Three SaaS Lessons They Won’t Teach You In Business School with Bogdan Maksak of DigitalGenius

As a founder, you love to build stuff yourself in the beginning, but then like potentially then you on the hook, right?

You on the hook to maintain this, you on the hook to like look after it, make some updates, and that is not ideal. So like the sooner you can onboard the team to like take over the better. Welcome to SaaS Origin Stories. Tune in to hear authentic conversations with founders as they share stories from the earlier days of their SaaS startups. We'll cover painful challenges, early wins, and actionable takeaways.

You'll hear firsthand the do's and don'ts of building and growing a SaaS, as well as inspirational stories to fuel you on your own SaaS journey. Here is your host, Phil Alves. Welcome everybody. Today I'm excited to have a chat with Bogdan Makazak. He's the founder of Digital Genius, and I'm excited to share with him today. Welcome to the show. Excited to be here.

So the first question I like to ask my guests is tell me a little bit about your background story and how you come up with the idea to build your product.

Yeah, so essentially I was at the university and I met my co-founder, Dmitry, back then, and it was originally his idea. It started with his idea. He was developing a feedback management platform where essentially in a restaurant or in a hotel you'd have a little banner that would say, have any feedback, text this number. That was his project. That was his idea. And it was going okay.

At the same time, I was working on some other startups, and we just collaborated a lot, but we were working on separate projects at that time. None of our projects really at that point exploded. I think we did like 10 different startups. Most of them failed at that time.

At the same time, we were getting involved with this accelerator in London called The Bakery, and one of the co-founders of The Bakery, Alex, he called Dmitry up and he was like, you know that feedback management system you have where people SMS their feedback?

Can it reply?

And Dmitry was like, yeah, sure, you can reply too. And he was like, great. We have a great project with BMW for you. And that was the start. So essentially, BMW was launching their electric cars, and that was eight or nine years ago, way before Tesla was popular, before people knew anything about how it works.

So they were looking for some tech to help them in their marketing campaigns to educate the audience on how far you can go on a single charge, where do you charge it, et cetera. So we then pitched out the BMW together, and that's when I joined Dmitry in our endeavor. And we essentially had our first customer on.

Back in the day, it was probably one of the first few chatbots on SMS before the chatbots became really cool. And that's how we started, since then it evolved, and we had a little bit of a pivot at some point. But that was our start. That's exciting. So you're going to college, basically, where you're your co-founder.

And you're not from London, so you moved to London to go to college, or you were already there.

Yeah, I came to the UK when I was 14 to study here. And then in university here, I'm at my co-found. We actually were at different universities, but at some random, really random tech event, there was one guy speaking Russian, another guy speaking Russian, somebody thought they should talk to each other. And that's how me and Dmitry got introduced to each other. Originally I'm from Ukraine.

And yeah, I came here to study, and then in university times, I was doing a number of different entrepreneurship projects, clubs, and me and Dmitry were just in parallel jamming on weekends at some co-working space, working on a new thing that is probably going to fail again. So we're just shipping out lots of different things together. That's awesome.

Yeah, I think that's the thing people don't realize. To get a business outdoor, many times as founders, we go through many ideas until we find that one that works and just the exercise of keep building something and building an X-wing, that makes a real difference. But BMW is your first customer.

How was the experience?

That's a big head start.

Yeah, I think, I mean, again, like you mentioned, before that head start, we had like probably more than 10 other ideas that were completely disastrous. BMW was great.

I mean, and this was, again, thanks to Accelerator, the bakery. Because essentially what they do now is they take big brands and their marketing briefs and they match that with different tech, mainly startup tech. Most of those brands are big. We had BMW first and we had Panasonic soon after.

I mean, it was good.

I mean, we were young. We didn't really know what we were doing.

We just like, what does it take to make it successful?

They'll do whatever it takes, but we didn't even realize BMW was a big deal in enterprise sales. It was like, fine, cool. Let's make it work. I see that in myself too. When you look back so many times, you're kind of naive. And I feel like that's also kind of necessary to be successful. You don't really know what you're doing. You're doing the work.

And I think it's a trait that entrepreneurs have, being naive.

Yeah, I totally agree with that.

I think, I mean, the fact that we were young in a way, it was to our advantage because like, like you might be naive to think this is like too complicated. This is impossible. Like you just do it because you just have lots of energy and you don't have the experience to tell you this is going to be difficult.

Obviously, there were some mistakes made along the way as well.

But yeah, I think being naive in the way of just doing things is really key to any kind of entrepreneurship project.

Yeah, for sure. So you told me the product pivot a little bit later on.

So what is the product today and what problem do you guys solve?

Yeah, so today we are making online shopping a seamless experience using conversational AI. So think chatbots, but on steroids specifically built for e-commerce. Let's say as a customer, you bought something online and then you need to check where your order is, or you need to cancel your order or change your address or get a return label. We can do all of that automatically for you in the chatbot.

And the difference between what we do and lots of other chatbots out there is we have very deep integrations with e-commerce ecosystem. So our chatbot is actually helpful. It doesn't just go in the loop saying, could you rephrase that or here's a logical for you to read. It actually performs refunds, cancels orders, does investigations. So there's other components to it, but it's a very simple way to understand it.

So how you go from making chatbot to BMW to making chatbot to e-commerce?

Walk me through when you realize you want to get into the e-commerce market over making chatbot for those big brands.

Yeah, that's a really good question. It was a bit of a journey. So just to add more context, we also had a third co-founder, Michael, join us about a year later and he was essentially running all things sales in the States. He got us some big customers in the States.

But what we quickly found out is in marketing world, at least with the chatbots we were using, they were very much campaign driven. It's like even BMW after like two years of running that campaign, that was it. The project was over. So there was no recurring revenue from that.

And the second part we realized is, especially back in the day, a little bit less now, but especially back in the day, to train a good AI, you needed a lot of training data, historical conversations, to make those deep learning models work. You got to have the data.

And again, in marketing, that would be some kind of new campaign that people want to run. There's no data for it because it's new. So we instead pivoted to customer service because customer service is recurring. They keep on having those requests year over year. And it has a vast amount of historical data. So that was our first pivot.

And we also moved away from a chatbot to more of an agent assist product. So imagine a customer service operation, hundreds of agents. To make them more efficient, we would recommend to them responses to use and just send. And one of our first customers was KLM Airlines. They're still like a big power user of the product. They love those prompts.

But what we quickly found out later is, although it provided really good ROI for the guys like KLM and a couple others, for a lot of other companies, it was hard to measure the ROI.

Like, yeah, the agents are a little bit faster, but how much faster are they?

Does it actually make a big difference or is it just a nice to have?

And also, we were targeting any company that had a customer service operation that was also using Xamdisk or Salesforce as their help desk solutions. That was it. So we were very much like going out there and getting companies like financial services, banks, subscription companies, travel agencies. There was such a wide range of customers.

And we got a number of them quickly, but it was very hard to go to the second level of like doubling that amount of customers. And we were looking at different problems and everyone was kind of blaming everyone for this. It was a product issue, it was a sales issue, it was like a customer success issue.

But in retrospect, and that was the point when I got recommended to read the book Crossing the Cosm. In retrospect, I think we were missing the focus at that point. And so we had a bit of a change in the company. We had downsides. It wasn't a happy time. We went back to the drawing board. Two of my co-founders had to step down.

I became CEO, originally I was CTO of the company. And we were looking at like the core of what we do and the product market fit. And we realized, you know, we're starting to too many companies in too many different verticals. And for a lot of them, all of those verticals, we got some innovators right now who are using the product and they're loving it.

But then to get to the rest of the market who need lots of references, it would be a difficult challenge because for them it was more like a nice to have.

I mean, they have their customer service operation running right now. They're not losing their sleep over this problem. Whereas there were a couple of customers in our portfolio, specifically in e-commerce, that I got a call from one of them in September time telling me, look, we've got this massive backlog of unsolved cases. It's a crisis.

Can you guys come over?

They're based in Switzerland.

Can you come over to our offices and help us solve the issue?

So like we fly over, we look at this issue, we come up with the plan and I understand like at the end of it, these guys are desperate, right?

They're losing their sleep over this compared to all the other customers. And I was a customer in e-commerce and there was like another one that had a very similar use case, a very similar problem. And what happens in e-commerce is you have those peaks like we have now with Black Friday and Christmas. For those peaks, they have so much pressure across the whole business, but also in customer service.

Sometimes they have to hire like triple the number of customer service agents for a couple of months just to help that peak. And that's a real challenge. So unlike other brands that are kind of growing at a more steady rate, the e-commerce guys, they have these peaks, it's hard to predict things and they're desperate. And that's exactly what we needed.

We needed to focus on the smaller segments, but that's of desperate customers. And that's what we did. And that's how we ended up with e-commerce. That's awesome. There's so much to unpack here. So let me try to say everything that you say in a way that we can kind of make it clear for our listeners, but it's amazing story.

So you guys start with, we're going to make AI chatbots for those big companies because you got those customers as referrals from your incubator. And then from there, you kind of got into, we're going to make this for everybody that needs it. You got into customer service because you realized that was better than marketing. So it was a space trying to niche down.

You're like, always trying to figure out what's your ideal customer. But I really like what you say about, we were still nice to have. And that's the problem you're trying to solve. We don't want to be a nice to have. You want to be a pain killer. And when the customer call you and you went and then you realize, okay, this is a space where we can be a real pain killer.

And then you explain why, like how they have demands that go high, demands that go lower. And I think again, if you're building a SaaS, that's how you should think.

How can I move from a nice to have to work with clients?

Again, I also love the word you use, desperate to work with me. And that's an amazing story. And another thing for people to realize, it might not get it right in the first time. You kept going, you kept figuring it out. I believe moving to customer service was better than work with marketing. And then from there, you did even another step, working for e-commerce is better than trying to work with everybody.

That's an amazing experience of how you niche down and how you found product market fit. Because I mean, you're the CTO and I want to dive down into the technology. I'm sure from day one, technology was amazing, but technology without the perfect positioning, it's not going to solve like a big enough problem so you can scale and grow your business. So you can rely only on technology.

And that's kind of like what I would like to dive deeper on it. Like walk you into the process of designing, prototyping, encoding, and the AI, figuring out like how to build that. Now you're the CEO, but you're like super involved in the building as the CTO. And maybe you can even go to the different for the three versions. I will ask her version one of your AI product.

And then let's talk about the customer. And I'll stop you if I want to, if I have questions in the way, but let's dive into the building of that product.

Yeah, yeah, totally.

I mean, absolutely, absolutely right in terms of summarizing of like, you might have the right product, but you might be selling to either the wrong market or too broad of a market, especially in the beginning. In the beginning, you need those desperate customers who look like ask yourself a question.

Are they losing their sleep over this problem?

Yes or no?

And normally it's an easy question to answer. And if you're not, you're building like a wrong thing right now.

Well, not even the building, but you you serve it up to the wrong market right now.

But yeah, to your question of tech.

So yeah, really good question. It evolved a lot. It evolved a lot.

I mean, in the beginning when we had our first product with the likes of BMW and others, it was really basic.

I mean, it had some basic AI into this, but it was mainly like Keywords driven stuff. The ITech wasn't that advanced back then.

So you had to write down lots of different variations of how somebody might ask a question like how far can this go on a single charge?

And yeah, there were some like cool synonyms and other features you could use, but it was so, so intensive. And then when it wouldn't get right, like when it wouldn't respond, then you would have a human who would help it by actually selecting the right respond and responding. So it was it was like AI chatbot, but there's a major human element behind it too. First version, very basic.

And I think that's how you should do the first version.

How long did it take to build the first version?

Three months. Three months.

And then how did you solve that like problem of needing to have a human behind?

Because many times when people are trying to build AI, they wanted to do everything. I know because I tried to do that too. And it just didn't work because people didn't realize the limitations and couldn't live with, okay, we need this is a helper. It's not going to do everything yet.

How did you solve that problem?

Yeah, I mean, in the beginning, like, you know, in a scrappy way, our the same engineers who are building the AI, they were also the humans behind it. They would also reply to those messages manually. That was the start of way just, you know, get it rolling, get it done. But then very quickly, we realized obviously that's not scalable.

And that's why another reason why we picked customer service as our next kind of iteration because in customer service, you have all those agents, right?

They are already there. The company is already employing all those people to reply manually. So when the AI doesn't respond to it, we can just pass all of those agents. That was also a reason why we picked customer service. Makes sense. You can look for the existing patterns in the AI. Because they already had the people behind.

So did you guys did kind of like a Wizard of Oz thing where engineers kind of still pretend to be the AI or did you let the user know, OK, now you're talking to. In the first instance, Beijing and now they were talking to a human. Now there wasn't like an argument to say, oh, now you're talking to a human.

Yeah, that was the first version.

Yeah, that's cool. Because again, that's another methodology, another strategy you can use to build your product. It's called the Wizard of Oz, where it's nobody knows what's happening behind the curtains. So it looks like it's AI is still for the user. And they're like, oh, look, this is AI, super smart.

I mean, you have to be careful with this stuff. Like obviously, we did this in small use cases for a limited number of time. Like where you need to bridge some gap, but you have to figure out some other things quickly. Like in other examples of companies out there where there was this story I read recently.

I don't know the name of the company, but it was something that was accounting and where it was all supposed to be AI. But turns out they couldn't actually make the AI work. And there were just like hundreds of people in the Philippines, accountants in the Philippines, just actually doing all the work, pretending that was AI. And it was a horrible result as well.

So like, you have to be careful, especially in the world of AI where like it's so hard to understand how it works and who's behind it. Like I think, you know, the fake until you make it then needs to be really careful. You only do it, you know, when you know actually how you're going to bridge the gap. I agree. I agree. So let's move on to version two.

Now you're building version two of the product. Yeah. So version two, I was coding the AI engine myself for a couple of months. And if you use the latest deep learning algorithms back then, it took me, I think, I guess, some sort of like three to four months to get it out.

And yeah, it was first used by KLM as a customer for kind of servicing prompts to the agents. And the first version I built, I think it was there for almost like a year or two live until like we got a bigger team in-house who had built replacement for it and maintained this for some time.

And that's like one of the lessons I learned is as a founder, you love to build stuff yourself in the beginning, but then like potentially then you on the hook, right?

You on the hook to maintain this, you on the hook to like look after it, make some updates. And that is not ideal. So like the sooner you can onboard the team to like take over the battle.

Yeah, for sure. As a founder, you want to replace yourself and then it becomes a very, being building that product in the day to day, you probably can spend your time better other places.

So tell me a bit like, where did you go to spend your time when you're able to not be like, developing so much anymore?

Like how did you spend your time?

Yeah, so I done one more into product and customers because I think that was the time when you know, the product was used by maybe a few dozen customers at least. And they were questions, they were reason about the ROI. Some customers had amazing ROI out of the box. And some customers were not clear about the ROI. And you know, this is my baby, I need to solve this.

And they were like mixed opinions in the overall team.

You know, some were thinking, we just need to build some analytics to measure the ROI. But then I would just run out and spend some time with the actual users and with some of our customers. And I realized, you know what, it's not so much the fact we don't measure the ROI.

It's actually just a question, is it there?

Like, are we actually saving that much time for the agents?

Maybe not. So maybe we shouldn't be building some new analytics features. We should actually look at the core of the problem.

Why are we not making them more efficient?

And most of the team actually disagreed with me back then. Like most of the management team disagreed with me. They're like, no, let's build more analytics. There was a mixed experience guys we hired by that time.

And I just like when I kind of stepped out of the main process and like got a small team, like we had maybe like 20 engineers back then, but I picked up like four engineers just for my own little project.

So you know what, we're just going to build something like a new iteration of this because I think this is a fundamental problem that is the rest of the company was like selling and like building the other product, the previous product, but maybe like a small SWAT team building this new iteration, which is actually what the product is now. Because yeah, and that's not to be true.

Like it wasn't a problem of lack of analytics.

You know, it was just was not delivering the ROI. The agent is this product. And that's how we ended up with a new version that was automation focused and had lots of integrations because one of the key learnings I had from again spending time with the users is agents, customer service agents would spend lots of time in other systems.

So the actual response part was maybe the quickest part, but where they would spend a lot of time is just going to one system, clicking lots of buttons there, another system. And that was the time consuming part. And the way for us to improve the ROI was to add all those integrations and to make it automated. And that's what we did eventually.

So did you ask the people what they need or you sit and analyze and you figure out what they need?

So like how did you?

I think it's the latter. I think this is one of the tricks.

I think very often when you're building a B2B product, the customer or maybe your CSM team, they will be asking you things, but they often don't have a big picture in their mind, right?

They're asking for things that are small improvements to what you've built. I think as the founder who's building product, you got to be there in the field. You got to be there like meeting the users, observing the users and seeing yourself because that's the only way to kind of get to the roots of what's going on and make sure you have the right solution for it. For sure.

I think the lesson here, it's like you as a founder, you listen to our customers, but you also look at what they're doing and at the end of the day, you have to make a bet.

Your bet, like you say, was that we need a product that was different and integrated and you have even a hard time convincing everyone inside your company that that was the right bet. But I believe building product is about that. It's about what is the next bet that I want to make.

You go and you understand, analyze the market, understand the better way that you can, the problem, and then you come up with a solution for the problem and then you make the bet. And then later, again, listen to your customers like you say, they're going to help you enhance. They're going to help you improve your product.

But the big bet, like this is where we're going, it's you to make as the founder. That's your call to make.

And if you're not courage enough to make the call and just keep making incremental improvements, it's going to be very hard, depending on the stage you are, to grow, especially when you're still trying to find product market fit because it was the bet that you made that got you guys to product market fit.

Is that correct?

Exactly. 100% correct. Yeah. And since then, we've been on a very successful path because we product market fit wise, we have really good metrics. And that was because we changed the product to some degree, added all those integrations that no one was really asking for. I was just me seeing that no one really was asking for.

But also, focusing on e-commerce, those were the two things, the product and the market, essentially.

But yeah, I think you summarized it really well. It's like the fundamentals, the big bad, like the people are going to ask you for it. That's something you discover. It's a bit like a narcissistic process. You sometimes need to see what other people don't see and then come up with a solution for it.

So another analogy might be, if I'm a doctor and I have a patient who is sick, I'm going to listen to what he tells me about their symptoms. But just based on what they tell me, I'm going to make my conclusion. You have to do some tests. You have to look inside. You have to look deeper than just what they tell you.

Very often, they tell you what's on the surface, but you're the one who needs to dig deeper to get the root cause of what they're experiencing.

Yeah, exactly. Because you don't want to be solving this, the symptoms, and never go to the root cause of the problem. So I love that knowledge, too.

So how did you fund the company throughout this whole process?

I imagine that you got some money from the incubator company about funding.

Yeah, funding-wise, this is where Mihail and Dmitry, they stepped up very quickly in the company. I think the first round of our angel investors was from Mihail's network. So Mihail's got a number of friends and colleagues in the States that raised money before. And he reached out to them, and they introduced him to a number of angel and early-stage VCs.

And we raised something like $2-3 million in convertible notes to begin with.

And then, yeah, we had a series A where we raised about $10 million, roughly. And that was Dmitry. He was pitching to lots of different VCs, and we got Salesforce as one of the investors, and a couple other big names as well.

I think, unfortunately, at least from what I've seen, it's very hard to get in front of an investor if you don't have someone introduce you to them. It is possible, but it's so much harder. So if you can, try to build a network for it. Either other founders, other startups. For example, right now, I work from VBuck these days.

How are companies remote?

I come to VBuck, and I see some really talented entrepreneurs here building amazing products.

And if somebody asks me, can you introduce me to some of your investors?

I say, hell yes, because I love what you're doing. And that's a much easier way to get in front of someone than sending them a call or email. Makes sense. I think that's great advice. Work with other founders, especially founders that raised money before because you now know the investors.

So how did that money affect you guys?

So especially after Series A, I haven't seen a lot of companies after Series A where there's a lot of money coming in, and then you start to go in too many directions. So walk me through how it was getting the $10 million add to your bank account. Yeah.

I mean, kind of similar to what you're saying.

And if you also bring the story about the product evolution, right?

Our Series A was at a time when the product was that agent-assist product without any focus, complete lack of focus.

Any company, you got some agents?

All right, you're fit. And so yeah, that $10 million quickly got spent on lots of sales, marketing, some product as well, but produced very little results. I think it was lesson number one was the PMF wasn't exactly there at that point. And before raising that kind of Series A, you should collect more metrics. That was the lesson for me, for example.

You should collect more metrics about your conversions, about your renewals, to have a solid understanding, yeah, the PMF is there. We've got the fundamentals. Now we can really expand and grow. And then we had a small convertible note afterwards, which was like $3 million or so. And we had way more results from that $3 million, like 10 times more results from that $3 million than we had from the original 10.

So I think timing is really important.

And like you mentioned, you could so easily burn the cash by doing just like a lot of things which on paper look right, right?

You got your sales team, you got your marketing team, you invest in the product.

On paper, there's all good things to do. But if you look at the roots of what's going on, and it's a disaster, like raising a lot of money at the wrong time can end up in a big disaster where you're just burning lots of cash, not meeting targets, everyone's like unhappy, morale is low.

And luckily, because at the same time as that was going on, I was working on the new iteration of the product, and I could see the light at the end of the tunnel, I could see the future, I could see the root cause of the problem. A lot of the companies at that point, they fail after Series A, they close down, that's it, that's the end of this journey.

So yeah, in our case, we were close to that. We were close to that, but then we turned things around in an amazing way, and now we're growing really fast.

And yeah, but that was our near-death experience at some point, like after Series A, after burning so much cash, we were very close to kind of not making it.

Yeah, because I guess that's kind of what investors want, they want you to make it or break it. And it's very stressful being in that situation because there's a real chance you can break, especially if you don't have product market fit yet, you were not ready to scale. And so it looks like you found product market fit in another round after Series A, where we have only three million dollars left.

You say there was a layoff, so things didn't went super well. And you're like, now is our chance, we have to find market fit right now.

Is that how it went?

Pretty much, yeah, pretty much. And I mean, it was happening at the same time, where by the time I was to be raising a stream million, I already made some good progress on the new, not completely new product, but evolution of the product. So it wasn't just like, let's get stream million and then figure things out. It was like, we already figured out lots of things.

So let's get this stream million to advance it. What we were lacking immediately is that focus, focusing on e-commerce and having a desperate customer, not just a nice to have, but a must have solution.

And yeah, that stream million, they kind of gave us some time to focus, to readjust the strategy.

And yeah, and then we became very given very quickly because our revenues started growing very quickly with the e-commerce focus, we didn't need to raise more money. And we had like amazing growth in the last two years. The PMF was there. But I think, I mean, the key summary there is raising a lot of money when you don't have strong PMF could be a disaster.

You got to nail your PMF first before you raise a lot of money.

Like now, like we're raising around right now, now it's so easy. Now it's just like the fundamentals are there. The whole machine is just working. You're making some adjustments. Everyone's working hard. But when fundamentals are there, like, yeah, now the investors can be make it or break it and we're going to make it because like the fundamentals are there.

But when they're not there, you're really taking a gamble and it only makes things more difficult. When you have more people on the team, when you have more expectations, it's only hard to figure out PMF because you have way more people between you and the end user. And there's so much things going on that you don't have enough focus to look at the actual like product and the market.

When you're smaller, you're more nimble, you're closer to your customer. That's much easier and you're more flexible.

You know, you can change things. You can change some of the product features. You can change your pricing. You can change your market. But when you're trying to grow really fast with a huge amount of team behind you, you can't change things that quickly.

You know, there's too many managers. There's too many processes. So if anything, yeah, in a reason, a big round before you got PMF not only could be disaster, but could also like distract you from getting the PMF. I think that's an amazing lesson. Thank you very much for sharing. Like make sure you get to product market fit before you do a big raise.

And I think like when you say early, you figure out this is the bet I want to make and you say a lot of pushback. That's because you had a lot of money. That's why the company was big.

And that's probably the reason why you're getting pushback on that one feature, right?

Is that correct, Asum?

Yeah, I mean, essentially, people were in denial, like because like it's very tough to like face the reality when we just raised all this money, we have so many people, the CRO has this huge targets and you come to us and say, you know what, guys, fundamentally, things are really flawed.

So like, you're not going to reach your targets. The money we just raised, like, it's a bit pointless. Nobody wants to hear that. Nobody wants to hear that hard reality. And so you just got a pushback and people in denial.

So, you know, we just need to build some analytics. And it's fine. It's okay. Just add some graphs. Just show like a trend moving upwards somehow. That's like the easy solution that people are looking for. Hands they're pushing back.

If they had not had that pressure to deliver the results, if there wasn't so many people, so many managers, so many expectations, then they would be more receptive to kind of looking at the fundamentals. Makes sense.

So one question I like to ask, and I think kind of like talking about that a little bit, but what is like the first oh shit moment that come to mind from the early days of your SaaS?

I mean, one that immediately came to my mind. So this is the new product, the latest product we have. Me and our VP of customer success, Bor, is amazing guy. We go to a customer and the first customer who was using the latest product when everybody else was like on the older product.

And this was like my latest baby was all the fixes, all the integrations part, right?

And we go into review medium, which was like a review of a renewal of their contract. And we set up the meeting and the customer themselves on the whiteboard just puts the ROI in front of us, which was really good ROI. We didn't have to do anything. Like they just themselves like, yeah, amazing ROI. These are the numbers. Good renewal sorted.

And that was like the first time Bor looked at it, just like, oh shit, yeah, it's happening. It's working. Because all the meetings before that, we had to like come up with ROI ourselves, you know, spend like weeks trying to like in the day to find that ROI somewhere where it was like impossible to find. And that was yeah, that was the beginning of the new era where things were working.

And then after that, we also then narrowed the focus to e-commerce. And we started doing free pilots because that was not the test of your PMF. Do free pilots and limit them in time from like 30 to maybe 60 days maximum. And just see how many like convert from those free pilots. And we had like 95% plus conversion in our first year, which was like, shit, yeah, this is working. Yeah.

So you give your product away for free and then you come back and say, hey, by the way, I have to pay now. And people are like, yes, sure. Here's my money. Pretty much. Yeah.

I mean, we tell them in advance like that, like you have it for free, but then you have to pay later. And they're like, okay, sure.

Let us, you know, start using the pilot.

And yeah, the conversion was made. It was basically just one pilot that did not convert out of like what 13 or so. And that pilot even not convert because of to begin with, they didn't really have the right use cases for it.

But yeah, looking at that at the end of the year, like we were like, shit, like this is real. Like the PMF just like speaks for itself. And then the following year, like all those customers that had the pilot the year before and then started paying for the product, they had their renewal of their first year contract. And in that year, we had 130% plus net renewal rate in terms of dollar retention.

Whoa. Which was again, yeah. Yeah. So when you're looking at zero churn, you know, you found product market fair. Pretty much.

Especially if annual contracts, it could be kind of scary, right?

Everyone is coming up to renew what's going to happen. And then everyone actually renew.

And to me, those two metrics, I mean, there are some other things to look at as well, like sales yield, like basically how much you spend in to get the customer. But from the stickiness of the product, I think like the first two metrics to look at is conversion of your free pilots and your renewal rates before you make conclusions about your PMF.

Like you need to have the data speak for itself.

And it's very unbiased, you know, metric, like are they renewing or not?

Are they converting or not?

And if they're not, then like no excuses. Like something wrong with the market or the product or the pricing, something's off.

So how long was it your free pilot?

And did you tell people upfront, like how much it would cost?

Like walking through the process of getting people to the free pilot to convert in then to a customer?

Because I think that's a strategy that many other founders should be using. Yeah. Yeah. It worked really well for us. Thanks to our sales leader, Chris. He developed that strategy. So the pilot is to begin with, it was four weeks and then we realized it's a bit tight. So we increased it to six weeks now. So four weeks to six weeks free pilot. We tell them the pricing in advance.

We don't want to, you know, the customers get a big surprise at the end of the, oh, I have to pay this much. So we tell them the pricing in advance, but they don't, you know, they don't sign up to anything. They don't have a commitment until the pilot where then they can commit to a 12 month contract or not.

Sometimes they do sign some kind of order form before the pilot starts, but it still has an opt out. At the end of the pilot, if they don't want to continue, they can walk away and they don't always anything. It's a completely free pilot, which is good if you're selling to kind of bigger companies because we like medium to large enterprises.

So normally those people, you know, they will be in the first year, right?

They will be paying somewhere in the region between like 50 to 80 K dollars. So obviously like they need to know what they, what they, what they expect after the pilot, but still it's, it's, you know, completely commitment free process. Makes sense.

And how did you attract those people to, to do the free pilot?

Yeah, I mean, this is something that we still experiment in ways, but historically for us just outbound approach produced the most leads.

You know, we have some people who send emails, LinkedIn messages, phone calls to our target list of customers and they get them interested in trying out the product. That's like our number one source of leads, just pure outbound. Then there were some, there were customer referrals, some of them inbound based on, I know them searching for our product and a bit of partnerships too. I think moving forward, this might change.

I don't know, but this might change moving forward. Outbound is probably harder to scale, might get some results initially, much harder to scale with some sort of partners. Much easier.

You can bought like one really successful partners and they can bring you, you know, five, 10 customers a year and you don't have to do anything, right?

I mean, yeah, of course you have to spend some time with them, but generally there's no effort per customer. Makes sense. Yeah. I hear that a lot of startups, they start with outbound because you know exactly who your target, it's not super expensive. And then from there you have to figure out other strategies because like you say, it's going to be hard to keep scaling that.

So if you could go back in time and meet yourself from the day you start this company and tell you something, what would you tell yourself?

Read Crossing the Cosm book. That would be number one. Number two, focus on the desperate customers who are losing their sleep over this. And number three is raise money when you really need this. Makes sense. Yeah. Those are great advice, not only for yourself, but also for everyone listening. But I like to ask in that sense because when you're thinking about yourself, not like the other, you like really. Yeah.

Answer the question.

And so how does the company look like today?

If you could share a little bit about the size, where you guys are and what the future look like for you guys. Yeah.

I mean, so we had those amazing metrics when it comes to renewals, the pilot conversions. We had 100% growth in our ARR. And the next step for us is to figure out the growth channel.

Basically to your previous question, how do we get those pilots?

Basically been a mixture of channels moving forward. Again before we can raise a ton of money next time, I want to have a data driven analysis to tell us, you know what, when we increase our span on outbound by acts or marketing or partners, we get back an increase in Y in number of meetings. A very clear relationship between investment and when we get back.

And for that, you know, we need to experiment with a few things. We've been breakeven for the last year and a half with about 25 people on the team. But to experiment with a lot more strategies and to experiment faster, we are raising some money right now, like a small extra million rounds to just experiment with those things and quickly figure things out.

And then once we have the growth channel figured out, I think that's the time when you can raise a ton of money to just hyper grow. PMF is great, but before I think you can have amazing growth, you also need to have a very solid growth channel for that PMF. And that's the part that we're figuring out right now. I fully agree with that.

Now you know who needs your product and what problem to solve. You have to figure out how to get a lot of those people very quickly where you can just put money and get more of them. That's been an amazing story. I love the story of your SaaS and how much you guys grew and the learning and I think it's like very real. Everyone goes through the processes that you walk through.

It's not like day one and you're successful. You have to learn. I thank you very much for your time today. I really enjoy talking to you.

And if people want to follow you or learn more about you, is there a place that they can find you?

Yeah, I mean, you can find me on LinkedIn, just box and maxigun LinkedIn. That would probably be the best place. Awesome.

Again, thank you very much for your time today. It's been a pleasure. Thank you. SaaS Origin Stories is brought to you by Dev Squad. To find out more about how we help entrepreneurs launch new products and help larger businesses plug in a ready to go development team, visit devsquad.com. Add us to your rotation by searching for SaaS Origin Stories in Apple Podcasts, Google Podcasts, Spotify, or anywhere else podcasts are found.

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