 Hello, everyone. Thanks again for joining us after lunch. So we're going to be a very important task of making sure no one falls asleep. My name is Monica Ugui. I'm responsible for the product and design teams at Radar Stack, which is a customer data platform. So let's start by introducing ourselves when we start from you, Vijay. Sure. My name is Vijay Amapathy and I'm a Director of Product at Heap. We're a digital insights product, and we help you understand the customer journey and make it better. My name is Nils Boat. I head up growth and automation solutions at trade.io. For those of you who don't know, trade.io is a low-code integration and automation platform. We know that integration is really important to product teams, and that unfortunately the overhead of delivering on your integration roadmap is often quite high, and so our goal is to help you deliver on your integration roadmap much faster. I'm Evan Michener. I'm really excited to be in the same room with a whole bunch of product people too. This is great. I'm the head of product at Full Story where we do digital experience intelligence. So we're combining the quantitative with the qualitative to help teams make decisions faster. All right. OK, so we're going to be talking about digital transformations, but this is a really complex concept. So in the interest of 30 minutes, let's narrow it down to talking about how data plays a role in digital transformations. So hopefully that little curveball does not cause any chaos for us. And so let's start with the first question. What role have you seen data playing in helping companies along the data majority journey? Yeah, I can start. So I think I can first of all explain a little bit about how we think about data maturity at heap. So it's actually not just about tools or about the data itself. It's about tools, people and processes, right? And so, for example, at one end of the spectrum, you have the organizations ruled by the hippo, right? This is the highest paid person's opinion is making all your decisions. You as a PM are kind of getting yanked around, right? It's no fun for anyone. On the other end of the spectrum, or the promised land, right, is a world where you've built this machine that generates reliable improvement in your business. That's increased revenue, reduced costs. And that comes as a result of understanding and quantifying opportunities up front, making a portfolio of investments and then having some portion of that portfolio pan out, right? And people are all over the spectrum in between these two states. And so in terms of where data can help, I think step one is understand customer behavior. That's the backbone in my opinion is what are your customers doing? What are they not doing and where are they getting stuck? What do you guys think? Yeah, that makes sense to me. I think there's maybe another sort of way we could think about framing it, particularly for this audience. Like you guys are the face of digital transformation for many of the companies that are your customers. And in some sense, data is the love language of digital transformation, right? Like it's the common thing that binds, right? It's both sort of the blessing and the product of it, but also like a huge challenge, right? So if you think about some of the organizations where digital transformation is like a major thing for them, healthcare, highly regulated industries, like this is very scary to them, right? And they need your vision and your guidance, like you're doing things in a fundamentally different way than they are. And they're trying to figure out how to tackle it and your experience and how to think differently about democratizing your data about open platforms and open systems and prioritizing that for them is extremely important. And just being pretty sensitive to the fact that they're very concerned about the health of their data. We all are as well, but it's particularly important for them. Well, I'll build on that a little bit. I mean, who here feels like they have their data under control? Great, glad we're talking about this. I think, I mean, when we talk about digital transformation, I completely agree with both of you. I mean, you're talking about the willingness to kind of challenge how you're creating value today, which is applicable whether your company's 100 years old or in healthcare or something else, or you're literally in a day old startup. So it's constantly trying to leverage the latest technology and data is central and core to that. And then making that data accessible to different teams in different ways. So let's get rolling. Excellent, thank you. So first question is, what best practices or what advice do you have for product managers to be at the forefront of chairing their teams to accelerate their path again on the data maturity journey or to adopt a transformative digital setup? Cool, so I got a whole bunch. I'll preface this with that. And if you want to talk about like 50 other ones, come find me at the boots afterwards. But I'll tell you about one that I'm super excited about that we've been using a lot recently, which we call the work backwards plan. And so essentially what this is, is you can think of it as a lightweight financial planner for your quarter and for hitting your OKRs. And so by the way, important thing to think about too, for all of you is if you're kind of living in a world where your OKRs are like ship this feature or do this thing that's like kind of zero or one, they're like push the code or I didn't push the code and it doesn't have a tie to impact, then at some point you really want to start revisiting those goals and trying to orient them as clearly as you can to business impact and ideally revenue I would say. And so let's say for example, you do have a goal that's oriented to business impact like revenue. The whole idea with the work backwards plan is that you're not in this intermediate state where your quarter consists of one bet that is trying to drive that impact. And by the way, it's good to make progress to be in that state, but you want to get to a place where you have like three or five of these bets. And if any two or three of them pan out, then you are going to reliably hit your goals, right? And so what a work backwards plan does is you look at all the different things that you want to change. You look at what the impacted population is, right? What's the reach of those changes? And then you start to rank things based on, this starts to actually, this is skewed heap towards orienting ourselves towards smaller changes that are further up in the funnel and that are reaching far more users. And it's allowing us to achieve smaller bits of incremental improvement far more reliably than before we were using this tool. So that's why it's kind of my favorite pet tool of the moment, but we can talk about lots more later. So I think the question was what advice might we have for product teams who are thinking about and helping companies? Best practices. Best practices, please. Okay, so I evaluate a lot of tools and I actually often start in developer docs. So I'm a huge, huge, huge advocate for highly open platforms, right? So you better give me all the web hooks and all the rest APIs. I wanna be able to do everything that I can do inside your application through an API and I want really good documentation, not gated, highly available, highly open because honestly, there are just so many people on the front lines of different types of businesses and there's different types of ways that those businesses are trying to work and make your product work with inside their organization and it takes a very open mindset and you don't know how they're gonna do it. So you better be pretty open and both sort of technically speaking and documentation-wise on how to actually interface with that open platform that you're creating. Well, I'll add one other flavor on top of both of those and it's you need somebody to champion data. It might be you, whether you like it or not but you need somebody in your organization to champion exactly what we're talking about here which is getting that data open, accessible, making sure people trust it, know how to get to it so that teams can move faster and one of the things that we try to do at Full Stories make that as easy as possible for you. So since we have a room of product folks here, the last time that you shipped a feature or capability and then the next day you realized, oh, I forgot to tag it and all of a sudden you're missing analytics and missing measurements and CEOs asking what the numbers are and you don't know that's one of the things we try to do it at Full Stories, help the data champion out. We use a technology called auto capture which means you don't need to explicitly tag and instrument all of these things. So it's one less thing that data champion needs to work for but that's, I guess just one practical bit of advice is you need somebody internally to help champion the data. That's such an important point. I actually want to drill in for the final statue, Evan. The concept of having a champion is crucial to having broad scale change happen on teams. So now very specifically to product managers, how do you, as a product manager, how do you influence your entire team to rally around a goal? Let's say you're trying to up level your data platform. Well, I'll take a stab here. It's not unlike what we do every day which is building product piece by piece by piece and you're kind of decreasing risk and increasing confidence as you go. So I guess my encouragement is just look at it like you're building a product if your product is building trust in the data. Great. So I might take a slightly different take on it, right? So I think there's a bit of a skills gap out there. So we've all gotten to this point where data is highly portable, right? So there's this sort of great abstraction happening, right? Of course, we're data platforms where they're sort of acting as a hub to be able to facilitate and move it around and all these sorts of things, right? It's far more accessible than it is today, but if you get like on the front lines of an organization, what you'll find is that there's still a lot of walled garden mentality where there's people who really see the promise of what's possible, particularly again in some of these legacy organizations where they're just dealing with all kinds of challenges and using their data. So there's a lot of people who see the promise but we haven't prioritized the skill sets on how to actually work with it. Like how do I work with an API? How do I write some basic SQL? And that skills gap is applicable both to these big organizations trying to do digital transformation, but it's applicable honestly in a lot of product organizations like product operations folks, there's a huge, huge opportunity for them to become more literate on using data and take advantage of some of these new, far more accessible platforms on how to get it and move it around and work straight process. And if we prioritize those skill sets, like you're gonna find that innovation and the digital transformation is gonna snowball. So I think we need to start prioritizing, teaching more people the skill sets required to take advantage of it. Yeah. And I think, I'd say a couple of things that I'd add to those, right? One is, I think it's really important to find a good foothold in terms of a pain point. It's really, really easy with data to boil the ocean and try and solve all of your company's data problems at once and I've never seen anyone do it successfully. And so I recommend that you figure out, what is a dire problem that you wanna solve? And I'd say also, don't just think about the product team. Think about the constellation of teams that surround product, right? So you have customer success, you have support, you have growth marketing, right? All these teams are very adjacent to product management and oftentimes when you're setting up the set of tools and processes to up level, you're actually benefiting more than one persona, more than one group here. So think about both of those when you're figuring out your foothold of how to start delivering impact and you'd be surprised how quickly you can scale up from there. Such great advice. I really like an underlying thread here, which is when we think about data, we're getting so much better at extracting tons and tons of data. There are all these companies that are building amazing products to process. And so it seems very logical that we should use data to make decisions, at least to inform decisions. But I have noticed as a product manager, it's really important to remember that you're trying to drive change, you're trying to change the behavior of your team. And so even though you're talking about something very logical, it's still important to understand you're dealing with human beings on your team that may not be familiar with all the new systems or all the new ways to gather data. And so make sure your EQ ears are perked and you're understanding all of the stakeholders, all of the different parties on your team that might be having resistance. Get to the fear or the source of concern and make sure you address that and don't assume that it's gonna be in no brainer to push forward like your strategy. But amazing, that leads me to our third question, which is what's your advice for combining qualitative and quantitative signals in decision-making? Second, I can start to take this one. I think that, so first of all, I would say that qualitative and quantitative data are extremely complementary, but they have different strengths. So I think quantitative data is much better at helping you find out where you wanna focus, right? The customer journey can be very broad. Your customer segments can be very varied and it is really easy if you start with just individual anecdotes to find yourself in a situation where you're fixing something that was frustrating one person but didn't actually impact your business as a whole. And if you rinse and repeat and scale that out, you get largely nothing. And so it's really important, in my opinion, to start with using quantitative data and quantitative tools to help you figure out where you wanna focus. And then qualitative tools really shine, in my opinion, at helping give you ideas for what you actually wanna do about it, right? What experiments do you wanna run? What do you wanna change? It's so much easier to step into the user's shoes and actually experience what they saw to help you get to that next step of, okay, here's the experiment I wanna run. And so they're very complimentary in that way. And full stories kind of started on the other side of that, more on the qualitative. So starting with session replay where you're observing what the user's doing which is kind of the ultimate empathy generator, by the way, kind of like you mentioned, PJ. So, and then we've moved into the quantitative as well over the past couple of years. The power of that is that you can get in there and see, oh, here's a problem or here's a bug or here's a friction point somewhere where the user's got it frustrated or you have an opportunity to improve that. And then immediately say, what's the scope? Like what's the impact of this thing? So you're hopping directly into the quantitative view of that qualitative piece. So that's, I mean, the power of quantitative and qualitative is together, you know? And huge full story fan, by the way, we're customers and I think you're 1,000% correct. Like there's really interesting platforms now. Full story is a great example where you're layering both qualitative, right? You have a visual sense of what somebody's actually doing with quantitative and how fast is the screen reacting and where the error point's happening. Gong's another great example of this. Gong is like my favorite qualitative tool ever, right? You can literally get in there and hear in your customer's words exactly what they're saying when they're first coming into the funnel versus what's happening when their account manager is talking to them a year later. And so I tend to be of the mind that qualitative is where I tend to start and then I use quantitative to basically validate the qualitative insight. And so if I'm hearing a pattern that keeps coming up on a Gong call or seeing something that keeps coming up in full story, then I can develop that into a hypothesis and then go find the data and say like, okay, edge case or common, right? And it truly is those two things converging that makes for good insight. So absolutely like get out there, listen, watch the full story sessions, put it together, come up with your hypothesis, validate it through quantitative and whatever you change then measure also after. And Gong's a great example too on whether it's customer support or on the sales side, being able to talk to a user and then immediately tie that back to what they were just doing on your mobile apps or website. That's like the whole world opens up because all of a sudden you're actually seeing what the user's talking about instead of trying to play that translation game. So yeah. Yeah, actually I really like, I like Gong. I think one thing I would also mention too is I think Surveys and Voice of Customer is a really underrated channel. I think a lot of times often people will jump to, you know, they'll jump to like starting with customer interviews, which I think is great. It's the accessible thing, especially when you have a sales team, it doesn't involve learning any kind of new tool because you're just getting on a call face to face. But it is really time consuming. I think I'm a big fan of if you have access to some level of scale, I think Surveys are really powerful in terms of speed, low effort, low time commitment. If you need a decision on a relatively rapid time scale. And I think also just to speak a little bit more to the idea of speed, I think an important thing, especially when you think about qualitative data is the rituals that you have in place, like the weekly watch session meetings and things like that. It's really important to think about how much time you're spending preparing for those moments of insights versus the time that you're spending experiencing them. It's crucial to find yourself in a situation where you're not spending three hours of time to prepare for like 10 seconds of value. And so that's where I think a lot of times the merging of that quantitative and qualitative is really important because the quantitative can help reduce that search time. So you can get to those moments of value a lot faster. I like to point on the rituals, like key in Surveys as well, right? Like distribute that information very publicly. Like I think in some of the organizations I've been in, like there's like one group who's responsible for like combing through the survey data and then like distilling that into insight. And again, that's that walled garden kind of mentality to that qualitative data, like put it in a Slack channel. Like literally every time that somebody fills out that survey, it needs to be in a public Slack channel. And every time like this keyword shows up in Gong, like link it straight in that same public Slack channel so people can watch it, right? And then create a culture in which people are just throwing that insight out there because you never know what people, what information people actually don't have. A lot of people inside an organization don't have direct customer insight. And so distributing that out to them in a very public manner is a ritual that in cultural precedent that I think like you should all be thinking about. I love this idea of speed too because I mean, looping back to the original question around digital transformation, the intersection of data, speed is a metric of success that you can use internally. Like how quickly can you act on an insight, quantitative or qualitative learning? And that's, I mean, that's the product loop is learn and act, learn and act, learn and act. So speed might be one of those metrics of success as you look internally to is the data out there do people know how to get to it? Is it accessible? Is it trustworthy? Do people, are they gonna spend the next 10 minutes arguing about whether it's the right data or is it accurate? So speed, crucial measure of success, yeah. Amazing. Well, we're right about time. So I'm going to ask you one last question. You have a massive audience in front of you. What advice do you have for aspiring product managers? Even regardless of data specifically? Yeah. I think the number one thing I would say is try to spend some time thinking like your CEO and thinking like your investors. And what that means is don't just think about the domain or the feature or the page or whatever that you start out in your organization owning because that's gonna change over time. All of that's gonna feel like a blip on the radar in the rear view mirror like as the years go by and your scope of ownership is going to increase. But if from day one you understand how does your company generate revenue? How does it tick? How do your digital experiences feed into that? How does my page in this workflow and this part of the customer journey drive impact for my business and you view your job and you operate with that lens? You're gonna climb that ladder a hell of a lot faster than everybody else. So that's my advice to you is zoom out and think like your CEO and your investors. So I've had the privilege of meeting a few of you especially on the aspiring side of this since one of my favorite conversations to have. So I guess I would, I love that P.J. too. I would encourage you to keep the curiosity alive. So much of product work is like the core DNA is curiosity. Are you staying curious about the customer about your product especially if it's a product that you happen to use every day? How do you stay curious about that? So you don't overlook the speed bumps that your users are feeling. And for the aspiring PMs here, all product work is combining the business outcome with the customer need. And you can do that, no matter what role or title you have today, you can find ways to do that. So stay curious, especially right now, it's times are bizarre, the economy's hard. It's super easy to get that tunnel vision on shipping, shipping, shipping and projects and lose the greater focus on the customer, which is what this whole thing is about anyway. So my advice would be stay curious. Yeah, I'd agree with that. So I don't know if it came through. I'm not a product manager by trade, but I'm quite the curious individual, right? I like to play an experiment with different technologies. And I've always approached my career as I'm not gonna really ask for a lot of permission. Like I'm just gonna go for it. And I think that mentality in your career goes a long way. Like there's a lot of people I see who come in young in their career sort of looking for permission. I like to kind of like, you know, try it first and not necessarily, I'm not saying like don't communicate or anything like that, but just be bold, right? Like don't always worry about asking for permission first. Like just kind of go grab it. Those are the types of people I think that really thrive and grow inside organizations, right? They just kind of have this entrepreneurial go get it mindset. And people notice it, like managers notice it. You get more responsibility, more impacts on your organization. And next thing you know, you know, your imposter syndrome maybe builds a little bit, but then you're going, right? You're doing it, you're making it happen. And you learn by doing. And so just go get it. Amazing. I think we must end on that, go get it. Thank you very much. Thank you. Thank you.