GTM 153: Building Technical Growth Machines & Signal-Based Selling with Austin Hughes

The way that we thought about growth as a function was much more akin to how you run a product team. At an earlier stage, you just have to take big swings. We announced our seed fundraise in early 2024 on LinkedIn and immediately had, I think, 420 demo requests come in over the course of the next 48 hours. We also took a very first principles lens of the thing that we cared about was maximizing the dollars of pipeline that we could drive per hour of work. We saw everything we were doing as taking bets. Distribution is the advantage that we're looking for in a startup. We could build the best product in the world, but if nobody cares about it, then it doesn't really matter. The thing that matters most is the impact rather than the actual solution that you're building. Buyers just want to solve problems. And if you can prove that you're the best person to solve that problem for them, like, they'll take you up on that.

Behind the scenes, it was a small group of people that were doing everything.

I want to know how this insane growth actually happened.

Before we dive in today, a quick important message from our sponsor partner TriNet, a trusted HR provider to startups and scaling companies. Every early-stage founder is told to focus on product and growth, but behind every product launch and revenue milestone is a team, and building that team is one of the hardest and most important parts of the journey. Build the team that builds the company. That is part of your go-to-market strategy responsible for growth. Hiring the right people, keeping them supported, and creating the infrastructure to help them thrive is critical. TriNet exists to make that easier. TriNet's full suite of HR solutions is designed to support companies at critical inflection points, from early traction to scale. Learn more at trienet.com/gtmnow to see what's possible for your business. That's trinet.com/gtmnow. Now on to the episode. This episode explores how to apply product thinking to go-to-market. Austin Hughes, founder of Unify and former founding growth leader at Ramp, shares a behind-the-scenes look at how he helped scale Ramp's go-to-market engine, building the outbound automation program from scratch and growing the growth team from 1 to over 25. Now, as a founder, he's reapplying that playbook and reshaping it to help companies like Justworks, OpenPhone, and Cursor turn signal data into pipeline. We get into the tactics, how to validate and scale go-to-market programs, how to operationalize product data, what technical growth really means in 2025, plus why AI doesn't eliminate the need for growth teams, just changes how they're built. Enjoy the episode. Welcome back to the GTM podcast. It's your host, Sophie Buonassisi. And today I am joined by Austin Hughes. Welcome, Austin.

Thanks for having me, Sophie. I'm excited to be here.

Super excited to have you. Been actually looking forward to this conversation for a while. I know we've been jamming on ideas for, for quite some time. So excited to jam a little bit more publicly now.

Yes, 100%. It's been a long time in the making.

Definitely. And I'd love to start off with your background because you actually have a bit of an investor background too. Before we get to more of State of the Union around what you're working on now.

So, you know, today I'm founder and CEO, which is a ton of fun, but I actually got started originally in finance. And so before I moved over to the operating side, I spent a few years in investment banking and then in investing after that, looking at companies and sort of understanding how investors look at and diligence businesses, which was, I think, a pretty informative experience for me. A lot of the frameworks that I actually used a ton as an investor, thinking about unit economics, thinking about how you benchmark these companies and some of the underlying operating metrics that really matter are things that I think carried really naturally to being an operator. And so definitely grateful for those experiences. But yeah, was an investor for a couple of years, moved over to Ramp and then started Unify more recently.

Very cool. And when you were at Ramp, you actually built out the growth function there and started it from scratch and really grew it from 1 to over 25 employees. I'm curious, when you started that function from scratch, What was the first kind of system or motion that you built and why?

So way back when I joined Ramp back in 2020, company was a few hundred customers and the era was very different. And I think the way that we thought about growth as a function was much more akin to how you run a product team. And so the foundations and how we thought about how we'd organize our time and the work that we would do and how we would prioritize what we worked on was much less, I think, from like the traditional marketing lens and much more from the product lens of thinking about it in terms of sprints, running a regular cadence, writing specs, holding ourselves accountable for metrics. And so it's interesting, it's like, yeah, we were running a, you know, a growth team, which traditionally would fall more under the go-to-market team, but I actually sat under Jeff, who's on the product team. And so very interesting microcosm there. And I think when we thought about building up the program over time, we saw everything we were doing as taking bets, right? And so some of those were more traditional, some of those we could look at the market and understand, okay, you know, you've got your traditional channels like paid and whatnot. That are going to produce for you. But we also took a very first principles lens of the thing that we cared about was maximizing the dollars of pipeline that we could drive per hour of work. And so that led us to explore a lot of different channels beyond your traditional ones as well. So really at the core though, was this foundation of like, how do we operate more like a product team than most go-to-market teams would?

And with that mindset around product, how did you end up validating high-impact programs before investing real resources into it?

A lot of the art, I feel like, in growth is thinking about how to scope projects really well. And I think that this is often really overlooked, but something that I think we did remarkably well at Ramp was we were really diligent about how we spent our time and maximizing the amount of output per hour of time. And so oftentimes what I found was that there were projects that the team would want to work on that they'd written down would take 2 weeks. And in practice, you could distill down an early learning from that project into an hour or 2 of work, something like that. You could take something that somebody thought you needed to work with an engineer on and take it actually to be a CSV that you could sort of like just pull from a dataset and test something. And I think that idea that you could always sort of refine things and always de-scope things led us to this world where the way we operated was very much so in terms of MVPs and full builds is how we thought about it. So MVP is like an idea that you could build in a few hours. A full build is something that might take a week or two weeks. And so the way that we really de-risked a lot of the work that we did was by running MVPs to test, learn, that something had signal. And then when we would actually go out and do a lot of work, it was usually investing behind something that we had high confidence in. So that was sort of the foundation for us being able to move really quickly. It was just using our time really effectively.

Yeah, definitely that experimental mindset. It sounds like I've read that when you stood up the program, you're running over 100 experiments per quarter, which is a ton of volume, very high velocity. How did you set up those systems for experimentation, measurement, and feedback? That whole loop?

Yeah, you need like, I think, a couple different pieces to do this well. One is that Everybody on the company side needs to just know how to dashboard and do analytics. And I think that's pretty table stakes is that it is certain scale intuition is valuable, but it starts being overtaken by your ability to use data. And the only way that we were able to drive so many concurrent experiments and learn from them is that we had clear reporting and metrics and the team was just generally data fluent. So I think that's really important. We also had a centralized CRM that we used to just like track what we were working on in a very systematized way of going about this. So no learnings got lost. And I think that was actually also really important was that When you have so many things going on, you need to make sure that everything, you know, it's the same thing of how you would go to market and make sure that you're using Salesforce or HubSpot. You need to make sure that you're not crossing your wires. And so I think we did that really well from a systems perspective. I think also just the way that we organized the team was much more around the goal of, of pipeline. And what that meant in practice was that teams that I think historically would have sat in silos actually were very sort of like cross-collaborative at Ramp. And I think that was really powerful over time. We were able to try things that probably would have been shut down at a company that was organized in an orchard way.

I got to ask, was there an experiment or channel that you doubled down on that changed everything? Maybe even if it didn't look promising at first?

I could talk more about the Unify side. I would say Ramp, there's no— there was no silver bullets. I think it was just a lot of hard work over many years that compounded continuously. And so I think that actually framed a lot of how I think about things at Unify. At Unify, I found that the silver bullet for us has been LinkedIn. Honestly, I think like it's very fascinating to watch this pan out. I think it's the one bet that I've taken as a growth operator where it just made sense out the gate. It just worked immediately. And for context, we announced our seed fundraise in early 2024 on LinkedIn and immediately had, I think, 420 demo requests come in over the course of the next 48 hours. And most of that we were seeing was being driven by LinkedIn. That was an incredibly powerful signal, I think, to us that this channel and this medium works really well. Since then, we've really doubled down on the channel, which I can talk a lot more about that.

You're a pretty prolific writer on LinkedIn, Austin. Was that what kicked off the writing? Was it your seed announcement?

It was. And I, I gotta say, like, there was just a lot of noise in our category at the time, and it was fascinating. I started posting in, call it March or April of 2024, 5 times a week on LinkedIn. Before then, I was not a LinkedIn writer at all. I actually wrote a lot more on Twitter than I did on, on LinkedIn. And that seed announcement was a really good indicator, but then also our category was just very loud on LinkedIn, and it felt like we would show up to conversations and people would be talking about our competitors, not because our product wasn't better or things like that, but they just didn't know about us, right? Actually, like what we found was that distribution was actually the thing that we were really struggling with and credibility and whatnot. And so I think actually heavily investing in LinkedIn boosted our credibility faster than anything else could have. Now when I actually meet somebody in a conversation, it comes up half the time. They're like, I see your LinkedIn content over and over and over again. And I'm sure at a certain point some of that gets repetitive, but I also try to keep it fresh. And I do think that at a certain point, somebody seeing Unify's name over and over again, it makes a huge difference and eventually will reach somebody at the right time when they are actually ready to buy. So it's been really powerful for us.

Well, if anyone does not follow Austin on LinkedIn, highly recommend because I would say you absolutely do keep it fresh. It's always very interesting, very different perspectives, very informative and tactical. What does your system look like for publishing and writing?

I run a regular schedule where every Sunday is like my LinkedIn day, so I'll sit down for 4 or 5 hours, plan out content for the week, think about ideas that I want to write about. Usually most of my work happens on the Sunday where I sit down and actually write pieces out. Usually though, I keep a note in my phone of just ideas that I think are interesting. And it's funny, they come about at like very random times. I'll be like on a hike or walking around the city or something like that, and something will sort of come to me as an interesting tidbit. And I'll usually just take note of it. And then when I need to actually sit down and write, I will just pop into that note, sit down, write out the idea. So that's sort of how I approach it. We're also actually going to start introducing a lot more video content, which I'm really excited about. So we have a whole new process for that. We have a team member actually that joined our team specifically to work on video content named Adara. So huge shout out to Adara. And we're going to be introducing a lot of that to LinkedIn very soon, which I'm excited about. That'll be something new that I think will be fresh for the audience.

Incredible. Shout out to Adara. Can't wait to see that content coming. And are you using ChatGPT or any AI to help support your writing, whether it's, you know, voice recording yourself while you're hiking up the mountain and an idea or inspiration inspiration pops in.

I today don't use it at all. I find that the style is just not how I want to write things, and the words matter so much that it just hasn't quite gotten there for me personally. But I do use it to record just like random ideas sometimes on my phone. I do use, you know, Slack notes too, or voice notes as well. So I use a variety of ways of just getting ideas on paper. That actual writing piece I do myself.

Love it. I think it shows through the writing. I know I'm definitely a big fan of the, the ChatGPT Audio. That's helpful. It just went on the move.

It's awesome.

I want to transition to a little bit more around the technical side. As you mentioned, everyone was very fluent with dashboards, had that technical ability. How do you evaluate whether to hire a growth marketer or a technical growth lead at an early stage?

We're in a world today where I think it depends on where your company is in lifecycle, right? Like if you're looking for somebody ultimately to do a very well-scoped job and let's say it's like, hey, you want somebody to go run a performance marketing program, you've got a budget of $100,000 a month, whatever it might look like. I think in those worlds, a more traditional growth marketer can make a ton of sense and can be a good fit for that role because ultimately the role is well understood. You can find someone who has 4 or 5 years of experience in it. You don't have a ton of volatility around that. You know what to goal someone on. I do think though that depending on what you're looking for, oftentimes people will sort of conflate a growth marketer with a bunch of things. Sometimes it can be, oh, I want someone who can do paid, who can go do the website, who can maybe build product here and there, et cetera. And I think in that case, you want someone who's more technical. So our, our first couple growth hires at Unify were all folks who came from very non-traditional marketing backgrounds. And so whether it's somebody coming out of investing or it's somebody coming out of being an engineer, we find that those more technical backgrounds just lend themselves to solving problems from first principles. And that's ultimately a lot of what we do. And so We've found a ton of success with our growth leader named Bria today. She comes from a background of having been an engineer and also having been at Bridgewater as an engineer there as well. And that sort of like mentality of how do I solve this problem from first principles just translates really well to being able to solve channels in ways that you hadn't really thought about. And ultimately like that distribution is the advantage that we're looking for in a startup. We can build the best product in the world, but if nobody hears about it, then it doesn't really matter. And so That ability to think outside the box is something we really value, and that technical persona, I think, tends to have a lot more of that.

Speaking our language, Austin. Distribution is definitely the moat. And I mean, that's your background too, right? You came from the investment side. You've got that first principles problem-solving perspective. So it makes sense that you also recognize that in others and new hires.

Yeah, definitely.

Are there signs perhaps that a team is trying to be technical but maybe missing the mark a bit?

I like this question. Yes. The goal of anything that we try to build is to keep it simple. Like even if we're doing something that's incredibly sophisticated and nuanced, we want that system to be really simple and scalable because it sets us up to be able to plug more people into that. It sets us up to be able to easily improve it. It sets us up to learn from what we're testing and iterating on. And I think sometimes in today's world, some folks have a tendency to try to over-engineer a solution to a problem that's actually pretty simple. And so sometimes we may try to make things technical or we may try to write things in code when they could be built in another system that makes them easier to use. And I think our bias is to keep things really simple because it means that it's gonna be just much more scalable. Ultimately, the reason why technical matters and being technical matters is that you can build a system that goes from point A to point B, and ideally it's as simple and as clean as possible to do that. If you start over-engineering things, all of a sudden you get a very complex system that's hard to maintain, hard to manage, and it's just gonna break more. And, um, that's ultimately not in service of what you're after. The way that we actually think about this is that the way that we avoid over-engineering solutions is that the folks on our growth team just own pipeline goals. And so there's a strong accountability around, I've gotta drive a number. My job isn't to build the most creative elaborate system ever. It's actually to drive this number. And yes, you're gonna have to write some code from that. You're gonna have to try some things that are outside the box. But the idea is that the thing that matters most is the impact rather than the actual solution that you're building.

I love it. That makes sense. Great answer. And you mentioned data and ingesting data for growth teams. You're also doing that at Unify overall. What are systems that you've seen work well to perhaps operationalize product-informed growth?

So product-informed growth takes a couple of different, I would say, like mediums. And one of the most well-understood ways has been This idea of taking product usage data, direct product usage data, and turning that into, into some sort of growth channel. So classic example is a PLG company that orchestrates an upsell. Let's say you've got Linear and Linear's got 25 users. They upsold their, you know, you added 26. That's sort of a signal that you should be reaching out. That works really well. Uh, not for Linear necessarily. I don't know about their operations, but I'm just saying it for a PLG company. You got another bucket, which is sort of a marry between first and third-party data. Maybe you've got a little bit of first-party data. And so whether it's website visitors or are maybe a free user signup and you've got some third-party data as well, which is like things like, hey, what technographics is this company using based off of going to their job descriptions and seeing what tools they reference as being important? Or it's people starting in new sort of like jobs within your ICP. So for us, when a new VP of marketing, as an example, starts, you've got this sort of other blend of, of data that has a little bit of product usage, but a little bit of third party. That's the world that Unify plays in a ton. Ideally, what you have is a system around these data points that makes it easy to understand the full user journey. And inherent in that is that you've got a bunch of different datasets that you need to join together. Selfishly, I'm going to show my own product here, but Unify does an incredible job at this. And that's actually a lot of the beauty of what we do is that we take these disparate datasets, we actually take a perspective on the joining and the transformation of them so they fit together in a way that makes a lot of sense. And so one of the most exciting experiences about Unify is you actually can drop somebody into the product, integrate a couple different data sources, and then they can see this incredibly clean timeline of all these activities that their customers have taken or things happening on the account. It could be a move champion, somebody coming to the technical docs to read about the product, somebody starting in a new role. And once you've got sort of that core data foundation, making, taking an action off of that is actually quite simple. And so historically, the way that companies were doing this 5 years ago was with modern data stack. So data warehouse, DBT and/or Coalesce, something like that. And then something like a high-touch or a census to pipe data into different tools. Fairly brittle system, also incredibly difficult to stand up. You usually need data engineering resources to do that really well. In this new world, you actually can just do all that out of the box with Unify, which is really powerful.

That is super powerful. Are you seeing any kind of broken datasets? I think that's a common ask we get from our founders is, you know, we don't know what kind of data we can pull in and we don't have reliability around those data sources.

Totally. Yeah, we, we actually spend a lot of time at the beginning of our customer engagements just thinking with them about the strategy. And so usually people come in with, the best teams will come in with a few ideas, you know, something they thought about, something they saw on LinkedIn, et cetera, but they haven't fully thought through the strategy and that this is even a strategy. And so usually we sit down with folks and for the first, call it like month of the engagement, help them think through the strategy. What are the signals that matter? What are the plays you might wanna run on top of that? Help understand and organize that. And then once you've got that, standing up the plays is actually relatively simple, but it is sort of that, that ideation. And we find that the best companies today are able to craft, call it, 25 to 50 different plays that they want to be running at any given time. And each of those is delivering a handful of meetings a month. You stack that up and you've got this incredible growth program. So it's cool to see that in practice. But yes, the data foundations definitely matter a ton.

So let's say you've got that data foundation, because that is a common ask we get from startups. Now that you've got it, what does that look like from an impact on outbound perspective?

So we believe really strongly that you've got two different ways of going to market off this data. One is a fully automated approach and one is a human-in-the-loop approach. Both really matter, and that synergistic equation between the two is really valuable. So, but you, what you want to do is drive the right message at the right time to the right person. Like, that needs to be true. That's the general framework that we operate off of. Brain in where you're gonna create the most impact because, you know, ultimately if you're, so if somebody, you know, as an example, shows up to your website browsing around on your pricing page, you take 2 weeks to reach out to them, they've probably already made their decision, right? Or they're in a conversation with somebody else at that point. So the timeliness really matters. So we end up spending, you know, a lot of time helping companies to build these systems that do one or that, that take those 2 approaches. The way that we start on the automated side is typically within Unify, we'll help companies build plays that go from data point to actually action. All that happens within Unify. We actually manage that full workflow for somebody. We actually typically are used to companies using a tiering model. So on the account side, usually what happens is somebody will have tier 1, tier 2, tier 3, tier 4 accounts, something along those lines. Tier 1 is really what they want their sellers to focus on. Anything beyond that, they're usually not covering super well. So what we'll do is we'll usually recommend an automated approach for tier 2 to tier 4, and then for anything that's in tier 1, usually what we'll do is tie in a human in the loop. And usually the best companies are actually intermixing LinkedIn messages, voice notes, dials, whatever it might be on the human side. And we find that both of those added together just create a ton of, a ton of impact. So it's usually the approach that we're finding the best companies are taking to actually act on the data.

Makes sense. And I mean, signal is a powerful, powerful word overall. You mentioned buying intent. Is that how you describe it really in the context of how humans buy? Because now we're talking a lot about signal-based selling, you know, signals overall. What does that actually mean to you, somebody that's deep in the space and an expert in the space?

Signal corresponds with that moment to reach out is like really what it means to me. And so, you know, it could be something that the buyer is doing. It could be something that's third party. You know, there's a lot of ways to read into it. What we'll see is, I would say one thing that is happening quite a bit today is that this idea of enrichment and signals are sort of being intermixed together. And I do think that they are quite important to break out. And so, enrichment. What do I mean by that? Some people are using Clay to build these sort of like bigger audiences or bigger lists of companies, their total addressable market, in a way that they can then go action against that. It's super, super valuable, right? I would say that building up that original list though is, is, is more on the enrichment side, right? It's more in this world of, hey, I've got a list of companies that I want to go after. Historically, companies would have done this in ZoomInfo or Apollo or something like that. Now it's happening in this product and you're able to get just much tighter enrichment, right? You're able to get these qualities that are coming from data sources that you historically wouldn't have been able to. Like, as an example, classifying a company as PLG or not. That wasn't a data point that something like Apollo or ZoomInfo could offer. When I think about signals, signals is then the actions and the behavioral data that's happening on top of that list of accounts that you care about to tell you when it's the right time to reach out. So, you know, we've got a laundry list of them and unify everything from, you know, user signs up for your product to someone's on the website browsing around to, You've got somebody who starts in a new job within one of your top tier accounts, and those are sort of like the right moments and nuggets to be engaging with that account. And usually the strategy that we're trying to— ideally you have a well enough understanding of signal and you've been able to build all the architecture to, to move data off of them that you can actually be engaging with accounts, call it like once a quarter off of a signal. That's best in class. We see is you've been able to operationalize the strategy enough that when those data points come up, you're able to move really quickly.

What are your thoughts overall on signal accessibility? Because of course, the question becomes, if everyone starts to be able to connect with the same signals, then it doesn't break out from the noise. It becomes noise eventually, and maybe it won't. But what happens then? How do we make sure that not everybody is prying on the same signal?

If the signals are good, I think it doesn't matter as much. I guess my point being that if you get the timing right, somebody who's in market is going to— it's in their best interest to take a quick look at what you're doing. And I find that really the thing that stands out is relevance more than anything. If you can show up to a conversation and just show you've done your research and show that you know the customer, you know their market, you understand their problems and their pain points, we find that that is actually a much better way to be standing out than to be reaching out and saying like, like, hey, so-and-so, like, I noticed you started a new job. Like, in 2025, that's just not actually differentiating anymore. But being able to use that as the, the reason why you're reaching out, and then to be able to show sort of what you know and build really strong relevance, it goes a really long ways. And so I think in that world, like, hopefully we start pushing down is this idea of, uh, of relevance being super important. I'll give you an example. It's like, I bought a product from Carta like a year ago off a cold outbound email from them. I had a to-do list item on my notepad of I needed to go out and find some sort of solution to employees understanding equity compensation. It's a huge problem at startups. Just generally speaking, folks ask a lot of questions and there's a lot left to be understood that's not well-researched. Carta has a, has a product around that. They had reached out to me and it was just like the timing was perfect. The messaging was like very clear. I understood what the offering was and how it fit in and bought the product within like 6 hours. And so I think in an ideal world, like that's sort of the model we move to is that like you get the timing piece right, you actually can make a ton of decisions really quickly because you're getting relevant research there.

I think that almost flips it on its head where a lot of people say, oh, we don't like being sold to. But the reality is we like being helped. We like our lives easier. And when it does that and when the resonance is there, when the message is great, it is helpful. It's actually saving you something on your to-do list.

Yep, definitely.

I remember back a little while I was running an outbound program and there was no difference in terms of performance between the automated version and a personalized version based on relevance. So when somebody identified it as a key relevant priority, no difference in terms of performance, which was shocking at the time. This is going years back before a lot of the sophistication that we have now.

Buyers just want to solve problems. And if you can prove that you're the best person to solve that problem for them, like, they'll, they'll take you up on that.

Definitely. And what was broken about outbound when you decided to build Unify?

Unify?

Was there a moment that tipped it for you?

So I had spent quite a few years around this world of like outbound and outbound automation and so was well aware of just like the problem space. I think the biggest thing was that when we started Unify in early 2023, we saw just this huge crescendo of the old way of the world no longer working, right? The world was sort of recovering from this VC hangover of 2021 and 2022 where the market had gotten had gotten really exorbitant and things were really easy to sell. All of a sudden we're course correcting in 2023. It was impossibly hard to sell. Growth rates across the board in public companies are slowing down. Go-to-market efficiency is, is just going through the floor. And I think the thing that really just stopped working at that time is just you couldn't spray and pray anymore. This had sort of been building up over the last 5 to 10 years. You know, we saw the rise of the sales engagement platforms like Outreach and Salesloft, etc., sort of create this idea of cadences become really popular and then they continue to proliferate. But I think what we saw is just that strategy just stopped working. And come 2023, companies were looking for a new way of going about things. They were looking for a new way. Outbound still mattered. Controlling your own destiny still really mattered. But we needed a new strategy and we just believed really deeply in this idea of signal-based selling and using these, these insights at the right time to actually break through the noise. And so I think it was just— what's the saying? Like, it's like success is like the combination of like luck and like timing and something like that. Anyways, I think it was a combination of those things that we understood the space really well. You know, we had actually been practitioners in it before and I had built in the space and my co-founder Connor had well understood the technology that would actually power all of it. And that plus like this compelling event of the world falling apart and outbound no longer working in 2023 sort of was the catalyst to really leaning into the company.

I love it. That makes sense. And success equals hard work, luck, and timing, I think is at least one variation. I think there's a bunch of variations.

That's it. That's what I was looking for.

And what about today? So if you're building or you're advising another founder who's building outbound from scratch today, where do you start? You know, tech, message, people. What does that foundational step look like?

Yeah, so I guess I can offer two pieces on this. So one is that I think for most companies, like the best way to get started with outbound, unless you have this sort of DNA in your team already, is to find a great agency partner to work with and to lean into that in the early days. So test messaging, test tactics, et cetera. You know, if, if it's helpful for folks, I'm happy to, happy to recommend, but I, I've got a ton of folks in our, on our network that have just been proven to get companies to success on outbound and I think the reason why I suggest that approach is that there's so many pieces that go into getting it right, which is why it's so hard, right? You've gotta get the technical things right. You've gotta get email deliverability right. You've gotta get the systems talking together. You've gotta get the messaging, you've gotta get the signals, you've gotta get the CRM pieces, and you gotta get the copy as well. And all those things take a lot of nuance. I think in particular we find that 9 outta 10 companies we work with are, they really struggle with copywriting, uh, and doing so in an outbound context. It's incredibly different to pitch someone cold over email versus to be writing copy for your website. And it shows up in how people, you know, tend to, tend to approach outbound. So I think agency partners can be a really good way of just going from 0 to 1, getting some of those learnings out of the way, and then you can always in-house or double down on that with your own team. For us at Unify, what we found was that, uh, so I had quite a bit of background in outbound and then our first AE, Skyler, also had a ton of background in outbound business development. And so, We sat down at a coffee shop one day and just like hashed out different ideas for— the thing that really broke through the noise for us at Unify was this is before we had even 10 customers. We just sat down and got incredibly specific about the types of people that we wanted to go after. So we're on LinkedIn using LinkedIn Sales Navigator, building these lists of 100 to 200 people that not only had the job titles we cared about, but had like specific keywords in their profiles. That signaled that they cared about the problem space that, that we do, whether it was tooling they use or their goals. As an example, like we would look for marketers that would mention like that they had owned outbound, which is like sort of a weird microcosm, and use that as the reason why we would reach out. We would send them a super personalized message, etc. And we actually found that in doing that, we were able to one, hone our product market fit because we really understood precisely who and with what message would respond to us well, even with very little credibility. And two, we just started putting meetings on the board. We would see something like a 15 to 20% response rate to the message we were sending out. And nobody was telling us to like, no one was like unsubscribe. It was like people were always engaging in the conversation, which I think was a good indicator to us that that was working. So you can take it a couple of ways if you want to go agency versus taking it in-house.

Mm-hmm. I'm sure you got faster feedback loops too, just from responses to outbound and then the calls. Itself that were generated.

Yeah, exactly.

So what if a company wants to and believes in signal-based selling? When are they mature enough? Like when you're recommending that, at what point does it make sense for them to be ingesting all these signals?

I do think in general, once you've gotten to the place of you've got tens of customers, you're probably at a place where you can start leveraging signals. The first one that I'll usually recommend folks get started with is new hires, which is one that we offer. So Hey, new VP of marketing starts in this category. The reason why is that that audience is just always refreshing for you and it's actually not dependent upon your company and your brand awareness. There are a lot of other signals like website intent data that are very dependent upon brand awareness, but this one's not. So usually I'll recommend starting there because you can start to get— that was actually the first signal that we ran at Unify as an automated approach was we would see these people would start a new job, so we would automate the reach out with a message around that. And that worked for us. Book 5 to 10 meetings a month, which was a great strategy. At a certain point you'll hit a tipping point, probably around like Series A, Series B, where you actually have enough critical mass around your brand where you've got, call it like 5,000 to 10,000 monthly website visitors. You've got tens of customers, maybe you have a PLG motion and people are sort of signing up self-service at this point. And at that point it's important to start building the foundations for signal-based selling because it helps you to amplify and double down on the things that you've already gotten to work, which is exactly what you're just going to find a lot of wins in there.

Austin, what parts of your growth experience from the past or investor experience have been most valuable in building Unify?

I think it's just a little bit of this. I think the biggest thing is just like the humility to fail over and over again and like the willingness to, to do that. So in growth, something that I always tell our team is that you're going to fail 2 out of 3 times and like, that's actually good. Like, that's a good hit rate. It's sort of like baseball in that way. And I think there's just an element of you open up a risk-taking mentality when you do that and you have that sort of right DNA where you're not afraid to fail because that is actually the default and what's going to happen more often than not. I do think you need to have that sort of— you need to have that sort of DNA on the team, on a growth team. And, you know, it applies to a bunch of different places. It applies to being a founder. It applies to taking big swings on ideas and building product and all these ideas, but it started for me in growth and I think that's been really powerful. And I think it actually for us on the growth team today, it opens us up to find new opportunities because we're willing to take the risk. We do so in a calculated way, but we are exploring the problem space and we will find wins because we know how to execute through it. So I think that's personally the thing that sort of crescendos into a bunch of other areas of the work that I do.

Yeah, I love the baseball analogy. One of my favorite things is around just how Babe Ruth led the league in home runs but also in strikeouts. So it kind of gives that perspective.

I love that.

What does it feel like to really lead an organization where failure is the norm and where it's encouraged?

For me, it feels really natural. I feel like I'm generally like a more truth-seeking person. I want to get to the right answer rather than it being like, you know, whose perspective wins or whatnot. I think ultimately I'm just looking for the right answer. I think culturally though, if you're going to do that, you have to set a few things up. You've got to just like get in the mode of like culturally celebrating losses as learnings. You know, I think that's something that matters a lot. And, and I think this shows up in a bunch of different ways. Like, as an example, on the growth team, we'll do, uh, you know, a monthly retro where we talk through ideas that didn't work. We also though hold ourselves accountable for tracking all of our experiments. So the ones that don't work, we can like sometimes have a variation of that that will work, and we sort of write that out and we're clear about it. I definitely think it's not for everybody, you know, it's, uh, it definitely sucks to, to lose sometimes. But I think if you can build that right DNA and build that right culture and build the right amount of safe space to experiment. And the thing I used to always tell the growth team at Ramp was, hey, the only time that we are, the only time that we are losing is if we are not learning from the experiment. If it works, great, fantastic, that's a win. If it doesn't work, but we learn something from that, that's also a win. What we can't do is not hold ourselves accountable for finding the learning. And I think ultimately, same thing at Unify, that matters a lot. But, uh, I like it. I thrive in this environment.

I love that. I love that. I don't think she actually started the saying, but she's who I first heard it from. But Payal Kadakia, the founder of ClassPass, in her book LifePass, said failure is just a data point. And so like you said, if you're not getting that data point from it, that's actually the failure. Otherwise it's just data at the end of the day.

Totally.

And what did you really have to unlearn when you became a founder? After building these teams of scale, especially at Rent?

I think the frameworks still really apply. You know, I would say like how to operate, how to grow a growth team, all that sort of stuff is very— is something that I definitely learned from. And I think like another thing that I really learned was you got to— it's all hard work. You know, I think outside in, it always looks up into the right, but like under the hood, it's a lot of hard work that's going into these things. And I think you can't discount that. In terms of unlearning, it's really important to, I think, just be open-minded, especially the first time. For me, when I was stepping into starting Unify, first-time founder, learning all these experiences for the first time, I think it's, it's important to be open-minded about that and to not write things off ahead of time. I also think the biggest thing that I've had to do is at an earlier stage, you just have to take big swings. And I joined Ramp at 40 people. When you join a company that has some reasonable amount of scale, you start getting into more of a mindset of like you're taking less, you're not taking quite as big big of swings oftentimes. Usually it's like, hey, how do we build the machine 25%, 50% better? Not how do we think about this crazy outside-the-box idea that's going to take us to the next level? I think one of the biggest things that I've had to reset is that I have to think about that often. What is like the step-change function that we're missing out on? What is the thing that could really, really work that doesn't today? And I think that's just something you just gotta— I, I've become a lot more accustomed to practicing over the past 2 years, and it's now a ton of fun for me. But I don't think it was as natural when I first got going in the company, besides the idea of like, hey, we're going to go start a new company and go raise money and all that stuff.

Yeah, makes sense. Makes sense. I'm really interested to hear your perspective around this. Of course, AI. What are your thoughts on AI's impact on growth teams? Do early-stage companies still need growth teams, or is it shifting towards great AI agents?

They definitely do. I think there's just like a lot of slop that AI can create. AI leveraged in the right way is obviously fantastic. I use it so many times every day to do my just general day job. But I do think that there is a ton of things that AI can't do. AI cannot be a very good strategist today. I think that will evolve over time and you'll start to have humans assisted on the strategy side by AI. But I don't think we're quite there yet. So I do think having a great growth team is really important. One of the things that AI has really opened up in growth teams is just like the ability to marry growth and sales teams in a way that wasn't possible before. I think one of the things that we're seeing the best teams doing today is bringing together how their growth team works with their BDR team as an example, and having that be a system that works together rather than a sort of siloed, disjointed set of teams that didn't get along super well. Like I think we started Unify, one of the things that was really common that we would hear is marketers would be like, yeah, I'll give my list of prospects to the BDR team and they'll just never follow up. And it's super painful. And I think what we found is that AI has actually been a really good bridge for that. And our own product is becoming more and more that surface area. But the answer was a lot of times like, yeah, like BDRs oftentimes have really great prospects and folks they want to go after. If you make their lives easier using AI, then sure, they're going to lean into it more. But if you just try to give them more work, they won't. I think in that context, we're finding that the best teams are able to use AI to sort of like grease the wheels and make sure that, hey, we're not asking teams to do work that they could actually be— we could be automating. And so that's what I think really exciting.

If I push you a bit further on that, what does that look like tactically? You mentioned in the product, it could be in your product, it could be external to your product, but what does that actual blockstep look like?

For a growth team, AI is a way to automate actions and it's a way to scale creativity, right? So, you know, you run a plan in Unify. Historically, we had to run this manual job. We can automate all the prospecting work to 10,000 accounts in a matter of minutes. That, that wasn't possible before. But let's say you've got, you know, 1,000 in there that you think are really worth doubling down on and having humans dial into them, all that stuff. What that looks like in practice in Unify is that you actually can pass those prospects off to a BDR. You can share with them all of the research that you've done of why this is somebody that's interesting to go after. Hey, this is a tier 1 account. Hey, you know, I noticed they're using XYZ competitors. I know that because they mentioned them on these job postings that are no longer up. I saw that they hired XYZ people really recently. I think it's about just sharing that context, right? In a, in the, like the right accessible way where that person doesn't have to go look for it. And you do that, somebody's gonna take the action then because they've got all this research. It's all powered by AI behind the scenes. Maybe in the future you can ask questions and say like, hey, like, okay, but I want to know about this as well. Like, what is this data point? And we actually can pull that up. So I think it's just a matter of simplifying the workflow, right? Historically, what you would've seen is that we saw this when we were building the first version of Beautify's product. We would literally see BDRs open 10 tabs and like, go do this research of like going to everyone's website over and over again. and we're just distilling that into like one screen where somebody can just act very quickly and have all the data that they need.

The browser tabs are being minimized. That is always a plus.

Yes.

And what about AI's impact on other areas? Are there workflows that you've automated at Unify that have materially improved your own go-to-market outcomes or that you're seeing customers do?

Two of the areas that we're seeing a lot of impact on today, well, three. One is using our own product. We actually power, depending on the quarter, anywhere between, call it like 15 to 30% of our new business from our own product. And so, wow, that's obviously been a huge application and a huge growth accelerant for us. We also see that I think two areas, customer support is a big one and then just insights from sales calls. So customer support, we use Pylon. And I think this idea of your support team shouldn't need to scale linearly with your company anymore is how we think about it historically, right? When you grew your company, you needed to hire more support reps because you had more tickets. But today, one of the key North Star metrics that we track internally is is what percent of support tickets are we fully automating with an AI agent? And we want that number to continuously be going up and to the right. To do that, we have to be building new articles, new knowledge base pieces that AI can learn from and answer from. But if we continue to do that well, we're seeing that that's paying a ton of dividends in terms of us just being able to deliver a better customer experience for the users of our product. So that's been a huge win. Another area is this idea of learning from sales calls. You know, just like like we use Attention for that personally, for call recording, which is another great product. And, you know, we are, you know, just last night I was poking around and I'm trying to understand like competitive analysis on us versus how are people talking about our competitors. Is it on pricing? Is it on feature functionality? What are the things they care about? Historically, right, you would have had to literally go parse through, I don't know, hundreds of calls and just like try to like Ctrl+F and look for the right write, you know, verbiage, et cetera. But I actually now can just run that analysis in a matter of minutes and understand exactly who's saying what. I can dive into the insights and the anecdotes that people are mentioning, and I'm excited about how that's gonna feed into our product roadmap. I'm excited about how that's gonna feed into how we better objection handle against certain things that competitors bring up. So lots, lots more to come there, but I think it really is transforming. It makes us just ask, hey, like, is there a better way of doing this? That any, at every step of the journey, which is a lot of fun.

Very cool. And what are you most excited about looking forward if you pull out your magic ball around operationalizing and supporting your go-to-market with AI that maybe we're not at yet, but you anticipate we're getting to?

We're at the precipice of an unlock where this idea of relevance, right, is at the fingertips of AI. Like we historically haven't been there because you need, you need this unique combination of data, data and the workflow layer around that data to be able to create that at scale. And I think we're just a couple quarters away from being able to do that with our own product at Unify, where we actually can just send hyper-relevant messages to the right people at the right time. You know, we have, we definitely have strong messaging capabilities, but there's a level of like being able to mimic humans, understand their intonation on a personal level, being able to pull in you know, just-in-time research on whatever data point you cared about. And I think that unlock is gonna be really big because I think we're gonna get to a point very soon where spotting AI slop today in your inbox is pretty easy, but I don't think it's gonna be as easy with a lot of great products in the next like 6 to 12 months. So that'll be something that'll be a, a really interesting unlock.

Very. Yeah, we're definitely in the transitional phase right now where some are being done incredibly well and, and some quite the opposite. So it'll be interesting to see that more at mass. What is on the product roadmap, if you can share, if you're open to sharing anything coming up down the pipe for Unify?

Yeah, we've been doing a ton of work for BDRs, so lots more to share there very soon. But one thing that's been a very common thread over the past year and change is that, you know, our product was originally built for growth teams, right? Which is a very unorthodox way of thinking about outbound, but we found that growth teams, the experimentation, the idea of using plays kind of like campaigns really resonated. But something that was always missing was, hey, great, like, but I've got this, you know, really high-value prospect. I want to pass that off to my sales team. How do I do that? Historically, that was something that we didn't do. We would pass it off to, you know, we'd create a task in Salesforce or something like that. But it was pretty, pretty rudimentary. Coming soon, we have a big announcement around our work for BDRs and in particular, how they work really closely with somebody we call to be like the outbound quarterback who's sort of our main champion today. And we're using this product internally today and it's been mind-blowing. So excited to share with the world.

All right. We'll keep our eyes out. And Austin, last two questions, always the same. What is one tactic or strategy that's working for you today? And I know we've talked about a bunch, so maybe if there's one you haven't touched on yet.

One tactic that's working for me today, we I've just got it. It's too good to stop to hammer home that you've got to own, I think, social today in this world. It's a really important medium. And, you know, I think you all obviously do this incredibly well at the GTM Fund. But for us at Unify, just like the level of credibility that we've gotten from LinkedIn in particular and just like being prolific writers about our space, I couldn't overstate how valuable that's been to us. And so even though we've talked about it already, I do think it's important to just like reinforce is like For us, LinkedIn is where our audience lives. It's where we reach them. And that's been really powerful for us. You know, I think most of the companies that are taking off today have some form of social that they really own, whether it's LinkedIn or X or Discord or whatever it might be. Just know where your audience is and start practicing reaching them because it's just been, it's been so powerful for us.

That's a great one to hammer home and a really good kind of note there just around your audience lives in different places, so figure out where they are and then show up in those places. What about if we flip that on its head a little bit? What's one widely held belief that revenue leaders or founders have today that you think is bullshit or no longer serving us?

I just think this idea of— I think the role of the marketer and the growth person is really changing. And I think no one's quite put their finger on this yet. But I do think that this era of of hire a traditional marketer to go do a small segment of the marketing function is slowly, it's not working. We see it happen time and time again with our customers. But I do think you're going to see this rise of, call it a growth engineer, call it like just like a growth operator, call it like a growth manager. This idea of somebody who owns a pipeline goal, who is more technical, who is more systems-oriented, who can think from first principles and problem-solve. I do think we're going to see a lot more of the rise of that person as sort of the go-to marketer going forward and the go-to person who owns pipeline goals. And people are starting to put their finger on this, but I still think it's quite early. And just time and time again, we find that the best folks are the ones who are able to take product-like principles and apply them to growth. And so that's what I'm, I'm really bullish on.

I love it. What a fantastic way to end off. And Austin, where can people find you? I know we talked about LinkedIn, so I'm going to to throw out LinkedIn already, but where should people find you?

Find me on LinkedIn, definitely. That's a great spot. And then you can also find me on X or Twitter at Austin Hughes with 3 underscores after that.

Beautiful. We will link to those in the show notes. Austin, thank you so much for the time, for the insight. Excited for that launch in a couple weeks. We're going to keep our eyes out. And to the audience, thank you for hanging with us.

Thanks for having me, Sophie.

You bet. All right, catch everyone next week.