Transcript | From VC to data product builder | Christina Guo (Co- Founder @ Cue AI)
The transcript from my podcast with Christina Guo
Christina Guo 0:00
So now you're on a podcast saying if I refuse to die votes, if you see a shutdown post, like two years from now you can come back and shame me for it. Or I know I said that on this podcast, do you like it's weird even thinking about the fact there's gonna be a lot of people who are AI natives who have never like really lived conscious adult life or pre adult, I guess, AI is only going to be best fit for automating manual work, instead of creative work. It's going to take all of everybody's jobs and the other four, like, No, it'll just make you better, I guess what's the only real thing that's shifted in the last like, year or so is the rise of LLM? So when I think about, like, what industries that will impact like any like, really, industry with a lot of unstructured data is like maybe legal or healthcare. But I don't know how much of those are online also. So who knows?
Max Matson 0:56
Hey there, everybody. Welcome back to future product. today. My guest is Christina gwo, co founder at Q. It's the AI startup that empowers teams to do anything with their data. Interesting afternoon, Christina, thank you so much for joining me, would you mind telling everybody a little bit more about yourself?
Christina Guo 1:11
Yeah, really excited to join today. Thanks for having me on. A little bit about myself, like Mac side, co founder of q. So really excited to be working on sort of solving some of the bigger data challenges that a lot of startups we've been talking to you are facing. For that I was an early stage investor at on deck grocer for a year and a half training when they were at seed stage and doing mainly growth and operations there. Went to Berkeley where I was running a startup accelerator. And so we'd sort of think it was like 1520 hours a week working with different student led startups every week, and so super cool. Just getting exposed to a lot of industries and seeing us like people took on some really big challenges, and a lot of them are still working on it full time. So definitely, what sort of got me into tech in the first place.
Max Matson 1:58
All right on. That's interesting. It's a very interesting path to kind of have experience in the accelerator space and the investment space before actually taking the plunge. Clearly, you you liked what you saw.
Christina Guo 2:11
Yeah, definitely. I mean, I think what really got us into thinking about data in the first place is that it's actually where like, most of the answers lie, like how do you find product market fit and sort of like, manage your team, a lot of that centers around data now. And so it's really like, what's underpinning most applications. And so that's pretty much like where we first got into it. But Berkeley has always been like, sort of an early space for like, a lot of data tools and data innovation. But I think that's mainly the nature of like being in the bay almost as everyone I feel like gravitates towards b2b SaaS, a little bit. Yeah.
Max Matson 2:47
Very cool. Very cool. What actually like specifically led you to founding queue, right, because I know we were just talking a little bit about how you guys have kind of done some need finding and found your place in the market recently. But what what was that initial kind of like kernel that led you to try it out?
Christina Guo 3:06
Yeah, it's a great question. I feel like people are always surprised because they're like, oh, like, you guys were in like data engineers before. And that's definitely true. So my first actual, like, real job was actually writing about climate risk analytics, for this climate risk startup, that's now part of Moody's ESG. Branch, I think it's called. And so that was sort of like my first look into how data has become really valuable. But for us, at least, like we sort of my co founder Kailyn, I worked at companies that were very different types and spectrum of how they worked with their data. And so I've worked at places where it's pretty manual like company grew very, very fast, but mainly relied on like Excel polls, and like a lot of automations. I think at one point, there were maybe like 10s of 1000s. Putting the whole thing up, I co founder setup like warehousing and like a very like technical first approach where like creating external ports for customers was like maybe the engineers job and they were doing all this, it's like a seed stage almost series, a startup, so relatively more sophisticated. But for us, like what we first started looking at was was growth tooling, and thinking about how most startups have to pick between investing their data or investing in growth, which is maybe not a trendy thing to say, in the age of like everybody's data driven. But the reality is like for them, it's so much work relatively and for them to pick between hiring like a salesperson that can close a billion dollars in pipelines so they can raise your next round, or a data hire is actually like, maybe like an unsaid thing that we've sort of realized that a lot of teams are running into, especially founders who are willing to kind of put that aside to just focus on like hiring engineers and hiring salespeople mainly. So that's been really interesting. And then I think for us, like we had, we're always surrounded by like a lot of data founders in the last couple years as well. And so much of the tooling is is quite complex. There's been so many data tools popping up in the last couple years. And maybe it was a bit of a naive thing for us to look at it and be like, Why is there nothing where we can just plug in our data and get the analytics and reporting it everything we want out of it, because we're sort of used to that. We're great software, and most other things like our sort of bank is so modern accounting solution is very modern. And pretty much everything else we use, like I use a different browser, just because it's like fun, and it cleans my tabs up better. Like I think, maybe we represent more of a discerning user. But I think most software's should go towards that where it's like, it should be fun to use, it shouldn't be that easy. You should just get what you want. And AI has played a big part of that and cutting down the implementation that previously made that impossible and have people charging $1,000 implementation fees and, and having you set up things for basically six months or dole out a couple grand on a consulting agency.
Max Matson 5:53
Totally. Yeah, that's a great point. And so when we're talking about growth data, right, what specifically are the inputs there is that things like, you know, your front end data mixed with CRM mix with product analytics, what what does that actually look like?
Christina Guo 6:06
Yeah, that's a great question. It's pretty different for different industries. But I guess like for SAS, what we see a lot is like CRM, data, marketing data is all over the place. If you think about ads, Ben, marketing has like a ton of different tools are testing a ton of different channels. And then you want to like Link marketing sales together, which is very, very hard product data, as a lot of people have plg arms right now. And they're trying to figure out a converter to sales. And that's a lot of tools have popped up to try and like bring those two data sources together. And then of course, you have, it gets more complex when you think about industries like thin tech, or healthcare, or even ecommerce, which has so much more data, arguably, at scale. So that's sort of like the main, I'd say things to think about. But I mean, whether it's like 100 different data sources, you have to port support or 500. They, depending on what industry, they do generally sort of use the same stack, which is, I think, also maybe surprising to people. But
Max Matson 7:08
yeah, interesting. So it's almost like, I mean, I think that this is a trend that I'm seeing a lot right now. Right. And I think AI is a large driver of it is going from these silos of data, even within departments, right. So like, I think that that's a huge thing, as kind of a couple person marketing team, it can be so daunting, trying to link all those things together and get anything usable out of it. Right, feels like 95% of that time is spent just trying to connect a dashboard to 500 tools. So with a background and growth, you were seeing this from people that you were working with this issue was kind of a prescient one.
Christina Guo 7:48
Yeah, I mean, I think it's interesting, because I guess ondeck was more of like a talent marketplace sort of company. So it's actually really manual, it took a lot of manpower to run way more than any traditional SAS company. And we originally had a lot more data, because we were running events, and then also, like, had a number of product arms. And so more than anything else, I think it was difficult to manage. And the tools were just not super customizable for that sort of, like manpower heavy company. But yeah, more generally, I feel like are like the generation that's going to come into starting to use and buy software, like, we were like playing Club Penguin and like, whatever on the internet relatively early. And so you kind of go into it with an expectation of like, it, you're so comfortable with software ready, whether that's Instagram or, or, you know, photo editing apps. And that's why so many of the like, new applications, whatever it is, be real catches on so fast. Because everyone's so familiar. That sort of person is coming into using software, and building companies and running them and work in them. So
Max Matson 8:58
cool. So um, all right, I love that point that you kind of bring up right of this new kind of generation of buyers, right. And I think that that's something that a lot of people miss when they're talking about new generations of software and how AI can actually make software more accessible for folks, and especially in the b2b space. Right. I think it's more common to think about it in the b2c kind of setting. But like you said, I mean, people of our generation have kind of come into, like running companies running marketing departments running growth departments. Like this is just the new reality. How have you seen kind of that digital native adoption changing? Like software patterns? Is that something that you think a lot of companies are kind of behind the eight ball on?
Christina Guo 9:40
Um, yeah, it's interesting. I know. I feel like I don't want to call out companies. I think you had a fault maybe but I get what you mean.
Yeah, I feel like it's weird even thinking about the fact there's gonna be a lot of people who are AI natives who have never like really lived conscious adult life or pre adult I guess without AI which We'll also be super interesting. But I think the way I think about it is, the tools that I see like sort of the people in the bubble adopting are very, very different. Or like, let's say, just like early adopters, you saw this crazy tool. The other day was called Vizard die, I think it's missing Saturday, but it like takes your like zoom transcription and splits it into different clips. And what was so magical about it was like, it clicks around like your cursor. So it clicked for like two straight minutes of like doing everything you would have to do manually to get you that same end result. So it was like a really magical software experience. And I think, I mean, it's hard to be like super to make something like, I don't know, inbound sales or like something like really like SAS, or like data, like really, like, magical I feel like, or just in general, I feel like enterprise software is hard to do, because they just, I don't think they're looking for that kind of experience. But for who we're building for which it's like modern, growth focused, very efficient startups who are building the same sort of modern experience for their users, they do expect a certain level of like, product should be opinionated and should be very fast, you should get exactly what you want. And you should have like, incredible customer service. So I think those are maybe the four pillars, and a big part of that hinges around opinionation. Like, you should tell your user how you think they should use your software the best to get what they want. And I think that's maybe been what's missing in a lot of the data products we've seen where, like for most people, and we'll hop on calls, like CMOS all the time, and they're like, What are other companies doing? Like, what is the standard here? Of what metrics you should be tracking? Are we missing anything? How are other people approaching this problem? So it's very much like not just, you know, like, only if you're new to the rules sort of thing, but also like a general curiosity of how other companies and their industry are tackling certain problems. And they want to sort of crowdsource that knowledge, if you will, which I think companies like Canva, or, or notion or finger or linear did really well in terms of like being able to have like a really, really opinionated product that tells people how to use it almost.
Max Matson 12:14
Totally. Yeah. It kind of leads into another question that I had, which is, so you named a few plg? Companies that right? Or do you guys, if you have a motion? Are you considering plg? And if so, how do you kind of see the evolution of plg? Now, right, because I think there's a lot of contrasting information coming out in both directions about it. A lot of hype, right in both directions. But I do think that the OG is still very viable as a motion for new startups. But But where are you guys learning there?
Christina Guo 12:44
Yeah, it's really timely question, because I feel like we went back and forth on this for quite a while. I mean, it's, it's tough because data tools have so much usually like hurdle. So it's like definitely the first batch of people we work with, we need to like we want to build such a great relationship with and do as much as we can for them. You can't really do that with plg. And I think the other piece of it is that you sort of like, Ah, I know that there's been a couple hour articles out, I guess about like how people took different approaches to it. But for us, at least, I wouldn't want to release anything that isn't like very, very, like, great experience. And with AI, that's also like another another factor in terms of the sort of quality you need to run a plg motion. But it can be great in terms of like, okay, if you're not like already, like a VP of engineering, or analytics for the last 30 years, PLJ is great, because your product can speak for itself. That can be good. So well, we'll have to test it out and get back to you. But it's definitely something we want to do, because I think people can prefer it. For the most part. Totally.
Max Matson 14:00
Yeah, it's a it's a careful dance. I feel like with data products, right? Because you are typically selling to people who are pretty technically minded, and a lot of cases, right. But there are a lot of new data products that I'm seeing that are kind of going after that the rest of the crowd, right, not just the kind of owner of SQL. It's now also everybody who is actually taking in that data who is requesting that data, right. That being said, I'd love to kind of get your perspective on how you see the role of growth kind of changing, right? Because with all of these products with all these changes in terms of like org structure in the market, and there's a lot of hype around kind of how that role is going to be altered, especially with kind of AI technology. Do you have any perspectives there? Oh, I
Christina Guo 14:48
feel like I've talked to like 30 heads of growth in the last couple months and they all have kind of different role descriptions, almost like they rarely kind of hop on and say they're working on the same thing as the last person said I guess the most frequent, like overlapping definition has been somebody whose it seems like a lot of the more plg companies have a head of growth, for instance, because otherwise, I guess you'd call it like a head of revenue or something else maybe. But in terms of how AI is going to change that, what did I put to your question to do? Did ya something else?
Max Matson 15:26
No, I mean, I'll keep you can keep going if you want, but I think that's pretty good. I do think that the role itself is so squishy, right? Like your price on there. I don't know that I've talked to another head of growth that has the exact same responsibilities, right, especially when you're talking about people that are one person growth teams versus multiperson. But all that being said, I would kind of like to touch on the bottoms up layer that you guys talk about? Because I found it super interesting. Could you just kind of define that real quick, as
Christina Guo 15:58
in like, in product, lead growth context, or something else?
Max Matson 16:04
So more? So in terms of technology strategy, right? combing your homepage?
Christina Guo 16:08
Got it? Yeah. So I guess the short TLDR of what we do is, if you're a startup that has a hot data mess, so either like a data sources, or even just like for you can't figure out how to answer questions that involve multiple data sources. What do we do if you went with us for I guess, the whole package, instead of like little bits and pieces, which feel like most people want to do bits and pieces, but that's just the nature of like the data world, right? Now, we would be able to set up a warehouse for you. So essentially, the goal here is to create a single source of truth for you to unify your data. And traditionally, so setting up a warehouse is not hard for most people, to be honest, but it's more so if you like don't want to devote engineering resources to set that up. Front data pipelines manage that, which most people right now are very focused on on core product. And then so what we do after that is have essentially out of the box metrics for you. So you asked me earlier about what do a lot of businesses have in common, and usually, like startups have to custom build all of this. So if we ran a company together, we'd sit down and decide out of our heads, like 30 different metrics, you want to track or 50. And then a CMO could come in, and they have maybe like 10, more. And if a new CFO comes in, they might switch it up. So it's like very, like people get really creative with it. And I think maybe like, controversial take is that that shouldn't be the case, we should have like some sort of standardized, like, if you're a SaaS business, here's like, what you should be looking at. And maybe you can like, adjust a bit from there based on you know, your FinTech or whatever. But for the most part, you shouldn't have to start from scratch. So we have these out of the box metrics for us. And we're handling most of the data modeling stuff. And so if you want to get really sophisticated with it, and have your data person or engineers sort of tweak it super easy to do that. But the nice part of having a standardized metric later, or having a very early is you can actually get sort of out of the box analytics for the first time. So very rarely can you do like, Okay, I want pipeline analytics, give me a pipeline analytics into template, and you get all those dashboards built down. But now, because we have your business context, in a sense, I able to do things like if I just go into my let's say, like KPIs graph, I can, it's already like, built out by legs segment by industry, or I can drill all the way down to like your account level, I can see my product usage of like my biggest accounts. And so all of that previously, you'd have to sort of build out and it takes, obviously, this is like the last thing most startups want to do. So they're gonna put off doing any of this till they're like series A or B, and they can hire a bunch of data engineers to manage it. Which is why they run in the dark, sort of, because the reality is, for most startups, they care about growth more than anything else, and you don't, if you have product market fit, you don't really need a super sophisticated data sales setup is the reality because we've talked to so many companies that are series A Series B, like killing it and ARR but like don't know where their lead sources are coming from, which is unpopular b2c for, like, you know, you need data driven growth. But for a while most people couldn't even afford to, like, you know, store their data. So it's interesting in that sense, but what we're sort of hoping to do is make it so cost efficient and so easy for you to know what's actually going on in your business on every single Friday, without having to take time out of core product or drop, like six months of runway, basically.
Max Matson 19:36
Totally, no, I love that. I mean, it's something that you know, I've definitely seen it for my experience, right is especially when you are just trying to focus as much as possible on building that core product experience. It is so hard to go to your head of engineering and say Hey, can you dedicate three days to get me you know, click through rates on on this landing page or something, right. That's super interesting. So you kind of moving forward from there. You mentioned customer facing analytics earlier. Right, which I think is a pretty powerful concept. How do you see businesses leveraging this to kind of build stronger relationships going forward?
Christina Guo 20:13
Yeah, so that's a great question, I think in two ways one, so for an early stage startup, you're just trying to prove your ROI. And you can't really do that without essentially a couple of dashboards. And so you can either sort of meet your user and their workflow, which is in product and build that. Or you can sort of like put in like a deck or something like that, if you sell mid market enterprise, I guess. But for the most part, I think the value there is, or I guess the challenge is that dashboards aren't usually most, like most of products value, whatever product you're selling is just a way to measure it. And so for most people, they just want to like prove ROI, so that there's no turn their customers are happy, they can like go to their boss and say, like, look how much money we're saving? Or, look how smart I am for picking this tool, etc. And so I think that's kind of the value there. For Yeah, we were, I guess, most BI tools don't have like embedded external analytics, just because I'm actually not sure why. But for us, we just wanted to like meet the startup in whatever like stage of their life cycle was where they need to work with like a bunch of data, and actually use it. And for some people, honestly embedded like embedding graphs in their product comes way before setting up analytics for themselves. So yeah, that's good. Thank you behind that.
Max Matson 21:34
That's super interesting. I definitely think that you're ahead of the game there, right? Because kind of with the changing market conditions and alignment to revenue and everything, especially for like plg companies, I can only imagine, you know, what it takes to actually get somebody motivated to, to push product forward and their work, right. So having that kind of clear ROI is a huge step forward. I hope that you know, you guys are able to get make that kind of like the standard, because I would love to just get an ROI number from products that I'm pitched daily.
Christina Guo 22:06
Yeah, it's a CFO stream is to be able to get that number to see what to cut. But yeah, definitely, consolidation is like, I feel like what we've heard the most in our comments, like, number one, consolidate everything, if possible. We've seen public companies sort of like, even like, compare a bunch of tools and like, do a bunch of pilots just to save like five grand and their, you know, Stock Exchange, or like to, I just cannot bother my engineers, I think is like maybe the second most frequent thing we've heard. Yeah.
Max Matson 22:37
Oh, yeah. Yeah, I heard that one quite a bit. So kind of jumping backwards real quick, I would love to kind of explore a little bit more about your time at Kaos accelerator. What is you know, and you can take a second to think about it. But what's kind of like your best story from from that experience?
Christina Guo 22:56
Interesting. Yeah, that's a really deep question. Um, I feel like maybe two. The first is, I feel like you see so much of the reality of like, what happens, right, like, there's co founder breakups. And when I was at school, I never really thought about being the founder, either. I think it was more of like a, like, trigger happy moment after I met my co founder. And we were just like, really hitting it off. And so I just usually try and do whatever is like, most interesting, generally. But I think people will assume founders either have to be like, have like certain traits or like, have like, massive risk appetite. But you see, when you see so many people ended up being successful. In their worst moments, it really reframes like, how much do you think you can take? And so that I think, was like a big factor. And then the second is, AI. So we would like work with like an internal team. So it's just a bunch of Cal students running the program can't really pinpoint a specific memory. But I feel like that just was also one of the best experiences in terms of like, like, what can you do like in college to like, set yourself up for success? I feel like just picking something you're interested in and putting everything you have into it. You can't really go wrong with that. But yeah, I'm sure there's other anecdotes that are maybe were interesting, but I feel like maybe we shouldn't be on the future. Podcast.
Max Matson 24:22
That's okay. That's okay. I think you raise a really interesting point, which is that a lot of the founders that I've talked to actually, one of the things that unites them is that they are the most interested person in whatever topic they're exploring, right? Like, it's not even so much the that there's any other unifying factor that I've seen other than, you know, all the standard ones grit and determination, leadership, but having that kind of core like kernel of interest in what you're doing? I do think it's such an underrated facet of it. Can we go into a little bit more of how you knew that your co founder was was the right one because I think that that's something that a lot of people who are who are looking into Entrepreneurship struggle with.
Christina Guo 25:02
Yeah, I mean, I guess it's a bit of a unique case, because I wasn't really looking for a co founder, I was working full time as an investor, and he was working on another company. So definitely wasn't like I was like, you know, scouting for, for co founders, which I think is also hard to do. But for me, it was just like, we just five really well, and I really enjoyed like growth hacking with him on his company. So I was putting in like, a lot of time outside of my full time job working with him voluntarily. And so when that chapter was short of ended for both of us, it just seemed natural that I mean, it was like, Okay, let's look at how the next three months are gonna go. It wasn't like, I don't think at that point. I was like, I'll never recruit again in the next two years, but I still haven't. So yeah, even before we raise money, I mean, we probably spent 669 months working together seven months, I don't really know, before we raise any money, which I'm now realizing is uncommon for people. But at that point, I feel like I was just going by vibes, which is not a way I recommend people make decisions. But at least for other people, I feel like the best co founder Duo's were like at just like friends firms generally. Because you have to spend so much time with this person. And the to do really enjoy spending, or, you know, actually doing the work because you can pick anything to work on, right? Like nothing was like binding us to a certain industry. So I really like Amanda's story too, of like, how the other founders sort of like found her way to sock to compliance, which is really interesting. But it also depends, like how, how much you love, like solving, or like doing startup stuff, like solving problems and like, being able to find customers and serve them versus like, you're like, gung ho in a certain space. And it's different for everybody. But I think, Yeah, as long as you're you're really, really, really into it definitely helps. Because when things get hard, you're like, yeah, yeah,
Max Matson 27:03
exactly. I need somebody's gonna be there. You mentioned growth hacking. What's your perspective on this side of growth hacking?
Christina Guo 27:11
Like my position right now?
Max Matson 27:14
Yeah. So not necessarily. I mean, you're a founder. Right. So growth is a component, but not being specifically with the title growth hacker or working in the growth hacking space? Because I think it's kind of caught a lot of flack in the last few years. Right? Yeah. Right. With more traditional marketing. Do you have any kind of perspectives there?
Christina Guo 27:30
Oh, I don't know if I have too many informed perspectives. But for us, I feel like we just boil it down to do something creative. And you think my work and if it does work, double down on it, which is not really unique marketing advice at all. But like, some things I'm thinking about are like filming tick tock in my lives to put on LinkedIn which, which is definitely going to be, you know, a choice. But for me, it's like, okay, what I personally watched somebody's life, or like, another webinar about, you know, who knows what, and it's like, ultimately, like, at this early stage, they're betting on you not necessarily like the quality of your webinar or your like press materials. So the more you can be, like, actually honest about what's going on is maybe the more interesting thing.
Max Matson 28:15
Totally, no, I love that, because it kind of juxtaposes with what you were saying earlier about the product, right, which is that you want to bring this like, as close to perfect as possible experience to people when you're actually going to sell right. But when it comes to the marketing, I would agree with you that it's more just about getting things out trying things, seeing what lands in these kind of early stages, and then going from there, because, you know, too often I think people put all their eggs into one basket, they make it absolutely perfect when it comes to the growth strategy. And then their ICP pivots, right? And now all of a sudden, it's like, oh, I have to start over. So I think you're pretty spot on there. So all that kind of being said, taking a step back and just talk to you more generally. What do you kind of see as the biggest misconception that's floating around regarding AI nowadays?
Christina Guo 29:04
I feel like maybe two things. The first is that it hallucinates or glitches lot and that makes it unreliable. That connects to my second thing, which is that AI is only going to be best fit for automating manual work instead of creative work. And I know people don't want to be like, I mean, there's a lot of people who are like, it's gonna take all of them everybody's jobs and the other four like No, it'll just make you better. But the reality is, like, I guess what's the only real thing that's shifted in the last like year or so is the rise of LLM. So when I think about like, what industries that will impact like any like really, industry with a lot of unstructured data is like maybe legal or healthcare, but I don't know how much of those are online also. So who knows? But I read this really great article by the US team is Ben Stancil, who's the co founder mode and his thinking was along the lines of how, like for human data analysts, we give them a lot of room. So it's unlikely the person is gonna come in and just know how to calculate or like get to an error answer on day one, you have to sort of work with them and give them feedback. And, you know, we're like, that makes sense. It's their first day, their first week. But for AI, it's like, they take a shot. And then if it doesn't work, if you're like about or maybe he'll, you'll give them like two or three tries, right? And that ends up in product flow, too, which is like, how do you make sure you have enough inputs so that not only is the answer accurate? But is it that it's super easy for the user to go in and fix it? Like, if you fix it, it's going to undo the whole thing? Is it going to make a result that's like worse than what you already have? So like, things like that were like what I think it'll be, it's actually arguably better for creative work than automating really manual mindless tasks, because I think it struggles with math. But like, if I were to ask you to plan my trip, it's like amazing, right. verta timezone it struggled a couple of times. So maybe that's like, the more interesting piece is that, for better or for worse, it is very, very good at Creative strategic work when we give it the right inputs and like forgiveness.
Max Matson 31:15
That's super interesting. It's kind of this new paradigm of how to treat software, right? Where it's, it's less so a widget to achieve X, Y, or Z, it's more so like a process, right? It's introducing this new process, this new way of kind of coming to that conclusion. So what does the kind of landscape look like we talked earlier about companies who delay, you know, building up the data function, because they're so focused on growth, which, you know, is a logical choice. But what is kind of the end game for those companies? Right? Like, what what is the scenario look like when you forego investing in your data systems too long?
Christina Guo 31:52
Yeah. It's challenging, because I feel like, like, a big paradigm is like, okay, there's a ton of crazy crazy things here data. And if you can only find it, you are going to for x ARR at a fraction of the costs. And it's usually not like that it's very moderate improvements, which, which makes sense. If you're like glossier or like, I don't know, read it, because you have so many users and so much revenue that even a tiny optimization is like, your quarters, your quarter is fantastic. But for startups, that's not really the case. Right. And so I think more often, what we've seen is like, just general, like lack of efficiency, in terms of like, if your customer success manager doesn't know how people are using the product, it's actually really hard to upsell. Or if you could actually get some insights into what products like ecommerce products are performing the best with this one person of like age 1721 Girls in, in this state or like that have previously bought that, that any products like that, like who wouldn't take that optimization, right. So it's more so like a lot of incremental, moderate games that you're missing out on. And then generally, the fact your team is confused on what's happening, there's no scoreboard, it's probably not great for morale, if you like, can't really measure or like point to like results. And it makes them harder for for them to do their jobs, because you can pressure your CFO to like deliver results, your head of growth, deliver results, but you don't know where the marketing leads are coming from or from what segment. It's It's so hard. And so they're kind of running in the dark, and maybe they'll throw money at it. But it's just like one of the things that has been put off, because it's moderate gains, and it's really hard to set up. Which is why we're sort of looking at like, how do you decrease this set up barrier? But the truth is, is that it's just a lot of water gains over a long period of time, but it does impact your core team members and how how well they can do their job?
Max Matson 33:49
Totally. Yeah, I mean, I think those things often get overlooked with kind of like short term thinking, right? It's like we need just to hit this this this revenue number. It's not about tomorrow, it's about today, right? I think that type of thinking can often kind of lead to those types of scenarios that we're talking about. One thing I would love to kind of pick your brain on is with being like a growth oriented data company, how are you seeing kind of the changes when it comes to like, the potential for like, deprecation of cookies, you know, all the ios kind of changes, and all these things that have made it a lot harder to attribute right to actually see where that lead came from?
Christina Guo 34:26
Yeah, I mean, I feel like the call we've made is to sort of avoid all of that, and it's more so like your, as a marketer, you're gonna pick the best attribution tool because you probably know best in that domain. And those tools are going to catch up and hopefully they're going to find a solution. And then we'll plug into whatever you've already bought and make it easy to use almost are actually usable. But other companies like triple well, for example, I think they had to build some in house snippet thing To track where I think I'm probably butchering this, but to track where I think like purchases are coming from or how people have found them. But that's also maybe because they had sort of already built out a core product. And that was like a missing piece of customers kept asking for so can make sense down the line. But I don't since we don't have an Industry Focus, it's maybe not super in scope right now. But definitely a lot of interesting developments on that front.
Max Matson 35:30
No, it makes sense. Makes total sense. So we're getting somewhat close to time. But I did want to ask, how your experience in early stage investing, like actually motivated you and kind of informed the path that you took to investment and to kind of starting your company?
Christina Guo 35:46
Yeah, it's a great question. So first, again, like I think nothing, nothing about nothing stops anybody from being a founder, and that it's not like super, super human, you could get to a series B and C and still plummet, right? And so, at the end of the day, it's just like, who ever like wants to be like you can, you can make it happen. There was no like one form of person or anything super magical about the people, obviously, they're like incredible humans and stuff, but still not like 10x more awesome than any, like anybody else who isn't a founder. I think second is that fundraising is a bit of a jaded view on fundraising. But I think it's like the easiest part of building a company almost, which is, it's so much easier for people to raise money than to find true product market fit and build a product people really like. And so is it necessary, maybe if you like, you know, your product is engineering heavy, or you just like, don't have the means to bootstrap or something like that. But I think investors ability to help you out is maybe a bit overstated. And that, like, you're still going to have to drive 99.5% of it. And the fundraising becomes really easy. Once you have some semblance of Panama for even like, you show some hints of getting there. And so if I were to give advice to myself from like, a year ago, I'd say maybe like, don't go to any, like networking events or anything, like only focus on the product, only focus on the customer, everything else is noise. I wouldn't even like build any product before like any like real, like validation, like seeing people excited on calls and that kind of thing. So, yeah.
Max Matson 37:30
Interesting. And you mentioned earlier that you you've sat down with a lot of growth leaders, right? I would imagine just a lot of people in general, would you say that that's one of the core kind of need finding strategies that you've taken to discover, like, what to build?
Christina Guo 37:44
Yeah, I mean, we've read, I feel like everyone goes about a different way, like some people are, like, just launch product, and other people will do like sigma, famously took, like, a couple years to to get through it and just like move to Japan and started like building it out. So I think maybe like the point where we stopped having as many calls was when we stopped learning new information, or at the very least, like even though like calls that are quote unquote unhelpful as in, like, they don't get your product, that's helpful to know who is not going to resonate with. So just being able to narrow down who you target, it's super helpful. And the space we're in, I feel like traditionally has not been great for a lot of companies in terms of the most challenging part is selling. So I feel like always having like that feedback loop and, but there's always of course, danger. And that if they're not paying customers and listening to people, it's hard, but I feel like we've learned so much. And I recommend anybody to like go send out like 200 LinkedIn pings a week to really targeted people, because it's a good skill to have. And just like, it's like one of those things where it's like, getting like rejected online and like making conversations elevator is all like it really helpful, like muscle to practice to just like not have that much shame left, which is a great trait for most people.
Max Matson 39:02
Yeah, I feel like founders and stand up comedians, both have kind of the same arc, right? Yeah,
Christina Guo 39:09
the villain arc a little bit. Yeah, it's
Max Matson 39:12
like, well, I go up on stage and everybody booed. But you know, I learned a lot and next time I'm gonna do better. So last question. For any potential founders, let's say even yourself before you knew that you wanted to go into founding a company, what would be the one kind of outstanding piece of advice that you would give regarding figuring out what that discipline is?
Christina Guo 39:36
Yeah. I guess in terms of like, like discipline, like skill set or industry or
Max Matson 39:47
industry, kind of, you know, choosing to go into data versus any of the other kind of potential lanes that you could have taken.
Christina Guo 39:53
Yeah, I would just say probably start building in one form or another and whatever in streets you think you could be interested in? So if you think you're interested in FinTech, like, go work at like, an incredible like Series B company that's growing really fast for like a year or two, and see how they operate and see how they build, because maybe you can productize something, you know, they didn't end up building, or you'll also just meet a lot of people that are gonna be really helpful. But yeah, I think obviously, the perfect way to do it is like, you know, you go into a couple industries you're into, you learn from a ton of people you spin out, make a product, it's perfect from the get go. But for the most case, like, that's not, that's not really how it works, right. So we probably have to pivot round, but I think for the most part, if you pivot in the same space, and you're so interested in that space, like, it'll be okay. And worst case, so many companies have done well, just by launching something they built for themselves internally. But, you know, one way or another, I feel like, the best piece of advice I ever got was just like, if you refuse to die, you just like won't like there's always another game to make. So whatever that looks like, like, Yeah, but I mean, all things considered, like, if you were so lucky to be doing something like this, right? And so in the grand scheme of things like the world will end, like, you should just have fun with it properly, do whatever, like you think you should be doing and kind of ignore what everyone else thinks in terms of like, how would you have to be or what you have to do before then because that's, that's not how the market receives products. Like, if you can build something that you can be really proud of, and people like then that's all that really matters.
Max Matson 41:31
Totally. And what was that quote real quick, was if you refuse to die, you won't. Yeah,
Christina Guo 41:35
if you refuse to die, you just, yeah. So now, on a podcast saying if I refuse to die votes, if you see a shutdown post, like two years from now, you can come back and shame me for it. I know he said that on this podcast.
Max Matson 41:52
That's a commitment. You know, that's, that's, that's a great, great advice, right? I think you're spot on. It's just about doing it and refusing to give up, right? If you're able to build something useful, somebody will find it useful. So I'd say that's, that's great advice for anybody out there. Now. Christina, would you mind telling people where they can find you follow you where to you know, follow what, what Q is up to?
Christina Guo 42:19
Yeah, definitely. Our site is a little unsettled right now, but won't be for long. And you can find me on LinkedIn or I'm sure if you look hard enough me on Twitter, Instagram, email to
Max Matson 42:32
perfect, awesome. Well, Christina, thank you so much for the time. It's been a joy. Yeah,
Christina Guo 42:36
thank you. This is super, super fun. So thanks for having me on.