 What I did was model all of that data to try to predict revenue and discover that actually to predict revenue, we had to do a product with it that eventually rebuilt in six months and drove about $500 million in gross processing volume. Welcome to GraphCakes and Discussion. I'm Amy Hodler. Today, we're talking to Claudia Natasha, a product leader who searched for revenue revenue growth led her to start a company that uses graph tech to uncover customer sentiment. Thanks, Amy. I'm really excited to be here. So I actually started my career as a data scientist and I worked in some wonderful companies trying to figure out how to get the company to its next state. So that reminds me of an experience I had at a company that actually reached a point of plateau. Revenue wasn't growing, we couldn't figure out why, and we had to really help the company get to the next state, which is either a fundraising state or some sort of exit. And so at that point, I had already transitioned from my data scientist career to become a product leader, and I also managed user research team. and that allowed me to get access to quantitative data so the types of data that data scientists work with qualitative data the types of data that user researchers work with customer interviews etc and what i did was model all of that data to try to predict revenue and discover that actually to predict revenue, we had to do a product of it that eventually we built in six months and drove about $500 million in gross processing volume. So if you're in FinTech, you'd know that I'm talking about a payments product, that's the metric GPB. And because that was so successful, I ended up presenting my experience at different conferences. And multiple product leaders, research leaders, data scientists, designers would reach out to me saying, Claudia, what tool did you do or what tool did you use to bring this practice to your organization? And can you build it for us? And with that question, the idea of Riley really was born. I decided to build Riley to make data-driven decisions accessible to everyone to be the simple. and with that question the idea of riley really was born i i decided to build riley to to make data-driven decisions accessible to everyone to be the simplest way that that anyone insights leaders can combine disparate sources of data to find clarity and strategy i've been a product manager and a product marketing manager and it's always a struggle to get in the heads of your customer like what do they not just say that they want but what would they actually pay for or you know stick around for it sounds like this started out more as a process than a tool or at least this idea is that correct and could you explain a little bit about that yeah it originally it originally did start more as a model. I've been tinkering with this model actually since 2011 when I was a student at Berkeley. At that point, my mom's an entrepreneur and she was trying to also figure out what's wrong with her business. So on a flight back to Indonesia, I modeled something for her and proved her wrong, whether she likes to admit it or not. She probably will say I did not do her wrong right now. But I digress. So from that experience, I started building these models, getting better and better over time, as we all do in our careers. And eventually, I got it to a state where it's good enough, at that company that I brought up the example earlier, it actually made some remarkable impact to the company trajectory. And when I started presenting the idea at conferences, I took two or three consulting gigs after. So I was still working full time, but a couple of different companies wanted me to help build it for them. And so I built the model for them. And they'd reach out after saying, hey, we don't really have anyone to manage the model. It's awesome that you did it for us at first, but we don't really have anyone technical enough to take it a step further. And that's when I realized that it just can't be a model. It just can't be a process. It has to be an actual platform and I want to be the person building the platform and designing the future where this exists so so for listeners that might not uh intuit what you mean by model here can you and I realize this is more than a model but can you explain a little bit of the elements that might be in the model? And if there's an example of maybe a surprise that you uncovered using your models. Yeah. Yeah. So it's a combination of a few different things. So of course, the base of our predictive, the most basic version of the model from a few years ago is a regression model that predicts revenue right and as with any regression models we have different variables that we put in so an example of a variable that i'd put in on what predicts revenue is let's say i'm analyzing a bunch of customer service calls. I'm trying to distill how those calls went and the general themes within those calls. So I do a combination of two things, one thematic analysis using k-means clustering. And based on those clusters, I turn those clusters into variables that I then input into the regression model. Another level that actually would answer the question also, what surprising thing did I learn is sentiment analysis. So themes are the only thing that's important. From a call, we might discover that there's three top themes and one of the theme might be, let's say user, I'd like to improve the UI because for some reason i can't find the button that clicks to update my my profile or something so things like that we might find general themes like that but without knowing the sentiment behind those themes we don't know whether people were actually upset or happy and so another variable that we put into the model is sentiment as well. And then with sentiment, I realized that existing models that are available to use or existing models that we can build ourselves could never quite be good enough if we keep them general. Let's say someone says, oh, great. Yet another feature. feature sentiment analysis models interprets that as a positive sentiment but we know that that could also be interpreted as sarcastic right oh great yet another feature so i think the human input is still important to help discern whether that is in fact a positive or a negative sentiment and to bring the cultural context as well. I grew up in Indonesia and a sentiment like that would actually be perceived as more positive in Indonesia as opposed to negative. So how do customers use Riley? Yeah, so we are lucky to have a number of customers even at our closed beta state. And we're really excited now that we've launched publicly to open it up to more of you. So how customers use Riley is they're able to input any qualitative data. So whether it's in text form exports that you have from customer service calls or whether it's directly through through Zoom meetings that you might have with your customers. We analyze Zoom transcripts directly. Also, we have an integration with Zoom. And you're also able to input any survey data. So in the forms of CSV, so quant data, click a button on Riley, and we'd, within minutes, generate insights for you that are prioritized by customer impact. So that part's really important because after spending my career in product, up until today, every time I talk to PMs and researchers, the one pain point that no one has solved is how to prioritize insights. I'm sure Amy, you've had this experience also as a product marketing leader, where we end up with a large list of feature requests and we don't know how to prioritize it. And we end up in meetings debating which one's number one. Everyone has different opinions. Everyone has different inputs. So Riley combines all of your opinions, all of your inputs. We allow commenting discussions on the platform that will then all come back into the AI, inspired by that model that I created many years ago to produce a prioritized list of recommendations on insights that we can feel more confident about. We know that if we prioritize number one on the insights that Riley recommend, that that is what's going to have the largest customer impact. There's just the value of having some quantitative tools to help figure out, like, what do we, what does our roadmap really look like? You know, whether it's revenue decisions or maybe marketing decisions, like what theme do we focus on for the next year or, you know, six months, and what other things should be, you know, moved to later? Because as you said, everyone has their favorite feature, pet project, theme, whatever it might be. So since this is GraphGeeks, can you tell us a little bit about the platform? Because as we've spoken earlier, I know you've got a graph there in the back end. So I'd like to understand why you chose a graph and how that's being used. Yeah. So to keep it very general for now, and if anyone would like to know a little bit more, feel free to reach out to me. How we use Graph right now is a lot of our recommendations are powered by very deep knowledge on industry knowledge of what drives business goals, right? Because at the end of the day, if we're trying to build AI that prioritizes insights and help you determine which insights are the most important, the highest impact of customers, we want to make sure that our recommendations are based on deep knowledge. So we currently train our graph to reflect industry knowledge that we've gathered from subject matter experts and also from widely available literature on, let's say, what drives adoption, what drives retention. And all of the nodes in our graph represent essentially all of those different knowledge that we've gathered over time. And as more and more users use the platform as well, our AI will learn from your behavior. So back to the example that I brought up earlier of Munzey, Amy and I are working on the same team. Amy is the product marketing leader, and I'm a user researcher. Let's say we're debating on an insight and I discovered through a recent user research study that we have to improve the filtering functionality of our e-commerce site to drive more checkouts. Amy is debating with me and saying, Claudia, I recently went to a conference and I actually discovered that it's not just the UI that needs work, it's also our pricing. Our pricing is terrible. No one really finds value from the way we price. Usually conversations like that might be lost. But if Amy were to write it as a comment on Riley or as a Slack comment with the Riley integration, we capture Amy's comment, bring it back to our graph and present it as like, is this is part of the actual recommendation and as more and more people bring up ideas similar to amy as we discover let's say on product analytics we discover that it turns out the filtering functionality is not what's the problem and we discover more things that are similar to amy eventually the ai the AI will get better also and recommend AIME strategy ranked it as higher. So why couldn't you do that without a graph? What drove you to choose that kind of technology? Yeah, I find that graph technology is more flexible and also can learn and grow with the different types of data that we hope to ingest and the different the different transformations that we hope to do with our data over time and so i've the way i think about the types of technologies that we use and it's aligned with the future of riley and because data especially like if you if you think about data in the past 10 years we've seen it transform in so many different so many different types so many different variables we used to just work with one-dimensional data back in 2011 and now since then it has transformed into multi-level analysis so i think um i like to choose what we use depending on really where the future of the company is heading. When you were working with the graph, maybe I don't know if it's implementing and maybe I don't know if you can tell us whether you're using a graph database or some kind of a store and then instantiating a graph, if you can explain a little bit about there, are there things that you learned worked well, didn't work well for your use case? Yeah. So I would say if anyone here is starting a project or starting a startup and you are starting with a small amount of data for the near term, I wouldn't necessarily start with using a graph database. I find that graph database are more useful once you have a substantial amount of data. Now, what we define as substantial is a little bit subjective, right? But at the end of the day, if you're experimenting with a project and let's say you're still at the experimental stage, so you're just trying to figure out if something would work or not, and you have a training data or you have initial data from, let's say, only two or three customers or two or three testers contributing, it would be hard to really scale your graph in a way that would make sense. Because at the end of the day, if you're trying to create multiple nodes that connects to other nodes that predicts a particular outcome, it's really hard to do with limited data. So I'd recommend that at that point, use some sort of other system and then think about how you would eventually incorporate graph in your technology. This also brings up a wider philosophy that I have that I often talk to my co-founder Kevin about actually, is that sometimes it's not about making the tech work for us, but it's about making, only finding the tech if it does work for you. So I personally love graph technology, but if it didn't work for Riley, that's not something we would have chosen. We choose the tech that really we feel like is the most valuable to propel the company forward and aligns more with our company ethos and what we hope to build at Riley. I love the fact that you call graph tech cool because, you know, most of the people here listening would agree. Yeah. Yeah. Any other recommendations or resources for a startup kind of getting started in this area that maybe wants to look at some of the technology you talked about, even if it's outside of the graph space? Yeah. So I personally use two different ways to actually learn more about graph technology. The first is I reach out to people who are actually early adopters of graph technology or the early builders of it. The most fun story I've had is reaching out to some early employees of Neo4j and asking them how they've built it. And in exchange to ask them how they've built it, I also gave them feedback from using the product. And so I know after talking to a lot of early builders, I know that you all, myself included, can feel like a little bit nervous reaching out to people and ask them for advice. But the best gift that you could give them to get their attention really is product feedback. So I know that we're all using these types of technologies very frequently. If you ever have a feedback, don't hesitate to, don't just email it to the supportline or keep it to yourself. If there's someone you can find on LinkedIn to share that feedback and have more of a wider conversation. The second way is also reading papers that incorporate this technology and reaching out to the people who write the papers. So I've met some of my friends, some of the people with opinions that I respect very, very and that i would often talk to whenever i want to bounce off ideas early ideas with through this this whole process of just reaching out to people after reading their papers i love those two recommendations because they're both they're both about making human connections and reaching out to the people that are passionate about what they've created whether it's a service a solution a product what you know whatever it might be and i do i i would agree i find that actually you know surprisingly successful that people want to learn how other people are using what they're working on and they want that feedback which you're helping with in general and to summarize, but also just kind of that human to human, you know, interaction and other people who care enough to actually reach out. So I love that, those recommendations, because I think they, they kind of both fit into that, like making connections, which of course, graph people should all be excited about making connections. You originally said, you know, you mentioned the closed beta, but now I think it's more available. Can you tell us about that? Yeah, absolutely. We started the company, my founder, Kevin Ma, and I started Riley about eight months ago. And since then, we've been building in a closed beta state where when we decided to start Riley, we had a wait list of customers about 50 different companies signed up to use the product, mainly from the conferences I used to, I mentioned I spoke in. And as we're building the product, we started onboarding some of these users to help us become really design partners. Because this is an AI that I feel very strongly should be built with the community since as every good ai should reflect the needs of the community we we decided to build very closely with our design partners and so for the last eight months we we slowly onboarded our wayless customers and built alongside them launch all of our features one cool thing thing that we did actually was one of our design partners. He's from Indonesia and both Kevin and I flew to Indonesia to meet him and to really have a conversation about two months ago on what's next for Riley. Just really, really exciting to see people worldwide starting to incorporate Riley. And now we're finally ready to launch publicly. So we're really excited to make Riley available as of today on the 23rd. And what's really exciting is now anyone can just sign up to use Riley simply by going to ask Riley.io and creating an account. We have a free tier for a few months. a few months when you start using Riley, you're able to just run projects, start experimenting with it, and feeling the magic of being able to prioritize insights within minutes just with the click of a button. I'm sure everyone in this community or some people in this community would like to be founders one day as well. I'm going to share a little bit about my journey and Kevin's journey in the blog in the next few weeks. It sounds like you're very collaborative in the way you work. Is this also going to be either, are there going to be any open source elements or developer tooling that maybe a developer could hook in and integrate with? Yeah. Yeah. That's, that's, we will absolutely have all of that, just not right now. So the plan is to eventually have open APIs that developers can integrate with in the next few months. Awesome. So I have one final question that I like to ask people, thinking over 2025, is there something you would like to do if you knew it were impossible to fail? If there was something that just could be nothing but successful, what might that be? I think there are a lot of industries that are extremely underserved that would benefit from having a tool like Riley. And I don't know if I mentioned earlier, but I'm originally from Indonesia. And so every time I go back, I see all of these different companies, all of these different businesses. They might be small businesses, like restaurants, et cetera, that have so much data and they don't know how to do it. They don't know what to do with it. They don't know how to make sense it and i my my dream really is to have every single organization that uses data to use riley and i know that that's such a strong feed we're starting with tech right now but my hope really is to get us to a state where every single organization uses riley because i think what we're building is extremely extremely important and can provide so much clarity and even the playing field for even small businesses. Awesome. I love that. Many of us often think about the enterprise, but there's just so much data out there in the mid, in the small market. It's not utilized. And just the power of that is kind of amazing. So, well, I enjoyed it thoroughly. It's a great way to kick off the year. Really appreciate your time. We would love to talk to you and your co-founder back at some point. So again, thank you very much. Really appreciate it and happy new year. Thank you. Happy new year to everyone.
GraphGeeks In Discussion S2 Ep1: Capturing Elusive Customer Knowledge
Today's conversation with Claudia Natasia, CEO of Riley, takes us into the fascinating intersection of graph technology and customer behavior. As a data scientist turned product leader, Claudia discovered that the key to unlocking revenue growth was hidden in the complex web of customer data. That insight led her to found a company that's revolutionizing how businesses understand their customers using the power of graph technology. Join us as we explore her journey from data-driven problem solver to innovative tech founder, and learn how companies are uncovering elusive customer insights.

More information on Riley can be found at https://www.askriley.io/
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