Omer (00:09.760)
Welcome to another episode of the SaaS Podcast.
I'm your host, Omer Khan and this is a show where I interview proven founders and industry experts who share their stories, strategies and insights to help you build, launch and grow your SaaS business.
In this episode, I took to Nate Sanders, the co founder and CEO of Artifact and an AI powered SaaS that analyzes customer data to uncover growth opportunities.
While working at pluralsight, Nate experienced firsthand the frustrating and manual process of synthesizing customer research data across the company's different departments.
That experience got him thinking that there had to be a better way.
In 2019, Nathan and his co founders set out to build Artifact using large language models to automate these painful tasks.
But developing an AI product presents unique challenges.
One of the biggest obstacles they initially faced was not having the necessary data to train their machine learning models, which was critical to building this product.
They also discovered that the champion Persona on whom they depended to advocate for Artifact differed significantly across various companies.
This meant they couldn't rely on a single buyer profile and instead had to figure out how to customize their sales approach.
Despite those challenges, the founders believe they found product market fit.
Today, Artifact is doing over $1 million in ARR and they've raised just $7 million in funding.
In this episode, you'll learn the tactic Nate used to get valuable training data from early design partners for building Artifact's AI models.
How scaling up too early caused problems and almost sank Artifact due to disappointing results and poor unit economics.
How the founders tested different growth channels and how they eventually found what worked to reach their ideal customers.
We also talk about why Nate believes making high quality decisions is more important than making quick decisions.
And the subtle signs Nate watches that show a prospect is truly interested and ready to become a customer.
So I hope you enjoy it.
Nate, welcome to the show.
Nate Sanders (02:14.910)
Thank you.
Glad to be here.
Omer (02:16.270)
Do you have a favorite quote, something that inspires or motivates you that you can share with us?
Nate Sanders (02:20.350)
Yeah, I definitely do.
All models are wrong, but some are useful.
Omer (02:24.750)
I have never heard that before.
It's kind of making me think a little.
Nate Sanders (02:27.870)
Yeah, it's a good one.
I think it's been very representative of my founder journey and how I've had to be able to step through a lot of learnings and the fact that you get a lot of, I would say, whiplash from mentors, investors, customers, whatever it might be.
And everyone has these mental models and frankly, none of them are the territory.
The map is never the territory.
But some can be really useful to be Able to move fast and make good decisions.
Omer (02:59.070)
Nice, I like that.
So tell us about Artifact.
What does the product do, who's it for and what's the main problem you're helping to solve?
Nate Sanders (03:06.350)
So artifact is predictive cx.
So we use AI to be able to analyze everything your customers say, everything they do, and to be able to use that information to help you get actionable insights so you understand what's going to drive growth and customer loyalty.
We primarily serve right now product teams, but frankly our customer inside of the organization is whoever is most passionate about turning voice of customer insights into actionable insights that can drive customer growth across the business.
And that's usually right now.
Product teams, we see CX strategy, customer success insights teams and give us a
Omer (03:45.510)
sense of the size of the business.
Where are you in terms of revenue, size of team?
Nate Sanders (03:49.510)
Yeah, so seven figure business we're chasing towards our next big Milestone would be 2 million in ARR.
And, and we're a team of 16.
Expect to be probably 20 by the end of the year.
And yeah, we've been around since kind of the late part of 2019.
Omer (04:07.380)
And you've raised about seven and a half million dollars to date.
Nate Sanders (04:11.339)
Yeah, that's right.
So seven and a half million, you know, about a million and a half pre seed round and then a $5 million seed round that we oversubscribed.
Josh Buckley from Buckley Ventures led.
Omer (04:24.800)
So your background is in product and ux and I noticed you were working at pluralsight for a while.
I used to love using pluralsight.
Great product.
So tell me, where did the, what were you doing at the time and like where did the idea come from?
Nate Sanders (04:45.120)
Yeah, the idea came when I was at pluralsight and like you said, like my background's in product, product design, product leadership.
And I think if you ask me like what I really resonate with and who I am as a person, from a job title perspective, it would probably be designer.
And every shop that I've ever been at, every business I've been a part of, mostly early stage and growth stage startups, I've gone through this process of operationalizing qualitative and quantitative data to be able to find product market fit.
So the pain point was observed from my experience as a practitioner of the fact that I was spending 70 to 85% of my time interfacing with other departments.
So success, support sales, doing my own primary research and it elicits a lot of data that's pretty difficult to synthesize, but a gold mine to be able to find product market Fit and just drive growth across the business.
Omer (05:39.850)
Okay, so what was the spark of the idea?
Nate Sanders (05:43.850)
When I was at pluralsight, I was tasked, first of all, amazing product organization.
I joined the team because two of my longest mentors, Nate Walkinshaw and Gilbert Lee, were leading the product team there and convinced me to be able to come over and join this wonderful product team that they had put together.
And we were doing thousands of customer interviews a year across the entire product team.
There wasn't a lot of synthesis happening.
There was some.
And we were using everything that was being said by the customer every day to be able to drive product decisions.
But I was asked with one of my colleagues, Mike Baird, to be able to actually go and try and operationalize synthesis across a few different teams that were, you know, running different product research efforts and things like that.
And so we did almost half a dozen of those activities where a lot of the folks listening probably have been in activities like this where after two days you end up with a glass conference room with thousands of sticky notes over the wall that are in like affinity maps and they're expensive.
You know, it's like 10 people that are paid $150,000 plus to be able to spend two days doing these activities.
And the natural course of events was, man, I wonder if we could use machine learning to be able to solve these problems.
And at the time, the language models and the NLP techniques were quite naive and simple and didn't really produce super actionable results.
But then in 2018, at the tail end of my time there, the first large language model, Bert came out and we ran some internal experiments using these kind of fill mask NLP activities or techniques and saw some really promising results and thought to myself, I think it's time and that the technology has come far enough that we could probably start a business around this.
Omer (07:39.640)
Okay, great.
So you've got, you've seen a opportunity here.
What did you do next?
Like, once you've had the idea and you're like, okay, well, this, we've got this business, we can go and build.
Did you, did you quit your job that afternoon or did, did it take, you know, some time for you to figure out or lay some groundwork?
Like, what did you do next to.
To get to the point where you could finally make the leap?
Nate Sanders (08:08.010)
Yeah, me and my, one of my two co founders, Trey, before our third co founder joined us, his name is Caleb, but Trey and I spent our evenings for about six or seven months tinkering.
So we, we'd build little prototypes in react and just put them in front of friends or colleagues just to be able to get early feedback.
And we're in a sense just trying to try as many things as we possibly could before we were, I would say, obligated to the life cycle of a business.
And yeah, so we tried, you know, probably half a dozen different little prototypes and directions and product ideas and finally started to be able to see some interest around one of those particular areas, which at the time we were actually focused on data gathering and the synthesis.
And so we raised a small angel round from friends and family here in the Salt Lake City area to be able to actually go full time.
So it ended up being almost seven months from the first time that we started exploring things in code to when we actually went full time as a company.
So lots of nights and weekends, lots of fed up spouses, things like that, before we, we actually went full time.
Omer (09:22.510)
So when you went full time, what kind of Runway did you have and were you generating any revenue at that point?
Nate Sanders (09:31.260)
We had an initial feature, not a product.
It was like as slim as it could be.
And we had a couple of really promising conversations that we thought could lead to revenue.
So no, I would say significant traction before we went full time.
And the Runway aspect it gave us, with the capital that we had raised and how we kind of penny pinched and reduced our salaries, et cetera, about 18 months to be able to figure out what is the play here.
Is there an opportunity for us to even raise venture capital that we can use to be able to build a real big business here?
So we explored quite a bit for about six or seven months, pivoted a couple of times in that direction before we landed on what Artifact is as a business today and the product that we've been building since early 2020.
Omer (10:21.510)
The thing about artifact is that, I mean, obviously everyone is talking about AI today.
You guys have been obviously doing it for a little bit longer than three months since whatever it was since ChatGPT came out.
It strikes me as that in order for the product to work, and you mentioned machine learning earlier, that you need to have a lot of training data and a bunch of stuff to basically make that the AI effective.
How did you overcome that problem on day one when you didn't have any of that?
Nate Sanders (10:59.830)
Yeah, it was actually pretty painful.
So the interesting part with these AI native SaaS startups, and I think there's degrees of how true this is, there are folks that can certainly consume these models via endpoints and get a lot of value that way.
For us, fine Tuning and even pre training in some ways was pivotal to actually creating a product that we could satisfy what our customers were hoping to accomplish.
So the first big problem was this kind of cold start aspect of in order to fine tune, you need training data.
In order to be able to get training data, you have to be able to have customers.
You don't have any customers yet.
So how do you actually get to that point where you can actually get this training data to be able to fine tune these models?
So essentially we had to take a design partner route where we ended up reaching out to almost a dozen different organizations that we felt were very forward thinking about this problem.
Most of them were in our network.
Folks that we had either worked with, worked for, knew, were passionate about solving a problem like this, networked into them, called them, emailed them, whatever it might be, and essentially said, look, if you will give us the data, we will let you use the product for free as it's productionized and gets to a general release for a certain amount of time.
And if what we're showing you in these prototypes, these clickable prototypes, if this is real and we can create this and you can give us the data to be able to do so, all we're asking for is your feedback, your data and your time.
And we did letters of intent that had, you know, paid deposits, about a thousand bucks to 1500 bucks, something that would disappear on a corporate card.
It was just enough to be able to tell us this is real.
People aren't just doing us a favor.
They obviously are a little bit, because, you know, they're networked into us and we're asking for a favor, but we just wanted to see that they were willing to put a little bit of skin in the game and then participate with the data.
So that's, that's how we started to overcome this problem.
And then we ended up with three, and then at the latter end, four design partners that we were just collaborating with weekly, getting feedback, trying lots of different things in the products, and fine tuning these models as often as we could.
Omer (13:26.950)
So before you started going out and trying to effectively pre sell the product and get some training data, you mentioned that you and Trey, over six, seven months, were building prototypes and react and whatever.
Were you also going out and trying to validate the idea with potential customers back then?
Nate Sanders (13:47.890)
Yeah, yeah, we definitely were.
I think we more than I like to today.
As a product person.
I had learned quite a bit of the really great practices that come from human centered design.
So we would do voice of customer interviews Without a prototype, without a product in front of them, we were just trying to understand the problems, what people needed, how they worked, anything that would help us understand recall versus idealistic kind of scenarios like how do you actually do this?
What does the work actually look like?
And so we spent quite a bit of time just interviewing and understanding.
And then we would do little design studios, little design sprints where we'd put together prototypes as fast as we possibly could.
And then we would present those Figma prototypes to our potential target customer and get their perspectives and watch them use it, watch them suffer, not know where to click all those good things before we ever started building.
Omer (14:47.830)
So I want to talk about the pre sell piece of this and getting the data in those early days.
If you can pre sell the product, that is such a powerful signal that you're onto something.
You don't have a product, you're just showing people, you know, slides or some kind of clickable prototype, and even on the basis of that, they're willing to get a credit card out and give you some money.
You were doing more than that.
You were saying, hey, can you give us the data?
Because we really need it and it would help us out.
And by the way, we're going to charge you for that as well.
So it was like a double whammy.
When you, when you look back at that experience, what do you think were some of the things that you got right?
Think about maybe a founder today who's in the same place where you were back then.
What are some tips you could give them to say if you want to go out and pre sell this thing, or maybe have more of an ask as an example of the data.
These are the kind of things you really need to do to be able to get those people on board, bought into your vision, feeling like, you know, you guys are the right people, credibility, whatever.
Right.
So what would be some of those, those tips that you might, you could share?
Nate Sanders (16:11.520)
It's a great question.
I think the first thing that we weren't afraid of was this almost binary aspect of like you either get it or you don't.
And I think some founders get locked into the spectrum of is this product correct and how right is it or how correct is it?
And trying to move the needle left or right throughout that entire early stage process.
And the fact of the matter is that you need to try a lot of things very fast.
My team, I presume, is very sick of hearing me say that startups don't get outperformed, they get out decided essentially like Someone is just going to try more things and make more decisions than you do in a faster amount of time.
So I think that's one aspect that we were very unafraid of is the fact that let's find folks that resonate with what we're trying to do.
They get the pain point, they get what we're trying to do and let's see if it's real.
If it's real, they're going to give us money.
And if they give us money, then they're going to spend time with us.
And if they spend time with us, inevitably the product's going to get better.
So we were just very unafraid of that.
I think that initial classification of the folks that we were working with and I think that proved to be quite effective as we worked through our early design partners.
The second part, man, I would say finding organizations that think the way that you think as well, not just like a specific user or customer, but that they also have the ethos.
And I think what that helped us do is that when it came time to ask for budget outside of this credit card expenditure, it wasn't like this was a foreign entity to the rest of the organization that they just couldn't lift or persuade anyone else to do.
So I think thinking about what's happening around the champion that you're working with from this kind of pre sale aspect, are they going to be able to sell it to the rest of the organization?
I think that ended up being very important to us and the nature of the product that we had.
And then, yeah, I would say the last thing that came to my mind as you're going through that is we had a very strict, almost demanding ask of the amount of time that we needed from these early pre sale customers.
We told them that we needed to spend at least 30 minutes a week with them and that proved to be one of the most valuable things that we've ever done.
I'm sincerely grateful that we pushed on that, even though it was an uncomfortable ask.
Omer (18:45.820)
And that on itself is a great signal in lieu of not, you know, asking a customer to pay, that if they're willing to commit, not, not just in an abstract sense will you use the product and try it out and give feedback, but will you actually commit to meeting with us for 30 weeks, 30 minutes every week or, or whatever that, that again, I think is a, is a powerful signal that there's something there.
This is a problem that they care enough about to be able to say, I'll take some time out of My schedule to do this absolutely is.
Nate Sanders (19:21.090)
Yeah.
Omer (19:21.810)
So I think what you described there in terms of finding those right types of potential customers makes total sense.
The challenge is how do you find those people?
Right.
It's not like, oh yeah, we're looking for product teams at companies between 25 and 100 employees or whatever.
You're talking about some very specific behaviors that aren't necessarily easy to target and find.
So how did you overcome that?
Nate Sanders (19:50.190)
Yeah, you don't, you don't have the benefit of like firmographics to go target these companies.
It's like it's all about the jobs to be done of how they're thinking about it.
One of the biggest things was, was networking.
We had a lot of calls with a lot of folks that we trust and outside of job title and the easy things, it was just a lot of who, who do you know that's trying to solve this right now?
Do, do you know anyone that has mentioned this pain point?
Lots of questions like that.
Over a three or four week period, networking into dozens of different individuals that helped us get exposed to a lot of different ideas and priorities and things like that as fast as we could.
Omer (20:31.810)
I mean, I think that on itself is.
It sounds pretty obvious when you say it like that, but I think sometimes it's easy to just focus directly on how can I find those people.
Whereas if you take a step back and say, well, I know at least 20 people in my network who are in that space loosely, they might not be target customers.
But if each one of those people is bought into what we're doing and knows even five people each, that's a potential another 80 people who could be target customers that I can get connected to.
So that's a good way of thinking about it.
Let's talk about beyond the first year and figuring this out and getting those initial customers and let's kind of switch mode into getting to that first million in ARR.
Once you had started to pre sell this product, you're getting training data, you're getting people who are using the product and helping you make it better.
How did you find the next set of customers?
Nate Sanders (21:45.270)
Yeah, this process took longer and was much more difficult than I think I expected it to be.
A lot of my experience as a product practitioner throughout my career had been in bottoms up SaaS.
Our ability to create just a wonderful product that could create these nice network effects across, you know, lots of champions.
And what we found pretty quickly with Artifact is not only did the technology have to be right, the right person inside of the Right organization had to be also targeted.
And what I mean by that is one of the initial challenges that we encountered is we would find that the Persona, that valued artifact, wasn't always the same kind of like I mentioned at the beginning of the of the podcast, which is sometimes it was product, sometimes it was customer success, sometimes it was CX strategy, and other times it was an insights group.
Our ability to traverse and predict which group that was inside of an organization was hard in the sense that the first SDR was me.
And I didn't have the ability to spend every day, all day doing that outreach and trying to be able to put together sophisticated plays.
So that that first initial part was quite difficult of just even trying to be able to do direct outreach to be able to identify the right champion.
The second part, as I think about that journey that was difficult was we had an initial MVP that we released in the fall of it would have been 2021.
That was, you know, kind of post design partner.
We had already closed like our first $100,000 in ARR and from our design partners and we're starting to be able to commercialize it into that spring and summer outside of our group.
And as we started taking the product to market, we noticed that a lot of the assumptions and things that were true for those initial design partners obviously weren't true for the broader market.
So we then again had to go iterate as fast as we could around some of those high priority aspects of the product.
And then you get into this cycle of the fact that really product market fit is a system.
And the really difficult part of product market fit is that everything has to work in concert at the same time and you have to heat this huge lift.
It all has to work right.
And that at that same moment, and that was, that was very difficult for us.
You know, I, I'm not sure I'm really in a place where I would say we have product market fit yet.
And it might be just because I'm too hard on ourselves, but that is an enormous lift and it's hard.
And I admire founders that have the grit to be able to make it through that initial step for us, our ability to be able to try and learn from prospects existing customers what is working and what isn't as fast as we can and then turn that into a decision as fast as we possibly could has become a superpower for our team.
So one of the big values that we have inside the company is to be able to make high quality, good decisions as fast and frequently as possible.
And that's just become a huge part of our ethos, is just making really good decisions as fast as we possibly can, as frequently as we can.
And that's the only way I think we made it through some of those initial trudges of you have to make it through this.
I don't know what's right.
I need to go figure it out.
I need data now.
I need to act on this as fast as I can.
To be able to get a feedback loop in here is, as I would say, trite as that is for startup founders.
It's so difficult and it's hard to get right sometimes.
And I think that's what helped us through some of those initial stages.
Omer (25:30.090)
So you said making high quality decisions.
I think most people get the idea of making decisions quickly, but how do you know it's a high quality decision?
Did you.
Was this just about making decisions quickly and then getting better at seeing what worked, what didn't work?
Or was there more of a framework or a mental model talking about models that you were using to make these high quality decisions quickly?
Nate Sanders (26:05.770)
I am a really big fan of mental models in general.
I don't know if you've seen Farnham street in the wonderful blog they have on mental models.
That has influenced quite a bit of my product career, let alone my founder journey.
So I would say we talk a lot about mental models and we talk about its ability to be able to help us make faster decisions, things like that.
I think there's a couple things if I reduced it past mental models that have helped us, the first of which is the ability to understand and identify your known knowns and known unknowns.
If you remember Donald Rumsfeld's famous word soup that he had back in the weapons of mass destruction days.
But it's actually a great framework, it's called the Johari Window.
And it's about this ability for you to map out, like, what do you know right now?
What do you.
What do you know is true?
What do you know you don't know right now.
And it allows you to be able to frame all this information asymmetry and assumptions and everything else that you have to go tackle.
So every single product decision we make, every go to market decision we make, we end up having that conversation of like, what's the known knowns, what are the known unknowns?
And we go chase as fast as we possibly can the information that we need.
And then two would be diagnostic thinking.
So if we're going to make a decision, how will we know it was Effective, how will we know our ability to be able to measure it?
And then rather than throwing out the baby with the bath water, with each initiative taking the time to be able to think about, well, what didn't make that work, what went wrong and how can we improve it this next time around to be able to create a system that can actually get us to a result that we like.
Throughout my career I've used okrs quite a bit.
One of the maddening parts of my experience in organizations that use OKRs is they almost always wholesale change every quarter regardless of whether you achieve the initiative or not.
They almost always just wholesale get swapped out.
And if you think about the equivalent of that, there's no diagnostic thinking.
It would be like if a check engine light in a car came on, you'd throw the car out and go get a brand new car every single quarter.
It doesn't make any sense and it's maddening.
So we try and take a very diagnostic approach to when we make decisions.
The fact that every localized outcome is not going to be great, but if we keep making as high quality decisions as we can throughout this process, it's going to amortize out to these really great outcomes.
Omer (28:39.610)
Love it, Love it.
Thanks for sharing those.
I think those are going to have to look up that Johari framework.
It didn't work out too well for Rumsfeld, but the model still applies.
Yeah.
So let's talk about getting to that first million in ar.
I know you eventually figured out one growth channel that worked for you and is still working for you and you're doubling down on that.
But before we talk about that, tell me about a couple of the other channels that you tried and either they didn't work or they just failed miserably.
Nate Sanders (29:16.460)
Yeah.
I mean over the last few years it's no surprise that in the founder Zeitgeist there's been a lot of attention put on bottoms up SaaS and that go to market motion is so amazing.
It's wonderful.
It can create really formidable businesses.
It also has to be right for the product and for the company.
And I think one of the missteps that we had early on was we decided that we would try and take a bottoms up approach and then feather that very closely with kind of a high mid market commercial and enterprise segmentation.
And we try and bring this together.
So we made some initial product decisions, you know, self service onboarding, trying to be able to create some share based loops, things like that that could expand throughout the organization.
What we found is that it attracted the wrong type of champion.
People that can make these decisions on their own and can do them with immediacy of a self service signup usually don't have enough data or are at a large enough organization that they would get value from Artifact.
It's us, usually small and medium sized businesses that are, I would say, making up the long tail of, you know, bottoms up SaaS.
And we that just proved to be very quickly not an effective way for us to be able to target champions that got the most amount of value out of Artifact.
We saw a stark difference between churn and retention between folks that we had targeted in the enterprise and a direct outreach approach versus folks that had come through a bottoms up sales motion.
So that was one of the first learnings that we had.
The second was we looked at different channels.
So outside of the strategy, we tried a lot of different ways to be able to get in front of folks.
One of them was community, which I'm still very passionate about, just from a company perspective of building a community.
But as a go to market motion, what we found is, if you recall when I was talking about the fact that the champion can be different inside of our organization is the community.
Ability to be able to bring folks in is very commonly attached to the activities of a job description rather than an affinity for like what you're passionate about.
So Voice of Customer is not as effective to be able to build a community around as much as product management or design or community, you know, community building or, you know, customer success, things like that.
So we found that it was too difficult for us to be able to traverse a lot of the different Personas that end up being passionate about Artifact with community building as a primary channel at least.
Omer (32:00.740)
So were you building your own community or were you trying to kind of infiltrate other communities?
Nate Sanders (32:06.740)
The former primarily, and a little bit of the latter as well.
But we, we started by hosting these, these bi weekly events that we call craft notes where we'd bring in, you know, speakers from different Persona types.
And it was just difficult to be able to find, I would say, a steady drum beat on who we could target in an effective way there and actually kind of build this gravitational bubble around in the community strategy.
And then the other was like dinners.
We would go out to San Francisco monthly and we would have dinners with a cohort of folks and just found the same that we, we had to change the theme, the perspective of those events too much.
There was too much thrash to be able to build out a consistent community, to be able to target the Personas we needed.
Omer (32:55.570)
Okay, so that was community.
You tried events and conferences as well.
How did they go?
Nate Sanders (33:01.170)
Yeah, I love conferences for learning.
Events are actually a great place to be able to talk to as many people as you can.
For us, what we found is that you're too, especially as a startup, the ability for that channel to be effective is almost like three parts in the event runners hands and one part in yours.
And that asymmetry is not great.
For a startup where your budget is limited, you're spending a non trivial amount of money to go to a booth there.
So I think they can be effective when they're not a primary channel or at least for our business.
For some businesses they're incredibly successful, but for us, we just had to learn very quickly that it wasn't the right place to be able to put dollars.
And the ability to find the right person at that event was too random, stochastic, not, you know, going to be predictable at each event we went to.
And we found more success from the direct outreach aspect.
Omer (34:02.220)
Roughly how much do you think you invested in in conferences, events before you realized time to move on?
Nate Sanders (34:10.220)
Probably 30 to 40,000.
You know, between the actual booth setup, event fees, things like that, which is again not like a huge amount.
I know there are people that spend, you know, hundreds of thousands of bucks on ads before they figure out this isn't the right channel for us, but
Omer (34:26.460)
this is not the show for that
Nate Sanders (34:29.500)
for us.
You know, 30, 40,000 bucks is pretty close to the base salary of an SCR before OTE commissions.
So it was pretty hard for us to be able to see the difference in efficiency and effectiveness and think that that was how we were going to spend the rest of the six months of that year of spend.
Omer (34:49.960)
Right.
So let's talk about the channel that you did get to work and is still working for you today, which is basically outbound.
Tell us, how did you figure that out?
Did it start working right away or did it take some time to start getting results from that?
And why do you feel that this was the best channel for your business and your market?
And I think this is really one of the takeaways from this part of the conversation is that just because something works for Artifact, it doesn't mean everybody listening out there should go and do outbound.
Right?
It's like you need to find your own channels, your own growth channels.
And it's going to take some testing and experimentation to figure that out.
But I think it is worth understanding, like how you arrived there and how you've been able to get that to work for you.
Nate Sanders (35:41.940)
Yeah.
And by the way, Julian Shapiro has a wonderful framework on how to be able to assess and understand which channels are going to be most effective for you that we really love and think really heavy of him and everything he's done there.
And that helped us through, I think our framework of how do we try a lot of different things as fast as we can there.
Yeah, we saw initially we were doing the direct outreach ourselves.
So again I was the first SDR and what we found is that almost every single one of the folks that we saw good retention, high engagement, just good qualification from, came from the fact that I spent time doing the hyper qualification and doing the direct outreach myself.
So that was the obvious impetus leading us towards SDR as a kind of early part of our go to market funnel as we brought on our first sdr.
His name is Chase, our first SDR company.
He did an amazing job and we saw MQLs skyrocket from the fragmented time that I was able to put my attention on it and our MeQL to SQL conversion was really healthy.
And so that was enough impetus for us to build to say okay, we saw this previous signal, now let's take this next signal and invest further in it.
Based on the fact that we're also seeing a lot of other channel investments not be as effective.
And so we doubled down with another STR and this time it was an actual manager and someone that we were looking can we have this person turn one to three with their contribution which ended up being a super effective investment.
And we're now currently doubling down on on that same channel with headcount and spend.
Omer (37:25.960)
So you hired an SDR and they're basically generating your leads, your marketing qualified leads.
In terms of conversion, did you also have an AE on board or was the SDR also doing that part of the job?
Nate Sanders (37:45.080)
From the earliest stage we brought on and an AE and comped them like a biz dev role.
So there were still OTE commission but we comped them high on the salary aspect and told them that their job was exploration, understanding, learning.
And I sat in shotgun with them obviously throughout the entire sales process.
So I've always been very, very involved on the sales process side because I think it's a valuable learning of product market fit and it's not something I'm willing to just abdicate to someone entirely yet even still.
And so we brought on someone that could actually help me operationalize and build out a system around the AE sales aspect and We've had some wonderful learnings there as well.
For us, like some of the biggest things that we've learned from Enterprise Sales Motion is that initially our sales funnel was our perception of how the customer was moving through our sales process.
And we've learned through mentors and our own experience that it needs to actually be commitment pattern based.
It needs to be the certain things that we ask a customer to do that they agree to keep, and that if they do, we know they're actually making progress towards becoming a closed one opportunity.
So that commitment pattern actually becomes super important for us.
Omer (38:59.490)
So it'd be great if you can give me an example of that.
But just so I understand, you're saying if.
If somebody is looking at a pipeline and they're looking at a customer and they're saying, well, I had a pretty good conversation with them, so I'm going to move them to a 60% on my pipeline, the probability of closing.
Now you're saying rather than it being some kind of subjective thing, you were saying, here's a checklist of things that we want the customer or the prospect to commit to, and once they do that, then they meet the exit criteria to move to the next stage.
Nate Sanders (39:35.560)
Yeah, yeah, exactly.
So an example of that is that in our old sales process, we had a stage, it was called poc for proof of concept.
And what we've changed that to is customer has sent us an identified list of data sources they want to integrate.
Customer has signed in and authenticated with those data sources.
You know, customer has logged in for the first time.
Like, it's actual commitment patterns that we know actually progress the customer towards a healthy engagement and something that we can recognize they're going to be qualified and get value out of artifacts.
Rather than our perception of our ability to carry and shepherd someone through a process, it's our perception of how they're moving through.
Omer (40:18.770)
Love that.
Love that.
I think the SDRA question I asked that because one founder I spoke to had on the show.
What they ended up doing was they were both tech founders, they hired someone to generate leads.
They had the same peop.
They basically just hired more SDRs and they had these people doing both jobs.
And what would happen was that these people would generate a bunch of leads and then they would spend their time trying to close these leads for some weeks or months.
And as a result, there were no new leads coming in.
So when they'd go back, the pipeline was kind of dry.
And admittedly, he told me, he said, look, at the time, we didn't know what an SDR was or what an AE was or whatever.
So we're just trying to figure that out.
But it sounds like you did a better job at getting that set up better from the outset.
Nate Sanders (41:14.640)
The interesting part there is that like you said a few moments ago, it's not always going to be right for every single company.
But the approach that we wanted to take is rather than having me be the sole salesperson that did everything, knowing that there's valuable learnings of being involved at every step of the process.
Our perspective was if we try and operationalize a system that works at a really reduced low spend way, but we can actually see reproduce over and over again, that's the best possible signal for a growth stage capital allocation.
Not from investor standpoint necessarily, which is also a great signal for them.
For us of this is something that's ready to scale is if we can see this system work and we can see it repeat itself.
We know that if we put money in, we're going to get X amount of dollars out.
That was a perspective that we took on.
This is which so we had an scr, we had an AE and me, and that's the system that we want to see.
Can we make this scale?
You know, there's obviously great founder advice sometimes for just have the CEO be the only salesperson in every possible way.
And I'm sure that works out for a lot of companies.
For us it was much more important in an enterprise environment to be able to make sure we had something that we could repeat for very large enterprises over and over again.
Omer (42:34.790)
We should wrap up soon and get onto the lightning round.
I have one more question for you.
I know one of the challenges or you had to overcome was being super focused on your target market and the segments you were going to go up, you know, go after.
Tell me about some of the early struggles with that.
Because it wasn't that clear cut.
Right.
And you were being pushed in several different directions.
Nate Sanders (43:05.570)
Yeah, we've always had this weird odd bifurcation of the further down market we went.
It took a lot more hyper qualification for us to be able to know that they're the right customer.
But they moved faster, they would respond quicker, they were more responsive.
It was very tempting because of those dynamics.
So you had to talk to more people but they ended up engaging faster the further up market we went to enterprise, the qualification was incredibly easy.
It's hard for us to find an enterprise that isn't just screaming with pain around the aspects that Artifact solves, but they move slower the sales processes are dramatically longer.
So as an early stage company, it's hard to be able to figure out which one of these paths do I choose based on the fact that I only have so much capital I can allocate towards it.
And we had a lot of investor feedback, a lot of other founder friend feedback that hey, like just go focus on this easier aspect.
And time and time again that has proven to not be the best decision for us and that we should actually just grit and bear the fact that we need to take the time to be able to invest in the best possible customer for our business.
And if we need to be able to find some sort of dilutive capability to capitalize that, then let's do it because that's the best decision for the business.
So that's our biggest learning from that is do what is right for the business and finding the best possible customer for the business rather than trying to fit or shoehorn this framework that you think is going to propel you along.
Omer (44:40.090)
So I mean, on the face of it, it makes sense.
And many founders I talk to will say I picked that segment because they were the only people that would talk to me.
So I said, why not?
That's where I'm going to focus my time.
But if you had done that, what do you think the consequences would have been?
Where you've gone down market?
And you say, well, these are the people who seem most engaged and they're most responsive.
I mean, that sounds like a good thing.
Why was that not a good thing for your business?
Nate Sanders (45:13.600)
Churn would be higher, ACVs would be 90% lower, everyday engagement would be lower.
It would just create a much less healthy business overall.
And we saw those signals in the product analytics and the email responsiveness, et cetera.
And at first it was hard to see that pattern.
But increasingly as we saw more and more of it, that's what became very indicative of if we had gone that direction, what would I think be the nature of the business?
So our ability to stay focused on that upper end market has just created a much healthier business.
The unit economics are better, the ACVs are better.
Engagement and retention, adoption is better.
Omer (45:54.250)
Okay, and as a quick follow up question to that, there's one step or part of this decision making is, is figuring that out that, you know, this group here, even though they take longer, there's more challenges.
It's the right group of customers to go after.
I would say the second part of that challenge is saying no and we're going to stop doing this.
We're going to stop going after these people.
And I think it's even more complicated when it's not just a discussion between you, the founders.
You also have to go and explain that to investors and other stakeholders who have been pushing you in that direction.
So how did you handle that part of the decision?
Nate Sanders (46:50.450)
Not well.
I had a lot of whiplash there for sure.
As a really quick example of that, in the fall of this last year of 2022, we had almost 80 to 90% of our enterprise pipeline disappear because of workforce reductions, layoffs, budget cuts, et cetera.
So we went from almost $2 million in pipeline to nearly zero.
And it was silent from about early October until February of 2023.
And so we had this timeframe where we were starting to be able to put more of our focus on mid market again because just you have to do something.
You can't sit around for six months and not do anything.
And so we saw customers closing at mid market and then we saw enterprise start to be able to pick up back again in the spring.
And that's yielded some really interesting TCV opportunities for us.
Moments like that have been a dime a dozen over the last three years where we had a lot of different black swan events that have made this very difficult to be able to see through the glass clearly.
And I think the fundamentals of just as a group thinking for ourselves, thinking about what's right for the business, and thinking about what we know is true has been the only thing that has helped us navigate that.
At the same time, I would say our ability to have switched priorities or change focus and do that in a nimble way.
I don't think looking back was the wrong decision.
There's nothing you can do as a founder that as long as you're focused on trying to make the right decision with the information you have as fast as you can.
I just don't think that that ends up being a regretful paradigm or decision framework that you're going to look back on and not have fond thoughts about.
So, yeah, we've changed focus priority many times, but only in light of the information that we had in front of us.
And we're going to keep doing that.
Omer (48:50.160)
All right, let's wrap up.
I've got seven quick fire questions for you.
You just try to answer them as quickly as you can.
What's one of the best pieces of business advice you've received?
Nate Sanders (49:01.280)
The customer breaks the tie.
Omer (49:03.120)
What book would you recommend to our audience and why?
Nate Sanders (49:06.160)
Thinking in Bets by Annie Duke.
It's the best possible way to be able to learn how to make high quality decisions.
Omer (49:12.000)
Wasn't she the poker player?
Nate Sanders (49:14.310)
Yeah, she was a world champ poker player.
Omer (49:16.110)
Yeah.
I don't know much about poker, but that book's come up in my radar before.
What's one attribute or characteristic in your mind of a successful founder?
Nate Sanders (49:24.470)
Resilience.
Omer (49:25.990)
What's your favorite personal productivity tool or habit time?
Nate Sanders (49:30.710)
Chunking.
Putting a task on my calendar rather than a task list.
Omer (49:35.830)
What's a new or crazy business idea you'd love to pursue if you had the extra time?
Nate Sanders (49:40.320)
I'd love to be able to explore AI and genetics in therapeutics.
Omer (49:45.440)
What's an interesting or fun fact about you that most people don't know?
Nate Sanders (49:50.800)
I'm a great cook.
I love cooking.
Omer (49:54.480)
And finally, what's one of your most important passions outside of your work?
Nate Sanders (49:57.520)
Family.
I have two beautiful daughters and a wonderful wife and have had children since my early 20s.
And I'm just crazy about my family.
Omer (50:08.440)
Wow, you started young, man.
Nate Sanders (50:10.680)
I did, yeah.
Omer (50:12.440)
Awesome.
So, Nate, thank you so much for joining me.
I think we covered a lot here in terms of unpacking your story from the days at Pluralsight to where you've taken the business to today.
If people want to find out more about Artifact, they can go to Artifact IO and if folks want to get in touch with you, what's the best way for them to do that?
Nate Sanders (50:34.620)
You can just reach out to me over my email.
So natertifact IO, happy to chat with anybody.
Thanks for the time.
Thanks for having me on.
Omer (50:41.780)
Yeah, it was my pleasure.
Thank you.
And I wish you and the team the best of success.
Nate Sanders (50:45.740)
Thanks.
