Episode 142: Mastering Data-Driven Product Management with Bethany Lyons, Chief Product Officer at KAWA Analytics
In this episode of Product Thinking, Bethany Lyons, Chief Product Officer at KAWA Analytics, joins Melissa Perri to unveil the role of a data-driven product manager in early-stage startups. They explore the world of operational intelligence, the importance of identifying high-demand customers, and the balance between pioneering and optimization in startups. Bethany also touches on the crucial aspects of seamless onboarding during early-stage product development and highlights the significance of choosing the right customers when scaling.
You’ll hear them talk about:
[24:21] - When determining KAWA Analytics' target customers, Bethany prioritizes those who show a high willingness to pay and an urgent need for their product. For instance, hedge funds value KAWA due to its capacity to improve their critical daily operations, such as risk and position analysis. KAWA aims for users who employ data in operational capacities rather than just for occasional insights, leading the team to coin the term "operational intelligence" to define its niche.
[27:38] - In startups, being data-driven might not always be viable due to the lack of data. Instead of focusing on quantitative metrics, lean into qualitative insights. Rely on vision, understanding the problem, feedback from user interviews, and product market interactions. While data can optimize, it can't tell you what to do. Therefore, startups should focus on pioneering over-optimization.
[31:18] - Seamless onboarding for their product is key to KAWA. The team prioritizes understanding user behaviors through screen shares during these early stages, ensuring users learn the product without requiring assistance. In the future, they aim to introduce event-based data collection to understand the user's journey and any deviation from the ideal onboarding course. Their primary metric for product success is user activation, indicating when a user derives value from the product. However, defining the point of value realization remains a crucial topic KAWA is still addressing, whether it's grouping a view, sharing, or building a dashboard.
[42:08] - Bethany believes that the most important product decision is choosing the first set of customers. Onboarding customers with a highly overlapping shared problem ensures easier scaling, as catering to diverse needs makes the process way harder. Moreover, recognizing that different company stages may require different leadership styles, Bethany mentions the possibility of her role evolving or even hiring her replacement in the future, something every business leader must consider at a certain scaling stage.
Intro - 00:00:01: Creating great products isn't just about product managers and their day-to-day interactions with developers. It's about how an organization supports products as a whole. The systems, the processes, and cultures in place that help companies deliver value to their customers. With the help of some boundary-pushing guests and inspiration from your most pressing product questions, we'll dive into this system from every angle and help you think like a great product leader. This is the Product Thinking Podcast. Here's your host, Melissa Perri.
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Hello and welcome to the Product Thinking Podcast. Today we're joined by Bethany Lyons, who is the Chief Product Officer of KAWA Analytics. KAWA Analytics is a data analytics and operational intelligence platform, which helps its clients make data-driven decisions and provide access to key insights. Bethany has a wealth of experience and expertise in product and data analytics and different size companies. Previously, she worked at a Series B scale at Mews, and before that she was at Tableau from when it was an unknown startup all the way through its enterprise phase. Welcome, Bethany.
Bethany Lyons- 00:02:10: Thank you. Really delighted to be on the show today.
Melissa Perri - 00:02:13: Thanks so much for joining us. So, can you tell us a little bit about your background, how did you get into product management, and specifically, how did you end up working at all these amazing data analytics places?
Bethany Lyons - 00:02:26: Definitely. So how I got into product management was through unrelenting persistence. If you want to move into product management, it's always a lot easier to transition in an existing company than to try to get a new company who you have no reputation with to hire you into an entirely new career. So I've been in London for my whole career in the UK and Tableau was obviously a West Coast, kind of Seattle based company. And so the thousands of people in Tableau's R&D team were in the US and they had no hiring people outside the US Policy for product management. So I had a pretty big barrier in place there, but I didn't care. I was like, I'm going to work on the product, whether I'm allowed to or not. So I went out and brought one of Tableau's best product managers on this cross European trip to about 30 different customers who all had the same problem. And then we wrote this pitch for why we should invest in data modeling in Tableau. It got approved for funding by Adam Selipsky. They staffed the project with like a hundred people. And then Roger quit. He was the PM I was working with on this and the project was then left without a leader. And so then there was a crisis of like, “oh no, we've just funded this huge initiative and our key champion is gone. Bethany, do you want to step in and now take over? Because you're one of the only people with context about this problem since I'd been working on it before”. So that was how I got into product management. They ended up like over making an exception to their policy of like requiring people to be US based. A bit of luck, a bit of skill, being in the right place at the right time. And then yeah, to your second question about analytics, that was also like kind of you make your own luck. So I got actually the very first consultants in Europe at Tableau did the exact same master's program as I did at LSE, but just like a couple years ahead. So he came recruiting in our pool of brads saying like, “hey, we're hiring at Tableau”. And I was like, “I've never heard of this, but looked at the product, thought this looks great”. Like my background was in operational research and math. So it was, I'm kind of like a quant nerd. So it seemed pretty aligned. And then yeah, took him up on his offer and the rest is history.
Melissa Perri - 00:04:48: So with all of these different companies too that you've worked at, they've been a lot of different phases. Like you said, Tableau was early stage, nobody really knew about it. And then it's scaled, it's huge, right? When you left, how big was Tableau?
Bethany Lyons - 00:05:01: So the engineering side was about somewhere between 3,000 and 5,000. It was massive.
Melissa Perri - 00:05:07: Yeah, that's a massive company then.
Bethany Lyons - 00:05:09: And when I joined, the entire company was only about 500 people.
Melissa Perri - 00:05:15: Definitely big changes there. What have you observed working from an enterprise perspective and then going into a startup? You were employee number 10 at KAWA, then you were at a scale up. Like what's different about product management that you've observed between those different phases?
Bethany Lyons - 00:05:31: Like absolutely everything. So in Tableau, it was all about stakeholder management and alignments, which is a fancy way of saying budget defense. Like, “don't cut my project”. And so yeah, providing constant evidence for why we should continue to fund this hundred person engineering effort was like so much of what I did at Tableau. Whereas in a small startup, there are no stakeholders to manage. There's only runway to manage. And so the job is totally different. It's how do we like protect and extend the runway of the business is like the number one kind of top of mind kind of existential question that I think about every single day, day-in and day-out.
Melissa Perri - 00:06:16: So when you were thinking about defending your Budget at Tableau, what types of scenarios would you get into where they would want to cut it? What was that like? What was that process?
Bethany Lyons - 00:06:26: So I think part of what made Tableau a complicated place is that when you have thousands of people working on one product, which is fairly unique because I think in other companies, those thousands of people would be working on multiple product lines. We had thousands of people working on a single product and so everybody wants to advance their part of the product. And so in a way it creates a bit of a competitive culture of like, why should we solve this problem over this other problem? And you need to be able to defend what's going to deliver the highest value to the customer and ultimately to the business. Like what's going to enable people to scale? So that was why.
Melissa Perri - 00:07:07: So you're coming into KAWA, you're now employee number 10. What's your day-to-day like that's different at KAWA than it was in the enterprise? Like, what are you more focused on, do you think, at the startup?
Bethany Lyons - 00:07:19: So the number one question that I'm responsible for answering, as defined by me. I wrote my own job description is who is the customer? I think like the most important product decision that you ever make isn't anything to do with product. It's which customers do you onboard initially? Because they end up shaping your marketing, they shape your product vision. This whole idea of like, “oh, you can just build whatever you want is so not true”. You have to build the product to solve the needs of the customers that you onboard. And so, so much of what I do is try to understand like different customer or prospects problems and see like, is solving that problem aligned to our vision? And if so, then I probably want to onboard that customer. If not, I don't want them as a customer. And so what that means is, I'm actually responsible for sales in our company. We don't have any salespeople. And it's because who is the customer is not something I'm willing to outsource as it's the biggest product decision we'll ever make.
Melissa Perri - 00:08:26: So when you're trying to figure out in these early stages too, who your customer is, what's your approach? Because if you're building something like an analytics platform, there could be many people. And you know, KAWA from what I understand is for business users. So like you could go after any size company, any type of users within the business with the data. Like how are you sifting through and trying to figure out which people are core and which people are our target?
Bethany Lyons - 00:08:53: Yeah, it's a great question. So what we look at is willingness to pay and urgency of the problem. I'll give you an example. In a hedge fund, the willingness to pay is extremely high because the day in and day out operations of like traders and portfolio managers is analyzing risk and analyzing their position. And they do that all today in like very manual ways in Excel. So we bring like scale and automation, collaboration, security, governance, like a whole bunch of things that they don't have today to their workflow to enable them to run their business in a much better way. So yeah, willingness to pay is super high and urgency is super high in a hedge fund. Then we have other people who are also like Excel users, but they're not using spreadsheets to do their job. They're using spreadsheets to measure things that are like tangential to their job. And so that use case is like kind of less interesting to us, because it's like maybe once a month, they run some numbers to check their performance. And it's kind of like, you're not materially moving the needle on your outcomes. You're just like measuring them. So we're less interested in those use cases, which is why we invented this term operational intelligence to kind of narrow down like we want to be the analytics platform where like in the circumstances where data is being used in a very operational capacity, not just to like generate ad hoc insights.
Melissa Perri - 00:10:24: For like operational intelligence and using it in an operational way. What's different between that and ad hoc? How are your customers using your platform?
Bethany Lyons - 00:10:32: Like an operational use case would be like a trader in a bank or a hedge fund. Like every single day they're using our tool to make decisions about investments. So that's like a very operational use case. It's driving the core decision making of their job. Whereas an ad hoc use-case would be like, I want to make an argument to like get a raise and so I'm going to prove that I've like hit some metric so I can tell this story to my manager about why they should give me a raise. That's like not really an operational use-case. It's like an ad hoc use-case where it's like maybe helpful to that person, but like we can't turn around and say like at the end of the day, here's how we advanced your business.
Melissa Perri - 00:11:18: That makes sense. It's like, and it's also sounds like the frequency, like the first use case with the hedge funds, they're using it every single day. It's like a core part of their workflow. Whereas the other ones, it's like, “oh, let me go pull up that platform that we have that we forgot about that we're paying a subscription for”. So those people probably aren't quite as sticky as the ones who use it.
Bethany Lyons - 00:11:38: Exactly. 100%.
Melissa Perri - 00:11:40: So when you're in a startup environment, the question I get asked a lot on this podcast is, I want to be a data-driven product manager. I want to do it right. I want to use all the analytics to inform my next decision, but I'm working in a startup. We have no data yet. We just launched. We're still figuring it out. Or I launched a new product line inside a very large company or new feature inside a very large company, and I don't have the metrics. How do you make decisions in the absence of data then?
Bethany Lyons - 00:12:12: So I view data as it's applied to product management as an optimizer. It's like if I want to get a 1% improvement out of something, I'm going to use data to do that. But data is never going to tell you what to do. And if you're optimizing in a startup, you're not on the right track at all. Being in a startup is about pioneering, not about optimization. So I'll just be blunt. We just don't use data right now because there isn't any. We have to use a vision and an opinion that we have about the world and our intuition of the problem space and a ton of qualitative feedback. The data that we use is 100% qualitative. It's from interviews with people. It's like going out and trying to sell the product. If people don't want to buy it, don't force it. We're trying to find where's the pole and where are we pushing. And if we're pushing, then we just withdraw completely and say, “okay, that's just not something we should be doing”.
Melissa Perri - 00:13:10: I really like this because I think, I always advocate for people being data-driven product manager. But what I've seen in a lot of places is that people are waiting for data to just give them the answer. And like you said, it won't give you the answer. It's an optimizer thing. And I've seen both leaders and product managers who are junior say, “oh, we don't have the data. I can't act. I can't make a decision”. And it paralyzes them. And it makes the team not go anywhere. It makes them not actually move. So I like what you're saying here. On the catch side, though, I wonder, how do you balance making sure that you're doing the right thing, measuring it along the way, and seeing that? And it sounds like in the early stages right now, you're using qualitative data, which is data. It's just not things that we instrumented in our platform. It's qualitative data. You're using the qualitative data for feedback. How would you suggest people make sure that they're building the right thing along the way as they start scaling and measuring data? And how should they be looking at that to inform what to do next or how they're progressing?
Bethany Lyons - 00:14:14: Yeah, I would say you need to lay out the questions you want to answer before you instrument. Because I think some people are like, let's just collect all the data we can possibly collect, rather than approaching it with a hypothesis-driven mindset. So I would first come up with the hypotheses of what are the top 10 questions we need to answer about our product, and then do the instrumentation of data. Because in some ways, you then create accountability to come back and actually answer those questions when you do have a steady stream of data. And I've worked at companies that instrument everything. They're the least data-driven companies because they've never created questions to be accountable to. So I would say don't try to be data-driven, try to be hypothesis-driven. And then it's being data-driven as a consequence of that.
Melissa Perri - 00:15:06: Yeah, that's a really good way to put it. Be hypothesis-driven. Actually know what questions. I think that's a big part of actually being a good product manager is it's not about actually doing the analysis on the data. It's picking the right questions to be asked. And then using a tool like KAWA or something to help you figure out what the answers are. But I think people get that wrong. It's like it's not doing the analysis. It's about actually asking the right questions. So when you're looking at KAWA, you're in early stages right now, you're figuring out who your customers are, are you thinking about data for scale? How are you thinking about measuring progress in an early stage companies? You're using qualitative feedback right now. What are you doing though, as the Chief Product Officer to make sure that you do hold yourself accountable, like you were saying, and going back and checking it and measuring progress and making sure you're on the right path? Like, what are you instrumenting now to do that later?
Bethany Lyons - 00:16:00: So our, like, one of our number one product priorities is to have totally seamless onboarding. We don't want people to have to interact with us, like, as they're learning the product. So the moment we're doing a ton of screen shares with people to see what do they do when they get started in the product, in the future, we're going to want to instrument, an event collection to figure out what's the sequence of actions people are taking and how far does the path that they take deviate from our ideal path to get to kind of activation and, like, realization of that first value of product. So yeah, it'll be figuring out data is just listening at scale, and right now we don't have a scale problem, so we're able to just listen to individuals. But the first place that we're going to instrument data is definitely in the collection of events to track our onboarding experience. And the reason for that, just to clarify, is the number one kind of product driver of revenue is the user activation, so the user reaching the point of value in your product. So that's the metric that we're going to optimize for. And we haven't yet defined what is an activation in our product, so that's another thing we need to think about is, how do we know somebody's realized value? Like, is it when they group review, is it when they share review, is it when they build a dashboard, is it? We're not sure. That's a big open question is, what's the value realization point?
Melissa Perri - 00:17:29: I think that's a really hard question to answer. And I've seen people struggle with it too. What's your process for figuring out what that is? What are you looking at? What's going to tell you like, yeah, this is it?
Bethany Lyons - 00:17:41: That's a good question. I think it'll be correlating user activity to account revenue. That's a hard data problem. And I think, again, we're still in the early stage of defining our ICP, so it's not something we've tackled yet. But it will definitely be something that we'll be looking at in the future.
Melissa Perri - 00:17:59: So it's like, get all the people onto the platform, let them just go, let them use it, and then we're going to look at some lagging indicators later and just see how they correlate back to how people are using the platform. When you're trying to figure out to your core value proposition here, I guess in general too, and it could be for KAWA or somewhere else you've worked, but how do you know what's the core? And how do you make sure you're not getting distracted on building a bunch of features and requests that people are asking for? I'm sure with the qualitative data you're getting right now, everybody's like, “oh, it'd be cool if it could do this, and it'd be cool if it could do that, and all this stuff”. How do you sift through that noise and make sure that you don't stray from what's really going to provide that value?
Bethany Lyons - 00:18:41: So we're fortunate at KAWA that we have like really strong agreements between myself, the CTO, and the CEO as to what our core value proposition is. And our core value proposition is actually exactly the same as what Tableau's core value proposition is, just for a different persona, which is putting business logic in the hands of business users. So anything around defining calculations, turning a question into a computation, that's our core value proposition. And something that would be adjacent to that is then setting up a workflow trigger. So that's a place that we can explore expanding the product, but it's not the core and the essence of what we do. We're really a user-friendly way of defining calculations on data. That's it. So I think having that clear vision from the outset and having agreement among everyone makes it like, it's not really ambiguous for us. I don't know how to generalize that to other companies. When I was at Tableau, I worked in consulting before I became a product manager, and it was just like very obvious that that was the value of Tableau as well as it enabled analysts to define business logic instead of relying on IT. And that's why, yeah, I always worked in the calculation team because I'm like, actually the core value of Tableau's calculations, not the visualizations. The visualizations is just the marketing engine of the company.
Melissa Perri - 00:20:06: I think that's smart too, if you hone in on that and look for that in your career. Because I see a lot of product managers end up in companies or different parts of the product that are not the core value proposition. And then they struggle with what you were talking about, like defending the budget, not getting cut, being seen as valuable in those different areas. That's really hard, I think, if you're working on adjacent type things.
Bethany Lyons - 00:20:29: Yes, and if I were to be truly honest, I would say like, so the project that I worked on in Tableau is the only surviving initiative, like post-salesforce acquisition. Almost everything else has been killed. If I'm being really honest with myself, it's not because my budget defense skills were amazing. It's because I just happened to work in the area of the product that was the core. And so eventually the company converged on the fact that, “okay, this is our core value proposition. Let's invest in it”. So that would be my advice to product managers when you're picking a job is do the thing, do the core thing, don't be an adjacency. And so don't just pick a company, pick a role within the company that's intimately tied to the core value proposition. In all honesty, I didn't last very long at Mews, I was only there for 10 months, nine months, something like that. They're a hotel tech company and I was working in analytics. I was the most adjacent to the core mission of the company than anyone. That was why I left. And I was like, “I'm not doing the thing. Why am I here?”
Melissa Perri - 00:21:33: That's a really good tip, I think, for people who are out there looking for it, what to do. And if they're in an adjacency or feeling like, this is not working, or I'm having trouble defending my budgets, or I just don't feel connected to actually look for the core and try to transition. So when you came into, so when you joined KAWA, you were employee number 10, but not the first product manager. There was somebody else there, but you were hired in as a product leader, and you worked really closely with the founders still who were there. I know this is a tricky thing for a lot of product leaders, right? You've got founders, it's like, this is their baby. They have got a lot of stake in this. How do you make sure that you're still on the same page with the founders? How do you kind of navigate that as the first product leader in a small company?
Bethany Lyons - 00:22:18: So I should tell the story of how I ended up in KAWA. So I left Mews because I was in adjacency, and I didn't have an X thing. I didn't have, “I looked around at other companies, and there wasn't a company where I thought gee, I really want to join that”. So in my period of profound unemployment, I just declared myself a founder. And I was like, “I'm going to build my own company”. And I started doing a bunch of market research, customer research to figure out what problem am I solving in this hypothetical company? And then I was just posting on LinkedIn every day about my findings and my experiences. And Sam, the CEO of KAWA, came across my post, and he was, like, “hey, you're writing about my company. It already exists. We've been building this for a year and a half. We've got all this funding. We've got it's a whole team and half a million lines of code. What are you doing this on your own? Come join us”. So I think I have a fairly unique experience in like, I joined a company where I have a very strong shared vision with the founders. I haven't had the issues that other product leaders have where they're butting heads because they don't have a shared vision. So my advice is, I think it's all about picking the right company and the right founder. And if you're in a situation where you are butting heads, do something different because that's an insurmountable obstacle. It's the way I would see it.
Melissa Perri - 00:23:42: So what'd you do to like, I know you were writing about this on LinkedIn, which is amazing. So the founder could see your perspective on everything. What'd you do when he called and wanted to sit down and just go through, you know, your shared vision? How'd you make sure you were like aligned or, and I guess what are acceptable places to, I guess, to digress or have different ideas or say like, “hey, but what about this?”
Bethany Lyons - 00:24:04: So I actually thought he was crazy. He was just describing the features that he built. He's a very technical guy. He's not a marketing-oriented guy at all. This guy used to run an IT department in a bank. And I was like, “what have you built?” And he's like, “I love my product”. And I was like, “everybody loves their product. That's not a selling point”. So after this conversation, he's like, “let me give you a demo of the product”. And I was like, “sure, whatever”. And then I was so convinced this guy was off on one that I showed up 20 minutes late for our meeting. I almost didn't go. And then within five minutes of him demoing the product, I was like, “oh my god, this man is a genius”. I have worked with some of the best data UX people in the world at Tableau. And he has surpassed them. He has built the product that Tableau wishes it could build, but never succeeded at. And it's because he has this joint understanding of design and data. And that communication gap, like even with the 3000 people in Tableau, they never got there because the people who understand design don't understand data and the people who understand data don't understand design. So I was like, I don't care how many disagreements we have, like I can work with you because you are like best in class, best in the world at Data UX. I think if you have like enough of a pull in one direction, then you can overcome anything, like any disagreements.
Melissa Perri - 00:25:30: That's amazing. That's such a hard skill to find too, like somebody who can do both the design pieces and the technical pieces when it comes to data. Modeling data and visualizing it is such a tough skill. So.
Bethany Lyons - 00:25:42: And he can manage people. Like, he ran a 600-person team at the largest bank in Europe. I was just like, how can so many skills exist in one person? It's just, it's outstanding.
Melissa Perri - 00:25:53: So it sounds also respecting your founder very well. It's helpful.
Bethany Lyons - 00:25:58: If I could have like dreamt of a boss, I wouldn't have been able to dream up who's that.
Melissa Perri - 00:26:03: Amazing. So definitely finding somebody that is awesome, somebody that you would want to follow into this type of work, that definitely sounds like a great treat. So you, again, employee number 10, walk in here, you've got one product manager, and you come in as a Chief Product Officer. In early stage companies, how do you think about diving in, getting your hands dirty, right? And then scaling the team for the future. And how do you make sure that you are doing things that will help scale rather than getting too sucked into the weeds?
Bethany Lyons - 00:26:35: I guess this is somewhat repetitive of what I said before, but I truly believe that the most important product decision that ever gets made is like, who are the first customers that you onboard? And if you onboard the wrong customers who don't have a very high overlapping shared problem set, it becomes impossible to scale the business because every customer is asking for different features and that has different problems. I really believe that getting the right set of customers on the platform with the shared overlapping problem is the way that you scale the business. That's my view for now. I don't have a great long-term view of like this, what's our hiring plan in three years from now. There's just too many other brewing problems to think about that. But I also think it takes a different leader at different stages of the company. And so it's not beyond the pale that I would hire my replacement at some point because I'm definitely a more of a pioneer than an optimizer. So yeah, I don't know if I would be the right CPO for KAWA when it's a multi-billion dollar company. I think I could be great as an individual contributor doing more customer research at one point. So that's a potential opportunity in the future.
Melissa Perri - 00:27:48: I think you're bringing up a great point here too. No, like you said, the leaders at different stages are all different types of people. And I don't think people really recognize that because I've worked with a lot of companies before where somebody's been there from day one. They were called the Chief Product Officer because they were the first product manager. They were there from day one, maybe the second. And now the company is 500 people and they've got four different product lines and they're starting to scale. And then the problems and the challenges of that scale become different. It's not figuring out who our customers are. It's about building a team and like setting up operations and working with the board to justify your actions and make sure they can get Budget and like fundraise. And I don't think a lot of people recognize how different it is to go from very early stage to go to scale up and then even enterprise, where you got to be a political genius in some places. Which I am not. It's nice to hear you talk about that because I think a lot of people get to that point where they grow up through this company and they love it and they don't want to leave it, but they're also they feel by not being the Chief Product Officer, right, when it hits that 1000 person mark, the 500 person mark that they failed, right? They see it as a failure instead of just a different phase.
Bethany Lyons - 00:29:08: Exactly. And I'm really glad to be on this podcast because I'm reaching an audience of my potential future bosses. So, anybody out there who works in product and data analytics, who's worked in massive companies, please reach out because I might want to hire you as my boss. Love that.
Melissa Perri - 00:29:30: I like that. It's so refreshing to hear somebody talk about that too. So with KAWA and with being a leader going through all of these different phases of thinking about who's our customer, what are we building, how do we focus on the core, I feel like I have to ask this and I keep asking a bunch of product leaders this. AI.
Bethany Lyons - 00:29:45: Oh.
Melissa Perri - 00:29:47: Yay. I just feel like I have to perch it. So you've got all these wonderful things out there, AI, ChatGPT, all of that stuff. How are you thinking about it?
Bethany Lyons - 00:30:00: So we're thinking of it as the future of user interfaces in a way. It's not that the user interface will go away. It's just that for 80% of the things, you'll interact through a chat and then you'll still have the user interface for doing like more advanced configuration. That's the way that we're thinking about it is like an assistant that makes you not have to bear the burden of like feature overload in a fairly complex product. So we are building a chat assistant. It can do like basic data mining and cleaning and analytics tasks. Like if you give it a list of countries, we can go out and like populate a new column that gives you the capital city of every country as an example, just by like typing in natural language, what are the capital cities of each of these countries? I think in the future, we'll want to integrate that with the internal knowledge of the company because with data, obviously the questions that you answer are more nuanced to each business. So that’s the way that we're thinking about it. Like our product isn't going to be an AI product. It's going to have an AI assistant.
Melissa Perri - 00:31:09: Okay, I like that. I feel like a lot of people got so thrown off with ChatGPT going out there. I've heard companies be like, oh, we're stopping everything to go build this, make it AI, just like slap an AI thing on it. And I've also observed so many companies just like straying from their core to focus all their attention on AI or put a feature out there with AI to like hit the buzzword so that they can like look exciting. Meanwhile, their core product sucks. Like it doesn't even work. And then they're getting distracted that way. How'd you guys sit down and actually come to an agreement and say like, “was there a distraction at all? Were you all just like super level-headed about this? Or was anybody, we have to do the AI thing”.
Bethany Lyons - 00:31:50: No, I think we were super level-headed about it. And we were kind of like, this is not our priority, but if we have time, we'll work on it. And then we had time, and we've got some pretty kick-ass developers who just went out there and made it happen. So we're pretty shrewd in our prioritization, which left some free time for them to experiment with AI. But it's not we're not out there selling an AI product at all. We don't even mention it unless people ask. It's so early and so adjacent to the core product.
Melissa Perri - 00:32:20: Cool. Very smart and rational. So for you, when you're thinking about next phases for KAWA and the startup on this growth trajectory, what are you excited for?
Bethany Lyons - 00:32:34: I'm actually really excited about, I'll do the CPO thing until we have a very clear ICP and like we've got funding, we've got customers, we've got runaway and it's just a matter of scaling. And then I really want to work on implementing growth strategies and using product as a distribution channel, because that's really then at the intersection of data and product. It's how do we use the data that we collect in the product to optimize our activation and our acquisition, activation, retention and monetization. So I'm following Leah Tarin and Elena Verna religiously. I love everything that they're putting out there. When we have more data, I want to implement a lot of what they are preaching and have real metrics to show for how we did this in the product and this was the impact it had on the business.
Melissa Perri - 00:33:28: Cool, really excited to watch that too. What's your advice for people who are considering going to an early- stage startup? I actually have a bunch of HBS students. Who really want to be like number one product manager at a really early stage company. Because one, a lot of times I think they just think they're going to get a massive amount of equity and be the next like Jeff Bezos.
Bethany Lyons - 00:33:50: Oh wow.
Melissa Perri - 00:33:52: That's definitely a poll that I've seen on a lot of people who think if they get in early stage, that's it. They'll just make a great exit and they'll be extremely rich. But a lot of them want to be number one. What would you tell them to consider, having seen all these different stages?
Bethany Lyons - 00:34:08: I would say if your reason for wanting to be number one is to get a ton of equity, you are in for a world of disappointment. So I'll tell you what I've done. I've set up my personal finances so that if KAWA totally crashes and burns, it has like no financial impact on me. So my husband is basically like this was a joint decision with him for me to join a startup and he's agreed to take care of the basic needs of us and so I'm not responsible for paying any bills. I have total freedom to just enjoy and see where this goes. So I wouldn't bet your personal financial future on an early- stage startup because you also don't behave in a rational way when your personal finances are so tied up in a company. So that's why I'm very happy my husband is he's the safety net as he calls himself. That's one thing. Two is if it's your first job, you have no experience and that absence of experience is going to show up just in volumes inside of a startup. There's you can't hide in a startup. Your weaknesses are going to be on display for everybody. And the success of the business is going to ride on you overcoming your weaknesses. Whereas in a larger company, you can clay to your strengths. You can hide your weaknesses a little bit more. You can hone them. There's more opportunity for learning in an early- stage startup. Nobody's going to train you. We have some junior people and I don't have time to be training them and doing career building. I’m trying to make sure you get paid. So that's what the senior people in the company are going to be thinking about is like, can I get you your next paycheck? Not like, how do I help you with your career progression? So I would say if you're fresh out of college, don't join an early- stage startup. Go get experience somewhere and set up your financial security so that you can then take the risk later on without having such a huge impact on your personal life.
Melissa Perri - 00:36:06: I think that's extremely wise words for people out there listening. Thank you for sharing and thank you for being so honest about your journey. If people want to go follow you, learn more about you, where can they find you?
Bethany Lyons - 00:36:17: Yeah, just connect with me on LinkedIn or you can send me an email at bethany@kawa.ai. Happy to chat with anyone.
Melissa Perri - 00:36:25: Great, thanks so much for joining us, Bethany. It's been great having you on the Product Thinking Podcast.
Bethany Lyons - 00:36:30: Thank you.
Melissa Perri - 00:36:32: For those of you listening, if you enjoyed this podcast, please hit subscribe to the Product Thinking Podcast so that you never miss an episode. Next Wednesday, we'll be back with another Dear Melissa where I answer all of your questions about product management. There is no question too big or small. So go to dearmelissa.com and let me know what you're thinking. We'll see you next time.