that earlier. But I think the fact that we did make those changes and adjustments after not too long, first in terms of me focusing from being an engineer to being a salesperson, incredibly important. And then from hiring outside help and executives who had done and seen

different parts of the journey before, you know, both of those were, those are the biggest thing I'd say, you know, engineering centric founders get wrong is appreciation for salespeople and a lack of appreciation for what great executives can bring. And, you. And they try to reinvent it and do it their own way. They're like, do the Google work. Hey, we're not going to have managers. Turns out managers actually serve a really important function. Someone needs to make the call of who is not confident, not on the team.

And same thing with sales. It's like one of the really interesting thing to me about sales was that you have this image in your head, it's at least a used car salesman. That's a lot of your experience, but great salespeople. It's all about matching up opportunity of where there's a problem and pain that your company can solve in the market and aligning two organizations on that. Now they come with their own set of differences and that like the way they do work is very different. They do work by customers. They're very incentivized by cash compensation. So it's a very different sort of mentality. But if you can do that well, then you can build a great business around. I've seen too many great engineers that build beautiful products

that don't have enough of an appreciation of the importance of that to the success of the company and ended up failing. Yeah. Yeah. Super interesting. Was it hard for you being so focused on the product to let some of that stuff go and hand off that responsibility? Or did you stay involved for a while? Because especially in the early days, that's a huge part of you especially in the early days, like, you know, that's a huge, you know, sort of, you know, part of you goes into the product in many ways. Yeah, I was lucky in that I had two really strong co-founders in Curtis and Jeffrey. And what would happen is we meet weekly to talk through different things on the engineering or

product side. And I would push on different areas. It's like, hey, if we consider doing the segmentation report like this way, or have we considered processing data this way? And they would say, oh, we already thought through that, you know, and it's bad for these reasons. And we came up with this better solution. After like five or six times of that, it's like, okay, you guys clearly got it. Like, I'm not going to spend more time on this, you know, you guys would ride there.

Yeah. Yeah, for sure. No, that's great. So, yeah, obviously, you know, we, you know, I'd have ways to keep up to speed with what was going on on the product side. And, you know, we'd have weekly check-ins and syncs, but 90% of the time was then like, I'm just going to focus on customers and sales and driving that part of the business. Spencer, I have a question for you.

You mentioned that you went like from beginning to like got into sales, actually, like you had to do that, right? Tell us a little bit more about like this experience, how you experienced this transition and share with us like a few things that you've learned that you find valuable and maybe also surprising, especially for the people who are engineers and others who've never thought of going out there and trying to sell something. So that would be amazing. hardest part was reorienting instead of going deep on a particular problem. Cause that that's, as an engineer, if you go deep,

you understand the first principles you can build up to a model that solves a particular problem. Instead, what you're really doing is you're figuring out how to navigate uncertainty in a world and you're figuring out where there actually is pain on the other side. And it's a very, very different mindset. Like one of the mistakes I see a lot of engineers doing is they'll go into it and they'll say, Hey, I want to learn this. Let me, what's the best book on sales? Because that's how they learned how to be a great programmer.

They read it, you know, they type some stuff. Turns out that's actually a terrible way to learn sales. The best way you have to learn sales by going and doing it, going and spending time meeting with customers,

meeting them face to face, meeting them on site, getting to know them, asking about their problems. The other big thing that was really high leverage for me is I got a coach, this guy, Mitch Miranda, who would come once a week, spend time reviewing what I was doing on

the sales side, and then give me lots of feedback and coaching. And so for example, one of the questions he'd always ask me is I talk about a customer and he'd always ask me, Hey, what's the pain here? And I'd be like, well, they wanted to make some sort of SQL reports and it's not working. And I'm like, he's like, yeah, but what's the business pain? That's not a business pain, Spencer. And so I'd go back and ask the customer about that. And then I'd come back to him and he'd be like, what's the pain? I'm still not hearing a business pain. And so after a few cycles of this in every single meeting, I would think, okay, Mitch is going to ask me, what's the pain? So let me actually spend some time asking the customer

about what their business problem and what the pain is. And that helps you get much, much clearer as to whether, because it's a two-sided thing. There's what your product is. And so many engineers are in love with what their product is and how great it is, how many features and functionalities it has. But it doesn't matter unless it can solve what it is a business is looking to do. And so really getting clear and understanding of that as the kind of golden thread to follow with a customer. And from that flows how you should spend your time, which customers are a good fit for what you do, how you pitch it back to them and position what you do, what you end up building on the product and engineering side and everything else.

And so that really, you know, that was huge. And so, yeah, I think biggest thing is learning by doing and then getting coaching and feedback from experts on it. That accelerates your learning so much faster than you could do on your own. So many engineers will spend lots of times on problems that don't matter. And the way you figure out which problems matter is by asking and talking with customers. And the way you figure out how to ask and talk with customers in the right way to narrow in on the ones that are important is by getting help, getting coaching

on what it means to be a great salesperson. I can totally relate to that because I've had a similar experience. To be honest, it was myself writing code that I decided to start the company and then suddenly I had to go and sell and it is like a very like transformational, let's say experience. And I would say like one of the things that I still like recognize a lot when I

talk like with engineers is like you use like a word, you talk about like navigating uncertainty and I think that's like, if someone asked me to describe what's the difference between a salesperson and an engineer, like I would say that like an engineer hates uncertainty, a salesperson loves uncertainty. It's like where they thrive, right? Like it's's- Totally. Yeah. Yeah. And it's like this transition that you have to do, like, regarding like uncertainty, which is like super, I mean, it can really boost your growth, like personal growth outside now of like, you know, being like successful in whatever you do, like personally, I think like it's a great experience

to have as an instructor to try that. One of the, one of the metaphors that I really like is that imagine there's 100 customers out there, all of which potentially you could have a conversation with and solve your pain. What you're trying to do is figure out of those 100 black boxes, which are the few that really desperately need what you need the most. And that's your job as a salesperson. That's what the art of sales is all about, is trying to figure that out. Yeah, yeah, 100%, 100%.

So, okay, you, that's, I mean, we can talk about this stuff like for hours. I think it's amazing to have someone who has been through so much transformation, to be honest, from writing the code, reaching the point to IPO. But let's talk a little bit more about the technology and the product. And let's start with talking about product analytics and the difference between product analytics and analytics in general. Analytics is nothing new. Since we had computers, like even before that, like

people were using numbers, like to try and figure out how to run their business or like do things in general. BI is like a thing like we have like since forever, but why do we reach the point and where your opinion that product analytics became like a thing of their own important enough to justify like a company to go and IPO, right? So tell us a little bit about that, because that's also very, I think, super interesting. I think what's happened is that because of the rise of online, as online used to just be a marketing channel where you just market to your users. You have a web page, you collect leads, but the actual transaction, now you have full-fledged

applications across the industry. And this is happening across in every industry, whether you're looking at B2B, the whole rise of SaaS, whether you look at media companies, now media companies, their growth channel, you look at Disney, number one growth channel is Disney+. Everything else about their business is actually shrinking. Parks is static. The distribution channels for movies is shrinking. It's like the digital and online is the future growth. You look at retail, Walmart, they realize that online is the future of their business. So every single industry you go across, it's like the online product is the growth channel. And so because of the growth channel, that means you want to invest in it and control it. And so how do you do that? Well, you need to be able to track it

first and foremost. And that's where product analytics and product data comes in. I think the other part of it is that technologically, it wasn't really possible to track how people used a product beforehand. You go back to the days of licensed software, where you buy something and install it locally, you buy some box software and you install it locally on your machine, can actually see how a user is using it in real time. But because all these application workflows have migrated to the cloud, all of a sudden, it's possible to see them. And then the last thing I'd say on it is that the whole, so this has led to the rise of what's called product-led growth movement. And so because you see that there are some companies that do it very successfully,

companies like Facebook and Netflix and Atlassian and HubSpot and Square and PayPal and tons and tons of companies that do this really well. And so everyone else is like, dang, I need to figure out how to use this exact same methodology. And that's where the need for it arises, because they realize that if they don't figure out how to make their product a distribution channel for the business and control it and make it successful, that they're going to be left behind by companies that do. And so I think that's what's led to the rise of product analytics and product data being a standalone category.

And that's what we are. Now, I want to be really clear. We're tiny. Our revenue guidance for last year was $230 million. If you look at that compared to the other big vertical SaaS players, you look at what's the equivalent on the sales side, Salesforce, right? So they do sales analytics. And that is a company that does tens of billions in business every year. You look at the marketing side, Adobe, that's a company that does similar order of magnitude of revenue, you know, amplitude, we're only, you know, 230

million in revenue. And so we're so early in the space relative to where the long term is. And so I think this still has quite a ways to play out. Can I interject a question actually for both Spencer, for you and for Kassus? So it was interesting hearing you talk about sort of the distinctions between, you know, analytics and product analytics. But in many ways, like if you think about like marketing analytics and you think about like a marketing website as a product, that distinction is really not, in many ways, it's not a real distinction. Yeah, it's not a real distinction, right? Like, is there a distinction between marketing analytics and product analytics?

I mean, obviously, there are like tools that are like, you know, like Adobe Analytics or, you know, it's just generally used for like marketing and attribution. But like, in reality, there's not, or at least from my perspective, like there isn't a distinction. But what say you? Totally, completely agree. It's the same. It's going to converge the same thing. From a customer, you don't think, am I on the marketing website or am I on the product part of the website? You know, it's all part of the same experience. You get an email or push notification. It's all integrated from your standpoint. You're not thinking, am I interfacing with a company's marketing team or their product team? And so I think long-term, the tools will end up converging. And so that's why you saw Amplitude last year. We ended up launching a whole ton of features around that allow you to do marketing analytics because people had these same sort of questions. They want to

view the integrated journey where you see someone landing and how does someone go from landing on the page to ending up being a great long-term customer? And where do those customers come from? And so we ended up developing a lot. Now, I think historically, product and marketing teams have been quite separate. And so that's why these two things have emerged separately. Because first, you had digital as marketing and the whole explosion of the marketing tech stack online, because online was a marketing channel initially.

And then you have marketing sort of questions that you're trying to answer. So where are my leads coming from? What's converting? How much time are they spending on the page? What's one of my most popular pages and pieces of content? To now you have product type questions being answered. Like where do people get stuck in my onboarding funnel? What's the long-term engagement rate of my customers? What causes customers to come back over time? Or what causes them to churn out of my business over time? And so you get product type questions.

Now, ultimately, again, from a customer standpoint, that whole journey looks the exact same. Those questions are actually very tightly related. And so I think over the long-term, you're going to get convergence of those two spaces. Yeah, I mean, I totally agree. And so I think over the longterm, you're going to get convergence of those two spaces. Alexi Vandenbroeker Yeah. I mean, I totally agree. There's like huge overlap between the two. There are also like some, like maybe a little bit like fundamental differences. Like I would say we, like there is a very important, I think, difference.

With product, you always have almost like, let's say, a face behind the data, right? You have like a customer who... You have a login. Yeah. You're generally have some sort of profile, you have some sort of, you know, recurring engagement with this company versus just being anonymous. Yeah. And probably like a bit of a better signal to noise ratio, because like in marketing, you also have all these anonymous, like traffic, that you have like to work with like much more noise way.

And there are like some different like way that you interact with the TikTok, like the similarities of like almost too much of like, you know, like doing the things. Right. So. Totally. And a lot of times you actually want to, those questions are related. So you want to acquire a bunch of users, but you don't care just about sending them to your living page. You care, are these long-term great customers for my business? I'm going to spend a lot because if they are, I'm willing to spend more on them. I just don't care about someone hitting my landing page. I care

about how much revenue I'm going to derive over their entire lifetime. And so they're actually very closely related. Yeah. I mean, part of the reason I ask is because like, you know, sort of being in the world of marketing and especially working in the world of SaaS, like among my peers, like the people who I really respect, you know, it's like, okay, you really know how to like figure out how to build something for scale, like are generally just using product analytics to like look at, you know, acquisition to like retention essentially, right? Because like that's actually how you make a true like data-driven decision. Anyway, sorry, sorry to interject there, Kostas, but I, you know,

of course, I had to ask. Not at all. That was like a great question. And actually, if you have like inside that's like you can't, like outside,

obviously, like we're talking about product analytics and I would assume that like the dominant persona using Ambitious are products, managers, product people. But maybe there are also like other people that are doing that.

Have you seen like marketeers like using it or like other people inside the organization actually using like Amplitude? Absolutely. That's actually one of the biggest learnings we had from last year was that product data is not only useful to product managers. It's actually useful to lots of people in an organization. Anyone who touches the product experience also cares about product data. So firstly, just within the product development team, you're talking about engineers, designers, folks like that, that also need to leverage and use the data. And then when you start to get outside of it, marketers actually use this too. I think we have a good percentage of our customer base that is marketers and also using Amplitude

to supplement what they do. Customer facing teams, that's been the most, one of the most interesting ones to me. Customer facing teams will figure out like a support team wants to know what did someone do before they ran into a support incident and they'll use Amplitude for that. Or they'll use it to figure out which customers are at risk of churn because they're not highly engaged with the product or what sort of features are our most highest paying customers using. And so you start to map it out and it's like almost every single function across a company needs access to product data. What makes product managers in particular special is that they're at the tip.

They're the ones with these questions first, and they're at the tip. They're the ones that want to start to figure out, okay, I just shipped this feature. What sort of impact has it had since we launched it? How many people are using it? Is it leading to more engagement and retention? 100%. And just because we were talking about sales at the beginning, have you also seen like, especially because of like the product-led kind of growth, like paradigm and all that stuff, have you seen also salespeople like getting value out of this data? That doesn't seem by like accessing, like directly something like CopyHit, but by, I don't know, like some of these insights going back to Salesforce.

Oh, completely. We use it a bunch here ourselves. It's actually a huge initiative for this year is to get everyone on sales and customer success trained on it so that they can use it. And a lot of the team already uses it, but making sure we're expecting everyone to do that so that they can find more opportunities and more within their existing customer base. Yeah, yeah. Yeah. Makes a lot of sense. All right. You mentioned that like you started like the company in 2012, right?

David Pérez- That's right. Alex Rauchmanis- 2012. It's like almost like 11 years now. So there's a lot of change that happened today, right? And one of like the biggest, let say, things that have changed is cloud data warehouse. People have much more access to, let's say, the dynamics between Amplitude as a product and product with the market out there, if it has done that? What have you seen happening out there?

What's really interesting is that the rise of Cloud Data Warehouse, if you look at the largest use case for it, it is to be the repository for what your customers are doing in your product and all the behavioral data. And so it's actually, the rise of Cloud Data Warehouse is really interesting because it's been like kind of a parallel rise. Obviously, you have a vertically specific end-to-end integrated tool like an Amplitude where we're specifically targeting product data and all of that. But then you have Cloud Data Warehouse, which allows you to say, okay, let me put that data and all the other data you have into a single place. And so that has become massive. What's interesting in talking

to a lot of the data leaders is the number one challenge they have is how do you unlock data out of that data warehouse and get it in the hands of end users. Aggregate all this data across tons of your business. It's a huge mess. Like, you know, schema, there's no consistent schema naming convention. There's tables all over the place. You know, events aren't tied consistently, all sorts of, all sorts of issues like that. Question is how do you then operationalize it and start to leverage

it day to day in your business? And how do you see like AmpliQ working together with a data warehouse? How does it happen today? And like, if you can share like a little bit of like your vision, like for the future around, that would be like great. Totally. So I think it's going to play out similar to the previous generation in which you have for more complex, more sophisticated analysis that requires

data from multiple teams, you're going to do have an analyst or a data scientist do that analysis custom on top of the cloud data warehouse. And that's going to be your kind of system of record. In addition to that, you also probably have a tool that end users use day to day to answer their questions. And so they want to know how many people are using this feature today? What's my daily active user count? And they're not going to go into a data warehouse and write SQL to it. And that's where an amplitude comes in. So just like your sales team

uses Salesforce, your marketing team uses Adobe, you know, your product team is going to use amplitude. In terms of overlap, I think one of the really interesting things you might say, it's like, okay, well, you have the same data sets in both. So don't you need to work together? Absolutely. The number one integration we have as a company today is with Snowflake. We have hundreds of customers that are both Snowflake users and Amplitude users at the same time. And so they'll often take data in Amplitude and then send it to their Snowflake data warehouse so that they can keep a copy of it there and cross-reference it with other pieces of database and data in that database. And so while the product team is still using it day to day, you end up having also that

data for more sophisticated analysis in your data warehouse. The other thing I'd say with cloud data warehouses and amplitude is that I think a lot of these data leaders come in with this vision of, hey, I'm going to go build out this stack by itself, where I'm going to put all this data warehouse or put like a BI thing on top. And one of the things you realize is that the workflows per function are so customized and specific that generalized BI tools don't really cut it. So it's not like if you think of the analogy of the sales side, it's not like you have sales managers running their forecast out of a data warehouse.

That makes no sense. Not use Tableau or Lookit or do that. They're going to use Salesforce. And so I think it's the same way with product teams. One of the biggest places that we see need for it is actually when the central data team gets too overwhelmed with questions to be able to keep up because there's a never-ending list of questions and things you want to know. And so you need something to self-serve those end users. That was the reason we were pulled in at Atlassian was because the product team, one of their mobile product teams, they constantly had all these questions they wanted to get answered to by the central team, and they couldn't get answers to that. And so they ended up deciding to say, hey, we need to buy something

that just does it. They used Amplitude. Other teams saw what they were doing. They decided to adopt. And then finally, the central data team, they were very forward thinking. And so they were thinking, how can we have an endless backlog of questions? How can we actually self-serve all of these customers and get them to answer things without having to come to us? And then they decide to standardize on amplitude. The funny thing, we call it among data leaders, we call it like data breadlines. It's like we're rationing data because there's not enough analysts and people who can write SQL and who understand the data model to go around. And so you end up having a constant shortage of data and data insights when

ideally the entire company is running off data all the time. I remember talking to one data leader at Airbnb a few years ago, and their aspiration was they were going to hire a data scientist for every single product manager. And I'm like, what? That's just the craziest thing. I mean, and this is Airbnb and they still weren't able to do it. And this is Airbnb, one of the most successful Silicon Valley companies in recent history. They still weren't able to attract enough and pay for enough folks in the data science side, you know, and so they ended up having to go to third-party tools. And so I think a lot of people don't realize what a bottleneck that thing is for the rest

of their business. Yeah, 100%. All right. One last question from me, and then I'll give the microphone back to Eric. Can you share with us, like, something that is happening in the industry today that you find really exciting, not so on its own, but together with Amplitude, like, something that you feel like it's going to be really exciting, how Amplitude is going to grow around that, based on, like, the trends that are happening out there, right? Like, what excites you? Oh, so much. I think one of the biggest things is how do you bring this way of leveraging data from

what I think of as the sophisticated, innovative product-led growth companies to the rest of the market? I think still a lot of innovation to go there. Eric, one of the points you made earlier was that one of the reasons Google Analytics is so successful and ubiquitous is because it has a bunch of charts that work out of the box for marketing analytics. And I think product data needs to go through that same transformation. It's still not there yet. Use us. If you use some of our competitors, it's still like you have to build a bunch of reports when you first sign up.

There's not a ton of stuff that works right away if you don't already have knowledge of how to think about it, use the data. And so I think this industry, we're in the kind of crossing the chasm moment where you're going from the innovators, the early adopters to the early majority. And the early majority, it's like, hey, I don't really know how to do this. You have to teach me how to do this. And so I think there's a long ways to go in doing that.

We're going to be doing a bunch on the innovation side this year where we're looking at coming out with out-of-the-box reporting and a whole bunch on the UI side to make it easy. But I'm really curious to see what other innovations come out in the space because that's the number one request we get from customers is what are the best practices?

How do I do this thing? And we've playbooks and we do training sessions and all that sort of stuff. But I think there's going to need to be more breakthroughs on the product side to make all the stuff work out of the box. Like you don't be an engineer. You don't have to be a data scientist in order to start to understand how to leverage this

in your job day to day. And so I think this industry is going to have to go through that moment because obviously folks like us were very familiar with the data stack, both the self-built one,

you know, that has the horizontal tools like the snowflakes and the runner stacks and, you know, other companies as well as the vertically integrated ones like us. But it's, there's still a lot more ways to go.

One of the other questions I love asking data leaders, I'll ask them, what percentage are you in the way of realizing your vision? And they always say like, oh, I'm only 5%. I'm only 10% of the way there. No one ever said that. Ever like, oh yeah, I'm done building my data stack and we're good and we just need to do this.

No, it's like, I need more resources, more people. Like there's still all this capability to build off. And that tells me the vast majority of the innovation in the space is ahead of us. And so I think the question that we all have to face is like, how can we make it work in a simple enough way for the early majority user so that they start driving and getting a bunch of value out of this without having to be, you know, learn engineering or learn SQL or be as much experts in data as we are?

Sure. Well, let's, so I want to use the last part of our time to actually dig into some more technical stuff. And I think this is a great jumping off point. And so I love the vision of, you know, product analytics sort of becoming as turnkey as say, like a Google, you know, Google Analytics, although, which this is a whole other podcast episode, but we should talk about sometime the, like the challenge of dealing with, you know, 20 year old, you know, methodologies and taxonomies in the Google ecosystem, because it's amazing how pervasive that's become and how painful it's become, especially compared with a tool like Amplitude. But when you think about sort of

delivering that level of sort of out-of-the-box, you know, sort of like value, let's say, to an end user who's logging into the platform. There are certainly UI elements to that, but then there are also probably some instrumentation elements, right? Because ultimately, Amplitude is delivering insights at the end of a pipeline of data, right? So you have events that are coming in, they're being processed, and then you can like build reports or you can produce reports or whatever. And the initial like instrumentation of that in whatever products you're tracking, right? So like, let's say you have a mobile app has a significant impact, right? That's actually the raw material that sort of, you know, drives the insights. How do you think about providing that sort of out of the box experience in terms of like where in that pipeline you're trying to impact that? Is it like super

early, you know, to, you know, I guess like maybe in the auto tagging type sense where you're like trying to like control the schema like very early on so that it's easier to produce those? Or is it really more of a UI like reporting challenge? I think the whole thing, the whole pipeline needs to be improved. So first with the instrumentation is the number one blocker to someone successfully adopting product analytics today. And so if you look at how Google Analytics does a good job of it, they auto tag pages for you. And so I think you're going to need

to do something similar because right now you end up having to tag each single event at a time. And with hundreds or thousands of events per product, that's much harder to get started in a simple way. Now, we have ways to do it where it's like, hey, let's start with the top five events and then go from there.

I think it still remains the number one block. Hard problem though. Like very hard. Yeah, very hard problem. And so there's a lot. I mean, there's start with the top five events and then go from there. I think it still remains the number one block. Hard problem. Like, very hard. Yeah, a very hard problem. And so there's a lot. I mean, there's auto-track ways to do that. Those have major downsides, which is why we haven't presented. There's going to be a bunch of node code ways that we're going to be trying to tackle this year.

So we'll be interested to see how much progress we can make. For sure in the UX, it needs to be simple enough that someone non-technical can use. And then I think the other big part is how do you get a reasonable set of dashboards default out of the box so that someone okay this makes sense i get it when they're using it for the first time and so i think all of those that end-to-end pipeline is going to be to improve to make it successfully adopted by the early majority. Yep. Yeah. Super interesting. Okay. Digging into that pipeline. One of the things that's

always fascinated me about Amplitude is that you can sort of derive these incredible insights. And I remember my first experience using Amplitude and you sort of like drill into an event and like, you know, I made some sort of discovery drill into an event and like you know i'd made some sort of discovery on an error that was happening somewhere i was like oh my goodness this is like a huge problem in this mobile app and like wow this is awesome you know it's just like the ability to drill down is super cool but that kind of obfuscates like the amount of actual like data engineering pipeline complexity in terms of the build you had sdks very difficult you know like to actually build a really robust sdk you know fault tolerance like

there are all these sorts of things and so how do you think about that you know because really like you're delivering an insight to an end user in the UI, but the product that's generating that is actually phenomenally complicated data pipeline software in and of itself. It's almost like it's a completely separate type of product. Yeah, I mean, I'll tell you, it's even more complicated than that. Because if you look at product data, so first, the surface area is enormous. Average product has a thousand different touch points on it. So a thousand different, can't even hold that stuff in your head. And so how are you going to navigate and know what's going on there in the right way?

So that's a huge challenge already. And so how do you even categorize, make sense of all of that? And then on top of that, it's like every single user navigates those thousand data points in a unique way. Completely, you have a million users and you have a million different paths. And so how the hell are you going to synthesize and analyze that in a coherent and meaningful way? And so I think there's really hard problems around both of those issues. Now, to your point, Eric, I think one of the things on the product side is

we think a lot about how do you make it easy because you have to abstract a way and make reasonable guesses in how to present that complexity because the vast majority of people, like that's where 99% of the value lies because you can't have the average person in your org needed to be trained on that complexity.

It's not going to work. Yeah. But can it get value out of product data? Absolutely. Whether there's someone on the support side trying to figure out what went wrong, whether it's someone in marketing trying to figure out what campaigns are working,

whether it's someone in design trying to figure out, hey, what's broken about this interaction? Like there's so many people in the company that need to leverage this data. And so you, and so the hard part becomes from a product standpoint, how do you abstract away that complexly? So like one of the things that we did that I'm very proud of in the early days was we actually made charts interactive. So you could

click on a data point and see the list of users that make out of that data point and then what they were doing before that data point. And that's been an incredibly valuable tool because it allows you to kind of break down and drill down on a specific data point or user or set of users in a really effective way that's intuitive without exposing you to that complexity all the way up front. And so I think there's going to be need to be a lot more innovations on the product and interface side there that drives goodness.

So, and I think we're still in the really early days. Like you have to imagine, you've seen all this AI stuff come out recently where you're taking this incredibly complex thing, language, and then figuring out how to extract it and automate it and automate it and perform it at a high level in the same way people do day to day. And so I think the same thing is going to need to happen on product data and behavioral

data in general, where there's going to need to be a whole bunch of automation abstraction that make it simple. And I think we're still in the very, very early days of that. Now, I wish I had the answer to all of that. We're still trying to figure out a lot of different ways. Like one of the things we're looking at is like, how do you map your data? So if you have a thousand different data points, how do you map to those thousand data points in a reasonable way that you can understand them, for example? So anyway, but I'm really excited to see what sort of innovations happen over the next few years, because every

innovation is going to double the number of people that this data is accessible to. Yeah, yeah. And so one last question, as we sort of wind up here, maybe I always say that, and then it ends up being two or three, but does the data model become more opinionated as you move towards, as you move in that direction, right? Because if you think about, I mean, there is a data model with an amplitude, right? But in many ways, at least as a user, my experience is that you're modeling the data in a way

that sort of gives me a canvas that allows me to sort of build my own models on top of that model, if you will, right? So funnels or cohorts or whatever, right? And so it's sort of like a blank canvas. There is an underlying data model, but as you move in that direction, it stands to reason that you would actually, the data model would become more opinionated in certain ways. Yeah, you have to, you have to. There's no way you have to make some set of assumptions of what you care about and what you want to look at. For example, just let's take events. If you have a thousand events, you can't show those thousands of events to an end user and expect them to have to pick between them. You're going to have to make some assumptions for them about which ones are important and which ones aren't. So just from that standpoint, you're going to have to start to opinions.

What sort of charts and analyses you present out of the box, same thing. You know, if you want to have a default set of dashboards so someone doesn't have to think about which charts they want to create, you're going to have to provide that for them out of the box. And so I think real art, if you look at what Google Analytics did,

as a marketing leader, there's typically a standard set of questions you try to ask. Where's my traffic coming from? What content is is popular how long are people spending on site and so you google analyze you can ride those with product it's actually quite custom and you need right one of the things that makes it challenging is you need a forcing function to align everyone towards a kind of similar point of view because if you ask people like really simple questions like, hey, what do you consider a DAU?

Every single business will give you a different answer on that question. And the question is, how do you simplify that so that you get some sort of standardization and reasonable set of answers? And so that's where I think the exciting part is in this space. And every innovation that gets developed is going to have a massive impact in terms of the number of people who are going to be able to have the data. Do you envision that as sort of a industry standard? I was going to say almost open source, but I don't think that's the right word, especially using Google Analytics as the example, right? Like Google Analytics, you sort of have

like uniques, you have sessions and some of those standardized things, right? But it doesn't really extend to the point of, you know, sort of daily active user, other things like that. Do you envision some of those things sort of being established or even just do you envision Amplitude

playing a role in sort of like industry-wide saying like, okay, not everyone's going to adopt the same exact definition, but like we're sort of establishing this as like the benchmark for these sort of core product flavored metrics. Yeah, I think it's going to, for sure, it's going to need to be something bigger than just amplitude of what we drive. I mean, people have asked us for those standards and we've developed a few, like we've written, we have a product analytics playbook on our website. We have a retention

playbook. We have a bunch of other pieces of things. It's like, you know, here's how you should think about each of these metrics. But the question is, you know, what sort of outside forcing function is going to come through the ecosystem and start pushing people to the same thing? And that I don't know the answer to. But that will be really important to the long-term success because that enables all sorts of shortcuts and fast ways of getting to the data and being clear about what's going on. Yeah, for sure. Okay, well, we're close to the budget here. One more question. And this is getting really practical, but you have built an incredibly successful product analytics company, but also have, you have built an, you know, incredibly successful product

analytics company, but also have an engineering background, have probably thought more than most people about product analytics. For our listeners who maybe haven't used product analytics very much, like let's say after this episode, they go, you know, wire up Amplitude, like, what would you encourage them as their first sort of like couple of reports to explore? Like, where's the best place to start with product analytics? Especially considering, you know, it's not necessarily a Google analytics where you just get these reports. Like, where should they start?

I would start off by picking five things, five events in your product that you want to understand. So just, it could be, hey, someone used this feature today or someone completed a checkout or someone hit the landing. Just pick five. Start with that. Start by instrumenting that. That would be the most important thing to do.

From there, you can get all sorts of insights on how are people rotating over time? Are they coming back to the app? Are they getting stuck? And then you can start to narrow in on deeper and deeper questions. So a very typical one is the onboarding funnel like very important thing to optimize how people get set up and started with your product for the first time and so i think um start off starting off with two don't feel you have to instrument the entire application right away that's one of the biggest mistakes because that

can be a very significant effort yeah talking hundreds of thousands of events. Start off with five. I'd recommend five and you want to get 10 great, but you know, just like something you can keep track of and that will help you really start to get an understanding of the shape of the usage of your product and you can build on it and build your, and that'll naturally lead to more questions, which will lead you to insert more stuff, which will lead to even more, even more insights from your app.

And it's this great virtual cycle that you build on. My biggest advice is don't be overly ambitious with your tracking from the start, you know, instead let it build organically over time. Wonderful. Well, Spencer, this has been such a wonderful time. An hour has flown by. It feels like we just hit record. So thank you for being so generous with your time. Eric Costas, that's fantastic. And meeting you, thank you everyone for listening. Really

appreciated. And yeah, check out Amplitude if you haven't seen it. We actually have a very generous free plan that you can just get started with right away. And yeah, hope to talk to you guys again soon. Absolutely. You know, Costas, I think one of the things that I appreciate most about that episode is there was sort of an underlying sense of humbleness that Spencer brought to the table. Taking a, you know, he said, I tried to start multiple companies, they were failures. He starts Amplitude, in many ways, almost a classic founder story where you're trying to build a company and you end up solving a problem that isn't directly related to what you're trying to sell with that company. That ends up becoming what you really focus on. They took it to market. Over a

decade, they went public. And his willingness to explain how small Amplitude is in comparison to Salesforce and Adobe, I think, speaks a lot to why, or at least in part, why they have been very successful. Because if that's their attitude, you know, in many ways, you almost hear him talking. It's like, we haven't even scratched the surface, right? We have so much more to learn, so much more to build. We're so small compared to like the established players. And I just think that's an attitude that I'm going to take with me and really think about over the next several weeks. Because I think that's rare, right? To see that. And it was really encouraging and I think says a lot about the future of Amplitude.

Oh, a hundred percent. And I would add to what you just said, that the excitement is still there. You know, like most people though, when they think about like entrepreneurship or like starting a company, they think of like a journey where you start and hopefully you grow it. And at some point you exit one way or another with the major exit being like going public and then you're done. Like you cross the finish line. And then you talk with this guy and he's like, oh, we're just starting.

Yeah. Like we have just like scratched the surface and there's like so much more that we can do here. Like so much more like to build. That's together with what you said about how humble he is. Like something that like I'll definitely keep and think about and reflect on it and see, yeah, like how much of this I also have, how can I help me grow, right? So.

Henry Suryawirawan, Indeed. Well, thank you for joining us on the Data Stack Show. Always a pleasure to talk with brilliant people like Spencer Skates. Many more on the horizon this spring. So definitely stay tuned. Subscribe if you haven't. We'll notify you of new episodes. Also, we have the live meet and greet at Data Council Austin in March.

You definitely don't want to miss that. Go to datasackshow.com to sign up and register. And you can meet Kostas and I in person. And we'll do some live recordings. Until then, we will catch you on the next one. We hope you enjoyed this episode of the Data Stack Show. Be sure to subscribe on your favorite podcast app to get notified about new episodes every week. We'd also love your feedback. You can email me, ericdodds, at eric at datastackshow.com. That's E-R-I-C at datastackshow.com.

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