Product-Led Growth with Analytics & Data - Adam Greco @Amplitude

So it's a pleasure to have another guest at the Product Bakery today. So I would like to welcome Adam today. Hey, Adam. Hi, how are you doing? Thanks for having me. It's a pleasure. And I think it's a long time since we had an interview. So yeah, it's just time to go back to our previous mode that we were following by interviewing experts from the product field or other fields, actually. And yeah, Adam, when I look at your CV and your history, it's like just super interesting because you have been around in the world of analytics and marketing a lot. And these days you're working at Amplitude as a product evangelist. And I was just so curious because your description on LinkedIn says you provide content, education, and strategic advice on how to build better products. And I was just curious to hear what that exactly means in your day-to-day business. Yeah. Well, I think going back in my background, I've been in the analytics space for over 20 years. I started as one of the early employees at a little startup that no one had ever heard of called Omniture. And that was like my second career after doing a bunch of tech stuff. And Omniture ended up being acquired by Adobe and became the leading marketing analytics vendor out there, basically Adobe Analytics and Google Analytics. I ended up authoring the book on Adobe Analytics. So went through hundreds or thousands of Adobe Analytics implementation. So I got to see different companies of different industries using analytics. What does financial services do? What does media do? And so on. So it's been really interesting. Most of my career has been in the marketing analytics space. And as you mentioned a couple of years ago, I shifted gears a little bit and joined Amplitude, which is primarily known for product analytics, which is probably more relevant to your audience. And yeah, I wanted to take on a new challenge, learn what is different around analytics for product teams versus marketers. And we can get into all of that as we dive into our talk here. Yeah, it was actually the idea because product and marketing is usually very tied together when it comes to collaboration and also looking at data. And I was just wondering, because I see in companies that the whole topic of collecting data in all departments actually is rising and there's much more awareness than let's say five to 10 years ago. Plus the fact that we have like machine learning, AI, and all these kinds of trends rising. And I just noted for myself that it's sometimes quite easy actually to get lost in a jungle of data. What can I do to ensure to not getting lost in a jungle and to be able to set clear data points, KPIs, to be able to have a functioning and well-working marketing and product machine? So there's a couple items you just brought up. So let me tackle these in order. So the first thing you talked about is just the rise of data. And I think what's happening in the marketplace is we're going through this really historical evolution of digital transformation, where it seems like every business in the world that used to conduct business face-to-face is now conducting business digitally. I think that was greatly accelerated probably by five to 10 years because of the COVID pandemic. So if you interact with your customers digitally, you no longer can shake their hands, can get to know their personality. If you think of the old shopkeeper who knew his or her favorite customers, and as soon as they walked in, started getting their products ready, all of that is happening online now. And the only way that you can listen, effectively listen to your customers is by using data, collecting data, tracking all of their clicks, their swipes, all of these things. And so we're in this new era where listening to your customer, which is always an important skill, is now being transformed to how well do you listen to your customer digitally? And I think that's why you're seeing such an explosion of data. But let me pause there. I want to get your second question. Let me pause there and see if you have any questions or comments or reactions to that. I mean, it makes totally sense. I mean, it couldn't have been better summarized. And I think the pandemic was definitely an accelerator, right? Because there was so much more awareness and also so much more challenges where a solution needs to come in place, which I see is definitely working with more data and being more focused on analyzing what's going on. Yeah, exactly. And you've obviously been around for a really long time, right? To see different technologies arise, to also see the internet develop quite drastically, right? I think especially like now, there is also a bit this trend, because as you say, like one of the main things or interactions that we have is to look at the data to understand and to listen to our customers. How do you see that changing now, especially like with Apple focusing a lot on privacy and making sure that actually data is way more anonymized? Or if we look at Europe GDPR, that also makes it drastically harder to get all this data and to track the users? Yeah, it's kind of a paradox because we're in this world where the most important thing for organizations is to collect data because of what we just talked about. But at the same time, you've got people, consumers and governments who are saying, we don't want you to track us as much. I think it's not a shock that GDPR came around right after digital transformation. I think the two are related. I think as consumers realized how much is being tracked about them, they kind of revolted and worked its way up. But I think that what you're bringing up is one of the reasons actually why I transitioned my career away from the marketing world to the product world. And the reason I thought about this is I think that the privacy legislation, cookie deletion, all of this is, I think, an existential threat to digital marketing and digital marketing analytics. It'll always be there. There's always going to be emails. There's always going to be paid search. There's always going to be display ads and so on. But if you're a marketer and you can't get really good information about how your ads are performing because the person clicking on the ad is anonymous to you and you don't know if they're the same person who clicked on your ad two weeks ago, I think that what happens is budgets will still be there, but they're going to probably shrink because whenever you can't prove the value of your ROI, budgets tend to disappear. But what is it that you can control even if everyone was anonymous to you? You can still, as a product team, say, we're going to collect data. There's no law that is ever going to make it illegal for you to collect data about a website or a mobile app and see how it's doing because you need to be able to do that to make it better. Now, you may not know who the people are. And so one of the reasons I shifted from marketing analytics to product analytics is I know the product analytics will be around for the next 20, 30 years. I can't say that about marketing analytics because it's just in a tenuous situation. But even if you go to the worst extreme where there's a law tomorrow across the whole globe that says it is illegal for you to know who is on your website or your app at any point in time, marketing analytics would pretty much die at that point. But product analytics would still survive. Now, it wouldn't be as great as it could be because it would be really difficult to do retention reporting and know, is this the same user who did this feature last week? But you could still always be doing experimentation, testing. You could still see what people are, what features people are using. You could still make your website or your app better, even if you had 100% anonymous users. Totally understood. Totally understood. But I'm just wondering because, I mean, so you said there's no law that can pretend a product team to be able to track how the app is functioning, right? But I would still say by understanding how the app is getting used, I'm still able also to derive which kind of customers are using the app right at the end of the day. Yeah, but you won't have what we call PII. You won't have personally identifiable information. Now, you may be able to know, you know, hey, if they're clicking on this section of the site, I'm guessing they're an enterprise, but you're not going to know that it's Adam or Joe and so on. And I do think that ultimately there's probably going to be carve outs in some of the privacy legislation for teams that want to basically just make a better software product, because there's nothing that they should be able to stop you from doing that. And if you honestly don't track anything about the users, they're just some random ID that honestly can never be tied back to a person. I don't think there's any legislation that will stop that. And I don't think that's the intent of GDPR. You know, you're already meeting the intent of GDPR if you've done that. Now, I hope we don't get there. And I hope that marketers and product teams still will be able to track people if they give consent to track. And there are a lot of people who want to be tracked only because they want to have better, more personalized experiences. So I think some people have a little bit of a sky is falling attitude when it comes to GDPR. I don't think it's as bad as people think. It's basically saying you need to communicate and be transparent about what you are tracking and make sure people know. And if you're doing the right things anyway, then GDPR shouldn't be that big of a deal. It's usually the people who are doing kind of not so nice or a little bit sketchy things that are the ones who are most worried about GDPR. I always tell people, if you're really worried about GDPR, then you should look in a mirror and wonder what you're doing right now, because you probably shouldn't be doing it anyway. Yeah, which actually brings me to another question that I would like to get your view on, right? Also, if we talk about product analytics and tracking some of the user actions, it could be for the purpose of improving the product general or for making a personalized experience. But where would you say is good to draw the line when it comes to the privacy concerns? Like what should I really track of how the user is using the app or what a user is doing with it? And what should I not track for the sake of also giving the user sort of the privacy on a product? Yeah, I think you want to look at it as, am I tracking this because I want to make the product better? Or am I tracking this and tracking data because I want to sell more to a particular user and I want to get to know as much about them so I can improve my chances to sell to them? And I'm not saying that the latter is bad. It's just, that's where you're starting to get into an area where you're probably have to ask some questions. Do I have permission to do this? But if you're just tracking the information and you don't honestly care who the person is, but you just are looking at a random sample of people to see, of 10 people who click on this button, how many of them actually click on this next button that I expect them to, then I don't think you're in any jeopardy there. And I think the further you go towards personalizing where if you swap one person for the other, it would be a totally different experience. That's when I think you need to start thinking about consent. But if it's the same experience for everyone, then I think you're probably in pretty good shape. But one thing that I feel like we didn't get to is, I know Christian, you earlier asked a question about, you mentioned that marketing and product teams should be working closely together. And I think this is kind of related to the last point is a lot of the people get in trouble because the product team, honestly, a lot of times they don't care specifically who the people are. I mean, if they're doing like a focus group, they do. But I think it's more of the marketers who tend to care more who the people are. But a typical example of where product and marketing are not working together today is a product team, for example, might say, I have these users. I don't know exactly who they are, but I can tell that they're the same users because they have the same ID and they've given consent to track. So I know that these people have come back over times. But one of the things I don't know is how did I originally get these users? And imagine you bucket your user base into like your power users, the ones who are just like, awesome, we're doing all the things we want them to do. And let's say you have another audience, which is one level down, which is they're kind of doing what we want them to do, but they're not quite power users. And then you've got the clueless users who are basically not doing really anything you want them to do. If you think of those three cohorts, now, how do you find more of your power users? And how do you move the people who are clueless to the middle group and the middle group to the other group? And I think that's where, unfortunately, when I work with a lot of companies, when the product team doesn't have any relationship with the marketing team, they don't understand where they're getting their power users from originally. But the companies that we work with, one of them is really good as PayPal, the marketing and product team, they're really good at working together so that as they build cohorts of users in the product team, the marketing team can also fill in where they found those users. And I think that's the wave of the future. I've joked that I think marketing and product teams will probably end up being merged in the future. And they'll just basically stop existing as two different groups and maybe be thought of as the customer experience group. And the customer experience, if you're doing it right, should start with the minute they hear about your brand, hear about your product, first come to your website or app, all the way through them being a customer for a couple of years. And customers don't really care if you're in the marketing group or you're in the product group. They just want to have a good experience. And I think that's why the smaller companies like the digital natives and the startups, they're much better at treating customers well because they don't have this artificial wall created between them. But if you're a Fortune 500 company, you might have 200 people in your marketing group and 500 people in your product group. And there's very little overlap. Yeah. And there's a German quote, I think you are running into open doors with what you said. I mean, I'm just looking at Alex because especially when it comes to the whole customer experience. So I think that's definitely the trend that is emerging. For me, I can just agree. I think my whole career working in design, I had the advantage that I was usually managing the teams both on the brand design and marketing side, as well as the product design side. So I think I totally agree. And I think it was also always the way I like to look at experience and user experience overall, because the marketing ad is just the first touch point. And then it goes all the way into the product and retention after the initial conversion. So I love the fact that you would bundle it similarly and that you're actually using this. And I think this is why at Amplitude, we're really big believers in product-led growth, because I think if you believe in product-led growth, you can't really effectively do product-led growth if you aren't including both marketing and product. And one of the things that I did when I joined Amplitude is for 10 years, they were a mobile analytics, product analytics vendor. And you could kind of try to do marketing analytics with it. But if I'm being honest, it was like putting a square peg in a round hole. But since I came from a marketing analytics background, I basically met with the product team and said, listen, don't you believe that for product-led growth and for the future, product and marketing teams should all be using the same platform? Why should the marketing team use Google Analytics and the product team use Amplitude? It just doesn't make any sense because you're using different numbers and you can't follow the same customer from acquisition to product usage. And if you think of product-led growth as a loop and the more customers you get, the better product ideas you get, the better you experiment, then your product gets better, then you get more virality, then you get more marketing, then you get more... You want this kind of great, positive, fruitful loop. But why wouldn't you want to have all that loop in one product? And clearly, marketing analytics vendors are very far away from being able to do a lot of the cool things that product teams need in terms of product analytics. But we actually at Amplitude weren't that far away from doing what we needed from marketing analytics. So we added like four or five really big features. And now we can be that one-stop shop for the entire loop. And we think that that will help product and marketing teams start to work together because data can start to become that Rosetta Stone that is like they all have in common. And I think that brings us actually also to the third part of the first question that I asked, because during this conversation, I see that when it comes to marketing analytics and product analytics, I see the distinction between functional information and information to sell. Right. So there are two purposes that marketing teams try to understand the customers in particular to be able to better sell their features, upsell, et cetera, while the product team tries to understand how the product is getting used, which hopefully will merge in the future. But I'm also trying to understand still in these days, apart from this great Amplitude solution, which is out there already, how can teams setting up their data collection and KPI definition to first of all, get the right data and how should they collaborate? Yeah. And it's very overwhelming. I'm glad you brought us back because I wanted to hit that last question. So this has probably been the biggest challenge that I've seen in my career is you can track so much, but should you, and how do you determine of the millions of things that you can track? And I have this expression I use in a lot of my presentations, and I always say, just because you can track something doesn't mean you should. So how do you figure out what you should track? Now, there's a lot of different schools of thought. I have an opinion on this. I can't say that my opinion represents my employer, but I'll tell you my opinion. So there's one school of thought that says, collect everything, collect every data point that's done. And there's companies out there that do this thing called auto tracking, where they literally walk the DOM and they track every single thing on a website or an app. I'm not a huge fan of that because I believe that it creates just a lot of noise and things can break when you do that and a lot of data quality issues. And if you don't have good data quality, then it's hard to get people to pay attention to data. So then you've got the other approach, which is what we call the prescriptive approach, which is where you choose what you want to track. But how do you even choose that? So my theory after 20 years of doing this, what I've come to learn is that the only way that I've seen companies be effective with data is to start at a higher level with the business objectives and the business questions that your company has. And it's so easy to say, I want to track this, I want to track that. But if you go to someone and say, why do you want to track that? What is the question you're trying to answer? And this applies to both marketing and product. So for marketers, it might be, why do you want to track the campaign codes that people are coming from? And they could explain that to you, but at least you know why you're tracking it. And you should never track data, especially in the GDPR world, unless there's really a reason to track it because you're exposing yourself to risk. So I think if a product team and a marketing team can articulate, here's the 30 questions that I want to answer, prioritize them. And what I used to do as a consultant is I would take each business questions and I would break it down into the events and properties that are needed to answer that business question. And then if they can help me prioritize the business questions, and then I can correlate what are the events and properties that go with those business questions, then by the transitive theory, I could identify the events and properties that are most important. And I even used to have this cool spreadsheet I made where I would count how many business questions every event and property was helping to answer. So there's an event that is critical to 30 business questions and another event that is critical to two, guess which one I probably want to implement first. And I want to make sure that the data quality is really good. I think that if you do that approach and you say, we're going to just focus on these questions, then you're never implementing anything for no reason. And I can't tell you how many times I've seen companies where they implement a whole bunch of data and then they say, okay, great, we have all this data. Now, how are we going to use it? But if you go with my approach, you actually know how you're going to use it before you do any lines of code. And then you make sure you don't waste effort, waste time, because developers' time is very precious these days. So let me pause there, see if that makes sense. Yeah, I think especially with tracking a lot, one of the things that I've also seen is that then if you have these masses of data, different people tend also to just pull exactly that one thing that is interesting to make their point or to get something through. So this really thinking about why you're tracking something and what is the question behind it and how do you come also to conclusions is definitely key here. Yeah. And if you want to be even stricter, I'll throw in one more thing. If people give you too much to track, another thing I used to do with my customers is say, can you put a financial euro or dollar amount associated with the answer to that question? Because if you just want to know it out of curiosity, that is a lower priority than something that says, if I know the answer to this question, this will help us save x million euro or generate x million euro. That to me is the tiebreaker. If you're not sure which ones do, go with the ones that you can get the financial impact because for most organizations, it's all about the bottom line. And it brings me also, especially focusing on the business question, brings me so much on to the whole topic of the product strategy, right? Because it has to be so many implications on how I define a strategy and how I also want to execute it by focusing much more on business questions rather than simple feature delivery and outcomes, where we also have many product teams who are trying to be out there and deliver as much to the world as possible without asking the right questions. And I think it brings up this whole conversation on a leadership level, right? To be more focused on the key business questions to ask, rather than falling into this trap of going into the nitty gritty details and checking out which kind of tracking behavior we want to analyze. Exactly. And I think that's where if you can get alignment on the business questions all the way up to the executive level, that really helps. And the good news is it's actually in the benefit of the marketer or the product person to do this because no one wants an executive to micromanage this feature or that feature, or why are you tracking this? If you keep them, if you just say, listen, these are the business questions you're trying to answer and these are your priorities. Now, leave it to me. We will figure out what data we should track and we will give you an answer to that business question and they can stay out of your business. Because sometimes executives get a little bit too involved and you want to just have a little bit of compartmentalization there. But if they want to get in the nitty gritty, that's fine. But I like to let them know that they're off the hook once they give us the guidance that we need. And what's interesting is I've even had some customers that will have an executive sketch out on a piece of paper, like this is the report or the dashboard I'm looking for. Just give me this answer. And then I will tell them, don't give them that reporter dashboard, ask them to break that reporter dashboard into the questions they're trying to answer. Because if you just give them the report dashboard, it's going to be checked off a box. They'll look at it once, they'll never look at that dashboard again. But if you give them the answer to the question, that can lead to another question and another question and another question. And it's kind of like a process that you have to go through. But I think too often, we think that analytics is all about creating reports and dashboards, when really at the end of the day, it's about learning things. And the companies that learn faster are the ones that tend to beat their competition. And the ones who learn faster tend to be the ones nowadays that are better at turning data into insights. Very nice. I think that's really good advice on how to also create this alignment. But especially when we talk about aligning and getting this alignment all the way up to the top of the company, who would you say is the person who should be in charge of analytics in a company? Who should run this? Who should create this alignment? Yeah, unfortunately, it differs by every company, by the size of the company. And some companies, the product team can be in charge of this. They're very powerful. Some companies, it's the marketing team. Other companies, it is a data team. And the data team is kind of like the referee or the umpire, if you will, that's kind of in the middle. And then you've got the executive team. So it's really tricky to figure out who it is. You have to know the organization. But understanding the right way to go about this can be really critical. And if you want, I can tell you a really interesting story from a client because I always find it's fun to kind of get real with the real world situations. So I had a client many years ago, who was basically trying to figure out what was going on with chat on their website. And they knew that people were searching for things. And they knew that they were using chat to get answers. And they wondered which was working. And what was interesting is they made the mistake of starting too low with the product and basically the product team. And so what the product team did is said, oh, you want to learn more about chat in search? Okay. I'm going to set an event every time someone searches. I'm going to set an event every time they start a chat. And I'm going to capture what was the purpose of chat. And I'm going to capture the search term that they searched. So all very logical, right? They then were told, okay, well, what questions can this answer? Well, here's how many times people search. Here's how many times they do chat. And then they thought they were really cool because they were able to use data to say, here's how often they search. And then within five minutes, do a chat. So we could tell if the search was working. And they were so excited. And it was actually a pretty interesting analysis. They walked around the halls. They started high-fiving each other. Okay. Now you take a step back. Someone else at the company went to talk to an executive and said, hey, we're doing some research on chat and search. And the executive said, they basically said, what do you care about? And the executive said, listen, what I want to know is should we have chat or should we get rid of chat at all? And the way I want to balance this is I want to see how often is chat reducing calls to the call center because that costs me a lot of money. And how often are chats used in the same session in which they do a purchase? Because if I can show the chat, help someone buy something, I'll keep it around. So they went down that path and that was a totally different path of like, how often do people chat and then go to the call center and how often do they chat and they purchase? So what was interesting is they went down that path and the team that was high-fiving each other, they did what I call the bottom approach. But the executive was doing the top-down approach. And if you compare them side by side, the KPIs of how many times did people chat within a five-minute period of search, the executive didn't give a crap about that. But what he did with his KPIs were how much money is chat generating and how much money am I saving in the call center because of chat. And that is an example, a very long-winded example of why if you start with the wrong team, you could actually do a bunch of analysis, but you end up in a place that isn't aligned with what the executives want. But if they would have started at the executive level, they would have found what he or she cared about and it takes you down a totally different path. And it's kind of like, you know, the old movie of, I don't know if you guys ever saw the movie Back to the Future, but it's like there's a scene in Back to the Future 2 where there's like, you know, two different worlds where like this changed and it started a whole different world. And that kind of works like that where you start one chain bottom up, you start one chain bottom top down, and they never meet. And that's why I think it is important to find, I can't give you the answer to what the right team is, but you need to find the right team. Otherwise, you end up spinning your wheels on stuff that doesn't end up being fruitful. Wow. We need to make sure that we do not end up in the multiverse conversation here. Yeah. But I think at the end, it just like shows again, how important it is to create this alignment and how important it is to involve the different stakeholders, right? And that everyone kind of is in the loop. Because if you keep answering the wrong questions that nobody cares about, your data or your analysis won't have zero or won't have any impact at the company, right? So I think the alignment seems to be like really the key factor here. Yeah. In that example I just gave, if before they did all this work on search and chat, they would have kept going up. Or if they ideally would have gotten top down direction from the beginning, then all of that mess would have been avoided. Now, I'm not saying that the information they learned wasn't useful, but it was useful at a lower level versus what the executive cared about. And what's funny is the executive could have ended up getting rid of chat altogether in the middle of them doing all this stuff on chat and search. Those things could have happened in parallel. At the end, if nobody's using it and if you don't have the proper buy-in, even if it's very interesting, it might still end up being useless, right? So I think it's always important to have that figured out first. Yeah. And Christian, I think we've gotten to all of your initial rapid fire questions except for one. So I want to make sure we covered it. We did not get back to AI. So AI is a very hot topic right now. So is there a specific thing you wanted to delve into with AI? Well, I mean, generally my intention with touching up on AI was, so there is like this trend of more and more technologies to better analyze data, right? And also to kind of outsource the manual work that needed to be done in the past. So I'm just wondering how you see this trend developing and to hear your forecast and also maybe the possible upsides and downsides from your perspective. Yep, absolutely. So I am a little bit of an AI skeptic because I've been hearing about AI for a long time and I've seen very few cases where it's actually impacted my life in the analytics world. But I'll tell you where I do think it is effective. And there's a couple examples I can give you, at least in our world, in Amplitude's world. So I think that AI is really useful to find data anomalies, things that a human would take, you know, weeks or months to dig through rows and rows and columns of data to find. That's something that a machine is really good at. Unfortunately, I don't think machines are awesome at figuring out exactly why a data anomaly happens. So like in our product, if you have a trend line of a metric or an event and we see five data anomalies, we give you the ability to click on the data anomaly and then we will show you every property in descending order based on the ones that our machine learning thinks might be the reason why that data anomaly existed. So for example, if you had a huge spike and there happened to be a property that's like a UTM campaign and all of a sudden there's one campaign that really took off, obviously that's going to probably be why it happened. Now the AI isn't going to know that for a fact, but it could at least lead you in the direction. We also have a machine learning capability in our product so that if you have a conversion funnel and people either drop off or convert in that conversion funnel, we can tell you which are the events that are highly correlated with people who either drop off or make it through depending on which view you look at. Now we don't know if we're right, but we could at least try to point you in the right direction. And I think to me, what I view AI as, is a way to do busy work and point me in the right direction, but don't think you're cute or smart enough that you can actually tell me the right answer, because I don't think we're there yet. Now maybe with all this chat GBT stuff, we'll get there. But I think that another example, like if you have millions of users on your site or app, one of the things that amplitude can do for you is automatically create clusters of groups based on machine learning that says, hey, these groups of people seem to be very similar to each other. So we're going to make this cluster one. These are similar to each other. That's cluster two. And then you could dig into it and see why we made those clusters. And if you like the cluster, you could then save it as a cohort or a segment and do some personalization. But again, it's all like leading the horse to water, but not drinking for you, if you will. Well, I would say that answers perfectly my first question of the episode today. And it brings me also to the last question, because I mean, we love to talk about examples and make our conversations as practical as possible for our audience. And I think the advice you gave on going up to the executive level and making sure to collect the right data and the right question was already very practical. But I just still like to ask you for one more advice that you would like to give product people and product teams to make sure that they make the best out of their data collection and data quality. Yeah, I'd say my advice would be to collect less data, but make sure that it's super accurate and it's super actionable, because I'd rather have an implementation that has maybe 10 events and 20 properties than one that has 200 events and 1,000 properties, if I don't trust any of it. And if all of that data being collected is not focused on a specific task or job that they need to fill. And I think my second advice is we talked about earlier would be to reach out to marketers and to understand what they're facing and see if there's a way that you can collaborate with marketers and not just be focused on what happens within the product, but understand how people are finding your product. I have found that when product teams work with marketers, they have some amazing insights that and they look at the world differently than marketers do. And marketers actually have some really interesting insights that product teams could really benefit from. And I learned a lot of this when I worked with John Cutler, who used to be with us here at Amplitude. And he is such a product guy. I was such a marketing guy. And I literally went to visit him at his house one day. We spent a couple of days together. And it was just to me learning how his mind works as a product person. Then he was kind of learning how my mind worked as a marketing person. And we came up with all of these cool ways that each group could benefit from each other. So I think that's really cool. And I'll give you one super tactical example because I love real world examples. And when I joined Amplitude, there was a particular thing that our product didn't do. It didn't do a great job of letting you have a tabular column-based view of data where you could then add properties and just keep breaking it down. And I had seen this in other analytics products I was used to. So I went to the product team and I said, we need to add this as a new data visualization. And they said to me, no one has ever asked us for that. That's not something our customers have asked for. And I said, I don't know. But listen, I'm in the marketing group. And I'm a marketer. And I know what the industry thinks. And I know what the industry is doing. And I also know that there's a whole group of people that we want to market to that are used to this view versus your view. So they fought me on it a little bit. They're like, we really like to focus on the things that our customers are asking us for. And I said, listen, you just got to trust me on this one. I've been doing this a long time. So they built an alpha version, I think to humor me. Then they gave it to a bunch of people to use. And our current customers, they're like, this is really interesting. They got a bunch of feedback. They added a bunch more functionality. And then they decided to put it into open beta. A lot of people started using it. And then it went GA. And now we have 15 or 20 visualizations in amplitude. It is now either the third or fourth most popular visualization in our product. And the reason I bring this up is not to pat myself on the back, but it's more like sometimes the product team, they know what their customers are asking for. But sometimes marketers understand the bigger picture. They understand the competitors. They understand what's going on in the industry. And so sometimes customers don't know what they want. But when you show them something new, then they recognize that they like it. And so that's, to me, what I'd like to leave you with and why I think marketing and product should come together so well. Because I think there's benefits from both teams collaborating. And I think ultimately, the product is better for it. The company is better for it. And I think both teams are better for it. You know, Adam, the one thing that I love hearing this is that it fits so perfectly into our theme that we constantly have in this podcast, which is all about collaboration and breaking down the silos between the different functions and getting them to work together. Because I think we can just all benefit from the skill set of the different teams and experts across the company. Yeah. I would even go as far as saying that I think product people should do internships on the marketing team. And I think marketers should do almost like an audit of the product team. Just like in the old days, they used to say that all the managers at McDonald's had to work behind the counter. I think that would be really beneficial. And I think that more schools and universities should kind of force product teams to know more about marketing and marketing teams to know more about product. I actually think because I have kids who are in college, I think that almost every business student is now learning coding and they're learning more about what the product world is like. But I don't know if the coders are learning as much. It's funny. My son told me last week that he decided that he's an economics major. He said he's going to be a minor in business and computer science. So he wants to do two minors because he thinks that that is kind of where the world is going. And I think part of that is influenced by all the stuff I tell him all the time. Amazing. Well, I mean, I can just say to everything that has been said, Eamon, we need more of that. And yeah, I'm curious to hear what our audience is thinking to that. And don't hesitate to drop us a message. And by the way, Adam, we're going to drop your LinkedIn profile also into the description. And if you want to be visited by any other social media channel, let us know. Yeah, awesome. Well, thank you so much for having me. And as I told you guys, I'm moving to Europe. So by the time this comes out, I'll be a European. So I'll be local. If anyone wants to reach out, I'll be on the continent. Beautiful. I'm sure I will see you in person once you reach Europe. And with that, thanks so much for the time and for the call. Yep. Thanks so much for having me. Have a great day.