Episode 48: Transforming the Way Teams Connect to Customer Insights – with Dwayne King

Transcript

Cathi Hey everybody. It's Cathi Bosco from Rethink.fm, the forward-thinking podcast about experience design. Join me and special guests to discuss effective design, technology, and the intersection of helping people and entrepreneurship. From user experience research to product roadmaps, we've got you covered. We've all got questions, so let's get started.

Cathi Welcome Dwayne. I'm so glad to have this time to speak with you and share with everyone else the amazing work that you and your team are doing. How are you tell us a little bit about yourself?

Dwayne Thanks. Yeah, I'm good. Thanks for having me. Yeah. So I'm Dwayne King, founder and CEO of Rutabaga. It is a platform to help product teams analyze their data faster and essentially get it into a centralized repository so you can have all your customer data in one spot. I come from a long history of a research background, so it's, I think, unlike some other tools, I actually get how the work gets done and, you know, and being a researcher, I've talked to a lot of people, shown it to a lot of people, you know, been very much a researcher through the process.

Cathi So you sort of struggled along with different obstacles or roadblocks that you've had to work around, and now you've compiled that Intel into this amazing solution. There's a lot of tools on the market. This one is one of the more visual. I love, the Knowledge Graph. Can you talk a little bit about what went into the visual nature of this tool?

Dwayne Yeah, absolutely. So one of the very first things that I said to the team or bring them on, is that I wanted a consumer grade experience and send a consumer grade brand for an enterprise product. So I didn't want I didn't want a dull name. I didn't want dull branding, and I didn't want an interface that felt like a bunch of drop downs and clunky refresh and stuff I'm like, I want it to feel like something that you would use in the consumer space and be happy with, but it's actually an enterprise grade tool. And currently, Rutabaga is designed for qualitative research videos, analysis and synthesizing.

Cathi Can you talk about how you've structured setting that up a little bit too. I know the AI gets rolled in based on the objectives of the research. So there's a AI happening behind the scenes, which is where it should be.

Dwayne Yeah, that was one of our I don't know if it was a tenant or just a belief that we had. So I really wanted for AI to be an assistant and a thought partner, not to do the work, not to take away work. And then part of that means that whenever AI does something, we make sure it explains what it did and why it did it, so that you as a human still have the agency that people say, Oh no, you got it wrong. You didn't quite understand. Or no, that looks about right. I'm going to keep it. And so that was something that we have been really intentional about, trying to make sure that, you know, I always wanted to human centered designers. We always wanted the human in the center of everything. And so really making sure that, like it mentioned that the that the human can keep that agency to be able to redo or undo anything that the AI did and understand why it made the decisions it did so that you you can, you can correct it when wrong. I think one of the things people get wrong, and this goes like even for like prompt engineering, is, I think the best way to think about AI is, like, a junior, like, in our case, a junior researcher, you know, so to a couple of things with that, like, if you just say, hey, analyze this, it's not going to do a very good job. Like, you need to, like, really give it style instructions on what, what you're trying to look for. And even with that, you're still going to want to check its work. Like, we've had customers that are not checking its work on really low risk things, like one customer was talking to customers about customized doorbells, and they're like, just it wasn't that, if they got it wrong, it wasn't that big of a deal, but they wanted to kind of get a sense for it. But obviously is that risk elevates the more you want that human really, in the center of everything.

Cathi Yeah, and so in my experimentation with tools, I feel like it is really intentional here, where I'm getting the Insight synthesized based on my objectives of the research, and I am invited, in an interactive way, to literally move things around and agree and disagree with the way things have been, you know, organized together a fiend. So my feedback on that is I really enjoyed that it really respects that I'm going to be editing and curating and bringing the context right? Because AI doesn't have the context like someone embedded in the research or product would.

Dwayne Yeah, and I, even as AI models get smarter, I still don't think they'll necessarily understand. You know, there's so much about an organization that you can't put into documents. We used to when I was at Salesforce, we used to have, I was on like, a digital transformation team, and we'd have customers come up in a workshop and draw a picture of a tree. And it was always to make the point, like, you'd be like, what's missing, and everybody, nobody ever drew the roots. So we'd be able to talk about, like, in a transformation context, that the top of the tree is all the your SOPs, your KPIs, all those things you can see and measure. But then there's that whole cultural piece underneath of like, mores, social mores that are within the organization, the politics, you know, there's things that, no matter how smart the AI elements get like, I think they'll have a really hard time understanding that invisible context that humans kind of just naturally navigate.

Cathi Yeah, yeah. I think that's really important, and it doesn't as people learn and get more experienced with the value of tools like this, or the lack of value it may not be the right tool for every situation, understanding that it doesn't have context. It can't think. I know a lot of researchers are really struggle when quotes don't come through accurately, or when quotes do come through. And so we compare tools in the back rooms. How did your quotes come out, or what did the quotes come through? Like, can you talk about how you think about and surface Customer Quotes?

Dwayne Yeah, and they're fair to be concerned, because they will make quotes. LLMs do that. So what we've done is, as we go through and we identify themes and start to identify text passages that fit that, we then go back once the LLM says, Hey, here's this quote from this person, then we have a fuzzy search that goes back to make sure it can find it, but it'll match it up, and then it'll correct it with the right text. So we make sure that every time it goes back and double checks and changes it back to the text it originally was, so that it doesn't do the modification way, the way llms are prone to do. And then you can always click on it, and it'll open up in what we call the HUD, where you can see it in its context, in the in the transcript, so you can triple check that way.

Cathi Oh, thank you for explaining that I didn't understand technically, what was happening behind the scenes and how you were able to get such accurate quotes.

Dwayne Yeah, it's, it's not a Herculean list, but it's pretty hard. It was hard because we first were like, Hey, this looks good. We go back. Look, I can't find this quote, like they're making up quotes, and that's unacceptable. So yeah. So then we had to, like, start thinking about, okay, how can we keep it from doing that? And that's the solution we came up with, is, and if we can't find it, then we, you know, quote, unquote, throw it away. It's still in the transcript, which is why we still have the kind of traditional coding tools. You can go through a transcript and grab different text passages. Because AI, what one, if we can't find it, that might have been a good quote, but we can't find that. They change it too much, so we can't find it. So we want to make sure that we get that, but can we still go find that quote? And number two, as we were mentioning, AI is going to miss things. It's going to it's really good at when you put in your objectives, and it sees an answer to one of the questions, it's really good at finding those. But as you know, in qualitative research, a lot of times you didn't, you know, like, Oh, we didn't know. We should have been asking this. That keeps coming up. And, you know, I can decide some of that nuance, like, it's lost on AI as well. So, right, right.

Cathi So, right, right. I love how you describe bridging the gap between scattered insights and then all the way down to meaningful outcomes. Are you talking about meaningful outcomes in later in the product delivery? Those outcomes and measuring it at that end. Are you talking about outcomes? Because we get these recommendations we can give to product managers.

Dwayne Yeah, I want our ultimate goal is. So one of the things that we find is that it seems like a lot of people in the knowledge management space, like once you can get the data into a repository and search it, then it's kind of like, okay, our job's done, and our thinking is, you've got all this rich data, why aren't you helping with ideation and figure out what experiments you should run and helping drive the actual outcomes and then for measurement, like we're really thinking, I feel like product teams and Researchers in particular, one of the reasons we get beat up so bad during downturns is because, you know, salesperson be like, Why I sold $5 million last quarter. What did you guys do? You know, and it's really hard to put any kind of quantification on what product has done. And so I want to start to figure out how we can help measure that, so that product teams have, like, a clear, clear, answer I want to say a clear way to illustrate to leadership in terms that are important to them, that they're providing value so that we don't get beat up as bad.

Cathi Yeah, that quantifiable value could be like we didn't build the thing, and so we saved this much in engineering time and this much in Lost marketing efforts and blah, blah, blah, blah, blah...

Dwayne Yeah, and that's what's hard too about. I mean, this is kind of off the subject of exactly what we're talking what is what we're talking about now, but not designing to Rutabaga, per se. But one of the things I noticed, like, I would go into organizations as a practitioner and a leader, and because non researchers had put together the measures for researchers, like, when I got to one organization, like, yeah, the way we do it is we go through and count how many of the insights led to features within the platform. Like, okay, but how many of the insights kept us from building features that were unneeded, like that? You know, there's like, what you're saying like, there's so much more, so many more ways to measure the value of understanding your customer, other than just, did you hear something that ended up getting pushed into a feature?

Cathi Right, right? And with all of the video capabilities and tools and being able to share with an engineering team what customers are actually saying. I know from research, and I've said this a million times, and I believe I learned it from Jared school's research, that when organizations go through rapid growth, they also lose contact with customers and the research was based on engineers who it diminishes the quality of their work a little bit, but really it diminishes the meaningful nature of their work. They're doing this work because they think they can help people through this work. So I love that tools like this give us the chance to share those video clips and things out to the rest of our teams. So then just hearing Cathi talk about it, they're hearing it directly from the customer, and they're seeing customers actively using their software and struggle with it.

Dwayne Yeah, so yeah, and that's to that end, we're also the belief that like having some sort of knowledge base and expecting people always to come to you to find information is probably a losing proposition. You know people. People like to stay in their workflows. So one of the things that we say is we want to be where and when decisions are being made, so we're starting to build the relationships now for integrations into so that we can integrate into the workflow that's already there for the people you know. So for instance, an engineer might be in JIRA that they see a request come through, and they're like, what's driving this? That that the researcher, the PM, could have attached Rutabaga instance to it to be like, Oh, here's the insight. Oh, and I can click in and see what some of the clips that they're they shared with us, and so they can really start to get more context around why it's being asked. Because I think a lot of times when, when the intent is right, but something goes wrong. It's because maybe the requirement is vague, so the designers found it a little wrong, and then the requirements vague still. So then when the engineer got the the designs, they engineered it a little bit wrong. You know, it's like, it's but I think you could get more context and really, like, grok, like, Oh, I get why we're building this. Now I understand it's not just like build this and not having any context as to why, right? What a customer might describe is a little bit different than what an internal team might problem to be, right, right?

Cathi Can you talk a little bit about Knowledge Graphs? Like, I've spent the summer really studying a lot of AI things and learning about Knowledge Graphs, and I'd love to hear you talk about that, because it's an important part of the experience at Rutabaga. Yeah, yeah. There's a few different angles to that. One is we used to do when I was a consultant, we would do basically Knowledge Graphs, but like physical, like big posters of the findings. I didn't know what Knowledge Graphs were at the time, so I call them ecosystem findings, or findings ecosystem, because I thought like eco, and there's a few things about it. One, it really helps people, like, for visual people, to be able to be able to see it laid out, instead of just being page by page by page by page. But be able to see the connections and why the connections are there, is really good. And it also made a really good socialization tool, because if I was going to meet with a QA manager, like maybe down in this corner was like things that had to relate with QA, so I could start there and kind of work my way back in towards the center, versus if I was meeting with someone on growth, that could go over to this other side, where it's talking more about, like the difficulty in buying the product, until you work the middle, you know exactly they might start with the middle and work your way out, getting more vague towards the outside. So we found that to be really effective. And then, as we started to do the research for Rutabaga, when we would show people how you can query the database, especially well, I'm going to back up one second here, real quick. One one insight that we had... We had a couple design partners that were using a tool that it would be considered competitive to us, and they weren't getting the adoption they wanted. And so it said, Hey, do you want some free consulting? I'll go, we'll go do a research project with them. Be great for you guys to know, why? Why? Like, what drives adoption with your tools? Would be good for us to know, like, what drives adoption with tools like that? The thing we heard over and over again was, well, my stakeholders aren't there, and they don't want to come into this tool, and so it's just another place for stuff to be. So that's why I don't use it. I go to where my stakeholders are, and that was like a light bulb moment for us, because we had been just talking to researchers about repositories, and like, oh, the repository isn't as much for the researcher as it is for stakeholders, because researchers are trying to get their stakeholders engaged in the work, and if we can't get the stakeholders there, so like, we got to go start talking to product managers and designers. And because of that, when we started talking to, like, early on, we're talking to researchers, and we would talk about, like, being able to query it and showing, like, different queries you could do. And like, Yeah, that's great. That's great. When we start talking to product managers, they're like, What I don't even know what data is in there. I don't know what to ask. Like, what? How do I know what kind of questions I should ask? And so we start thinking like, oh, that's where that findings ecosystem. Now, Knowledge Graph, we learned that's what it's called when we started looking at the Knowledge Graph, because it gives you an opportunity to see not only all the data that's there, but how it's connected and how things can move through it. And so it and so it just gives them a lot more perspective on what's there, so that they they know the types of questions they can ask and what that looks like.

Cathi And they can click through the visual information hierarchy to the areas that pertain to what they're interested in. And oh, and this also impacts support blah, blah, blah, and you can see connections that you wouldn't necessarily come up with.

Dwayne Yeah, different scenario, yeah, it's funny that you say that, because that was what originally inspired me to do, what wouldn't call them finding ecosystem, because I would tell because we had one customer in particular that was really bad about well intentioned, but their people would see like, Oh, our phone trees broken. I'm going to charge phone trees. I'm going to go fix it and make it better. But then, you know, there are people again, support it. They're like, Wait, if you change that, then this is so one of the things I tell them is, like, imagine this, findings, ecosystem, this, this Knowledge Graph, is like a doily laying on the table. And if you go to pick up one, all this stuff attached to is going to pull it with it. You're not just going to pull up. What can pull up a piece of it very and so like understanding that there's those connections, I think is really important. It is, it is, it's, it's, it's incredible, because we've gotten to extraordinary efforts to try to build it manually and tools like narrow and stuff, but just to have it exist for anyone on the in the org to search and understand.

Cathi I mean, product strategy is not a role, it's a shared understanding, and that's amazing. Yeah, have a couple questions here in my script that I'm gonna go to, yeah, something else you'd like to add to that. Let's take

Dwayne Let's take it where you want to go.

Cathie Are you seeing any signals to there's a lot of speculation, or we're seeing a lot of how AI is changing the way we design and rely on and interact with technology. Are you seeing any signals that point to how AI might change our our research patterns in the future? You're building on that. So maybe that's your work. I don't know.

Dwayne Yeah, yeah. I mean, I know, you know, depends on if we want to go utopian or dystopian or somewhere in between. But my my hope is like, I'm hoping that with Rutabaga, it seems to have that I think we'll be able to execute research faster and probably do smaller research projects instead of because you can start to see all across the organization and see what information you already have. Well, so let me back up again. Sorry. I'm all over the place today. One of the first inspirations for Rutabaga was I used to have a consultancy, and we were a smaller consultancy that worked in the enterprise, which meant it was really hard to get in, because procurement teams hate small consultancies. They're like, just give it all to McKinsey or Deloitte or somebody, and call it good. And so when we get in, because it was hard to get in, you'd land and expand and get multiple clients. And the number of times that I would have a customer ask me for work that I had already done for that organization, but just a different group, was astonishing, like so astonishing, and the number of times that people would ask for work to be done, and I'd be like, Oh, are you guys? Are you talking to these guys? Because it's that you're asking for different stuff, but this is really you guys are kind of tangential to each other. And like, No, I had no idea that that group even existed enemy. And I'm like, You should talk to them because and look at the work that it did for them, because it's different. But I think it will ask better questions when you come to me, then you will now. And so that's my hope with Rutabaga, is it starts to create those connections where you can start to say, like, oh, this, this works going on. I have more context. I have more understanding. And instead of having to start from ground zero and do a big, like quarter long research project, maybe you could do something in a couple weeks that would get you enough answers to move forward on the few questions you have, and hoping that we can make research more kind of quicker and iterative than it is now, just because of the kind of some of the labor intensive tasks that it takes to get research out the door.

Cathi Repeating research not good. But also, I think that opens the door to doing more research and having more impact on making an organization proactive instead of reactive. You know, so measuring those metrics along the way can be helpful. Like I was just in the last podcast episode with Nikki Anderson, we were saying, like research impacted two features on the roadmap in q2 and this quarter, it's impacting five. And we can measure the outcomes. Nothing gets shipped that we aren't measuring the outcomes with or benchmarking that kind of work as well. So it's rigor that's really, really needed. I found that there's a real gap in the organizations I've worked for as well, between different teams who are trying to solve similar problems, like you said. And so I blatant self promotion. I solved the problem of global taxonomy in systems so that they map all the same and being able to bring that global taxonomy, or that product hierarchy, because it's ultimately the product hierarchy of features and sub-features, into the research structure and into the Knowledge Graph, people aren't having to relearn everything. Like all the support insights stack right up with all of the customer facing teams insights and yeah, it's, I think we're kind of at an inflection point with research in that way, high demand for teams to solve that problem, so the Knowledge Graph helps a great deal.

Dwayne Yeah, my hope is, you know, I was talking to a friend of mine that has a consultancy still, and we're talking about Rutabaga, and she's like, that'll be so good for organizations, like, horrible for me, but great for organizations something like, my hope is, it won't be horrible for you because, because we've designed for the stakeholders, my hope is that we can start to help the whole organization, start to see the benefits of understanding the customer and want more of it. You know, like to create that hunger. And so whereas, like, I think she was thinking so much of our work probably is repetitive work, like some other agency did something over here, and we're doing something here, and if they found it, then they wouldn't need it. But I find that most of the time, when you can find what number one, it's very seldom. Two projects are exactly the same, you know, like it exactly, but but close enough that they should be aware of it. And like I said, we said before, like, I think they just ask smarter questions then and said, but they still have questions. They still have things that need to get answered. Very seldom. I seem like someone asked me for research and be like, well, read these three reports. First, they all touch on stuff that you've done. It changes the questions they ask, but it doesn't change. Doesn't change that they're asking questions. So I don't think, my hope is it's gonna that we can actually help leadership see the value in understanding the customer across the organization?

Cathi Yes, absolutely. And I'm glad that people read the reports you give them, half the time, I wonder. But the other thing is the strategic part about like being able to identify latent needs as opposed to just active needs. I mean that kind of strategic working in context and thinking is still really critical to have people for, and then you need people closing the loop with customers too. So I think your consultant is is in a good position to be able to make more impact, you know, faster.

Dwayne Yeah, yeah, I think so too, yeah. But then on the flip side, there's like, you know, there's what I hope, what I think can happen, what I'm trying to drive for. But then, like, on the dystopian side, I was on a panel discussion the other day, and they asked What scares you the most about AI and research and, you know, right now, there are people creating synthetic users and AI interviewers, and I, I can see the future where the company ends up buying both and has fake researchers interviewing big users. And it's kind of terrifying. It is, it's it really, it makes my eyes roll back in my head.

Cathi It's just a very dystopian and I was just talking with a former colleague of mine earlier about that, and he was like, Well, I can see a synthetic person at a company that helps onboard new staff. I'm like, Yeah, okay, that's a use case I could kind of understand, although I don't know that I would appreciate that. It's kind of nice to get the inside scoop, right? -Oh, my God, don't drink the kombucha. It's not safe in the cafeteria - kind of information. But yeah, in my experience with having artificial intelligence conduct interviews, even setting that up feels like you're creating a survey it doesn't feel qualitative. And so there's like a little blend there of like, is it a survey or is it an interview? I can't ask questions. I can't observe anybody use anything. It just feels more survey asked from my brain, and I prefer qualitative Intel aside from basic stuff.

Dwayne Yeah, so well, and it's interesting too, like I even saw recently, somehow I'll leave them nameless, because I'm going to insult them. They released a synthetic interviewer, and in their demo, it was asking questions like, What Would You Rather this or that? You know, just like, to your point, like, a great question on the survey, but not in a qualitative interview. Yeah. That was another, like, marketing materials demo and like, where you know, you think the right pulling the best of the best for that. So it's Yeah, lot of ethical things coming up around all that. It's yeah, not sure I'd want the CEO having a synthetic user in his pocket he could be chatting with. I'm not sure he's going to come back or she's going to come back with the best customer. Human centric, yeah, and it's also interesting. Perceptions change, obviously, and they change fairly rapidly, but I know in our research, like talking to ops teams. They don't even want to use Calendly, because they feel like, especially when we're talking to B to B SAS, because those users are so hard to get that they're like, they don't want to take the risk of, like, saying, Okay, you take care of this now, and losing them. So you know, like, a lot of these users are so hard to get, so precious, that you're like, do you really want to.

Cathi Yeah, like putting the burden on the end user again is like 2005 all over again, I don't know. Okay, let's see. We got another good question for you. Yeah. Well, you talked a little bit about the devaluing of research and AI. Do you have guidelines for when you believe it's appropriate to leverage AI and when you recommend not using it?

Dwayne Yeah, I mean, my perspective is very biased, but it's also I built the building the company on my bias, and that is like we've seen some more legacy tools have a hard time using AI meaningfully. And then we see a whole bunch of tools coming out that we affectionately call magic AI machines, where they're like, just throw all your reports in here and answers pop out. And I just think, unless you're keeping that human in the center of things, with some level of curation that can be helped with AI, but I think that it needs that transparency. Like, those are like, kind of black boxes where you're throwing stuff in and you don't, I mean, sure answers popped out. But what like, are they the right answers? Like, I think just being able to see more holistically your data and know that, you know, one of our kind of value props is that, you know, you have that workbench that you saw recently that then publishes up into the repository, where you get the Knowledge Graph. And you know, we've talked about how researchers are in that analyzed tool, and that stakeholders don't see the data unless you invite them in. And some people have pushed back. Like, well, that's got withholding information. I'm like, No, I think it's more like, we want the promise that everything's been curated and signed off on, so that a stakeholder doesn't have to be like, well, how important is this? Like, it was important enough that somebody saw it, curated it, and published it, yeah. So it's more around what? How can we make sure that there's a space that a human signed off on? It's like, this is something that is important to our organization. I love it. I love it.

Cathi And before you kick off a research study, you should be talking to your stakeholders about what you're trying to learn and understand and what your objectives are anyway, hopefully so they're gonna have some awareness about it. I do it every time as a first first take, because I want to get bets. I want people place and bets on what they think we're gonna get, so that they can't say, oh, we knew that already. We don't need that research, you know?

Dwayne So it's, it's important to include them early.

Cathi They're gonna have you ask questions you didn't think up to, like, can't think of everything. I'm doing the best I can to get rigor to things. But they know, oh, I also want to know blah, blah, blah. I'm like, Oh, that's a great insight I wouldn't have known to ask about. And I guess they are. It's good for that too.

Dwayne But yeah, when, when I had my consultancy, we started in our stakeholder kickoff workshop, we started doing an exercise of knowns and unknowns. Um, specifically, because a lot of times with qualitative research, especially generative research, you come back with things, and once you say them, they sound obvious, but they didn't know. And so then you say, like, Well, yeah, like, but no one can, well, you said you didn't know two weeks ago, so it wasn't as obvious as you're making it not to be.

Cathi That's right. I love that knowns and unknowns. So you never move into the knowns until you really understand the problem. And if you really understand the problem or problems, why? Then the solutions really are more clear. You understand exactly the problem you're trying to solve, and the solution is not so hard to labor on. It usually reveals itself much better. And if you can't do that, then you still don't understand the problem. It's sort of like a curve like that. I love that.

Dwayne Yep.

Cathi Okay. More questions. More questions. Some of the AI's capabilities that might be a surprise to people today, or what capabilities do you see coming very soon that will change a products team's relationship with design and research?

Dwayne Yeah, yeah, I think I have this again, something I'm betting on. So I hope I'm right. But I think between kind of this LLM wave that we had and now this agentic wave that we're having, more and more data is getting cranked out. And like, if we can speed up research, it's gonna be more data, more data, more data. And that's where I really think, like, the Graph database is going to be kind of that next wave to be able to organize and see relationships. Because, I mean, you think about like a chat bot right now, like chat GPT or something, you know, it's all, it's all like a chat bot with your data, kind of like you're having to help you, like, think through things, but it's still sitting there in that chat and if you had a wave and to start to move it in and organize it and keep track of it, same with like, as you do your research, like to really be able to keep track of it at scale. You know, it's interesting. We've had the idea for Rutabaga for over a decade, probably, but the way we're doing it now, we wouldn't have been able to do without Graph and AI, because it's easy enough for a researcher to we basically, we have them create a mini Knowledge Graph of your, probably knowledge Graph of your project that publishes up into your body of work. But second one little bit harder, like 30th one and from a different organization, you know, like you need to start using Graph theory and Graph operations and AI to start to help identify where there's just collisions or or where there's conflicting information. So you can start to, like, navigate that we wouldn't have been able to do that if we had started when we first started talking about building Rutabaga.

Cathi Oh that's amazing. Yeah. Could get unwieldy quicker than you think, right?

Dwayne Yeah. Oh yeah, yeah.

Cathi Wow. So what are the obstacles or roadblocks you're trying to overcome today?

Dwayne Yeah, yeah. I think our biggest, like technical one, is Graph is cool and really powerful, but the tooling for it is still really immature and poor, and so it's really hard to build on. So in some ways it's nice because it's a bit of a moat, because, like, you know, like, if it was a old map, it'd be like, you know, dragons be in these waters, or whatever, you know, like, don't, don't go over here. Yeah. So it's been a big struggle for us there. And then it's, you know, it's a really tough fundraising environment as well. So that's been a challenge, too. So those are kind of our two big challenges right now.

Cathi I think it's just a terrific opportunity for someone who wants to really innovate and do important work. On Graph, I can see how that the opportunity is just a huge opportunity. I don't know how else to put it, like call to action. Let's grow this up a little bit. It's powerful. We need hierarchy, visual hierarchy, to succeed. Wow, yeah, yeah.

Dwayne And you know, Graph is really good at understanding relationships and time. So, you know, it makes it a lot easier for like, maybe if you had a release a month ago. You could ask Graph questions like, What is, what are the what kind of feedback we're getting this month versus the months prior? So you can compare like, from that last release, which would be really difficult in a more traditional repository to be able to design that.

Cathi Oh, I get really excited about that. That's like, really great. I like where that's headed anyway. Oh, is so how can people get involved and they want to learn more. What should they do? Who should they talk to?

Dwayne Yeah, yeah. We have on our site, Rutabaga.app is you can check out what we're doing there. You can also book a demo. We're trying to figure out how to soften the language on that, because we're, we don't really have an outbound sales motion yet, and so, like, booking a demo is really to get feedback and, you know, maybe build some advocates. And I will say, like, I'll reach out to a lot of people and say, you know, can you, I'd love for you, I think that's actually maybe how we met. Do you want to take a look at what we're doing, and I'm really intentional about I feel like one of three things can come out of that. The worst of which is, I get feedback, which, as a startup founder, like, there's hardly anything more valuable. Number two, what happens more often is I also get an advocate, like, they're like, Oh, this is really cool, I should tell you, but my boss about this, or have you come on my podcast or and then every once in a while, somebody that is a decision maker and could use what we're doing, and they're like, Oh, how do I get in on this? But I'm very intentional about so I'm just one way of saying it, click, say, show me a demo. I'm very intentional at this stage of not turning it into a sales call unless the participant does, like, to the point where, recently I had someone, and when they she gave me a lot of feedback. And at the end, and she's a director of research at a large organization, the energy like, Well, is there anything else you want to get out of this discussion? Like, no, I just wanted to like, No, I just wanted to get your feedback. And it's been really helpful. She's like, well, here's what our budgeting cycle looks like. So like, so don't be afraid. Don't be afraid to click that. And then I'm also at LinkedIn slash in slash, Dwayne, D, W, A, y, N, E, and feel free to reach out to me there. Make sure, if you invite me to connect, tell me why, because I get a lot of random stuff. But if you tell me, like you heard, you heard about Rutabaga on a podcast, and you're interested in learning more or talking to me, or asking questions, anything like that, then, then I'll accept.

Cathi I worry that Rutabaga is going to be hard to discover on the internet because it's hard to spell.

Dwayne Yeah, but let's just spell it. So it's what you know, it's fun to say, but then, you know, it's R u T, A B, A G, thing. It's kind of fun to like, get that kind of you have to sing it a little. So Rutabaga, R u T, A B, A G, a dot, a p, p, right?

Cathi Yeah, and the team there is just easy to talk to. No question is a stupid question. If you're experimenting with tools and you want to learn more about anything they will. Your team is amazing. They will answer every question, and nobody feels stupid but but the power of what you're building, and the uniqueness of the visual integration with the canvas and with the Knowledge Graph is different than anything I've seen. I've been looking at a lot of tools this summer. So I really wish you all the success in the world. I really hope we can get Graph doing what you're talking about with time. That is, that's gold. It's a gold for a research team.

Dwayne Yeah, thanks. I appreciate the vote of confidence there. In terms of the team too, I mentioned, you can reach out to me and want to talk about Rutabaga, but because I'm in this space, I'm studying and thinking a lot about AI and research, and what research, how we can be more effective. So, like, doesn't have, if someone wants to reach out to me, we don't have to talk about Rutabaga. You could. I mean, like, you can even we can talk about competitors, tools, if you want. One of the things we have a zero churn idea, this idea around zero churn, which we know that we're going to lose customers. Like, it's eventually, like, going to happen. But one of the things, you know, I said it kind of naively, thinking, I don't want to be the organizations like, well, we're expecting 5% churn, and we came in at four. So good for us, no problems. Like, if we lost the customer that we wanted, I want to interrogate that drill. What do we do wrong? How can we fix that? But then you start, like, unwrapping that a little bit. And like, we're going to think differently about sales, because if it's, you know, kind of a traditional pump as many licenses and can out of them, and then they're going to trip half of them the next quarter or next year. That's not really fitting to the ethos that we're trying to do. So because of that, you know, we're really quick. So like, if somebody's describing something like, Oh, that's not us. Like you don't I hear what you want. You know what you want. It's these guys over here. Because that's not quite what we do. And so we're because, so to your point, we're pretty easy to talk to. Like, we're not going to try to push you into this, this late model for the right research, for the right team, for the right it's got to all just be just right. Yeah, and I guess my my last question for you is,

Cathi Where do you see the industry in a couple years from now?

Dwayne Oh, specifically, research and research combined with AI or AI?

Cathi Whatever you want to guess about...

Dwayne yeah, I am optimistic that, you know, the Judd had that article about the Reckoning and losing so many researchers. I think it's part of a pendulum swing, and I think some of the to this point were to blame in that, as much as leadership, like we've got our work that we need to do, but I think it's been a wake up call for a lot of researchers about not I think, I think there was several researchers that would think their job was to come and say, Hey, here's, here's what we found, here's what's out in the world. Good luck. I was like, No, you gotta, like, gotta help them get to that next step and figure out how they can take action on it and move with it. And I think I for better, for worse. I wish I hadn't had to be through all these layoffs. But I think, like, it was a good wake up call. And I think we start to see the pendulum swing back a little bit where they're like, Oh, the people we have, we're getting value out of we need more of this, you know? I mean, I think that's evidence in this whole idea around the democratization of research. I mean, it's they just, they don't have enough people for the amount of research they want to do, and so I think that as they start to see the value created in that, I think they'll start to the pin alone will start to swing back.

Cathi And these tools definitely help, because things are not gated off. Things are accessible to executives, sales teams, Customer Success agents, all of that they can be closing the loop with customers, kind of like having a public facing roadmap, like, why wouldn't you have that you can grow trust and close the loop with customers. Same thing with internally, having a repo that's sort of open for people to have that knowledge. I think also, researchers are getting better at measuring the outcomes of things, and I think that will help organizations and teams be more successful in the future. I have an optimism bias, and so may be longer than I think.

Dwayne And to a point that you were talking about, you know, where it's not gated and you can throughout the organization, you can start to see this. I just wrote about this recently, but I think at some point, I think embedding insights under product is a mistake, because every or every group in the organization should have a good understanding of the customer, have access to people that can help find answers to the questions that you have, like it just it's, I think it's minimizing and siloing the discipline too much, and it really should be for organizational insights about the customer, not just product insights.

Cathi What a great recommendation to end on. I totally agree. And I think those teams really appreciate that visibility. You know that making that visible to them too. So, yeah, thank you, Dwayne, yeah, thank you on the podcast today!

Dwayne yeah, I appreciate it.

Cathi And for moving all this work forward, keep going. I'm in the background trying to help amplify the amazing work your team's doing.

Dwayne So I appreciate it, yeah, I appreciate it.

Cathi Thank you. Okay, bye.

Dwayne Bye.