SaaS Interviews with CEOs, Startups, Founders
How This SaaS Product Studio Built 3 Tools and Did $16m in Revenue Last Year
25 Jun 2024
Chapter 1: What is the main topic discussed in this episode?
You are listening to Conversations with Nathan Latka, where I sit down and interview the top SaaS founders, like Eric Wan from Zoom. If you'd like to subscribe, go to getlatka.com.
We've published thousands of these interviews, and if you want to sort through them quickly by revenue or churn, CAC, valuation, or other metrics, the easiest way to do that is to go to getlatka.com and use our filtering tool. It's like a big Excel sheet for all of these podcast interviews. Check it out right now at getlatka.com. So my name is Jaroslaw. I run a company called Railsware.
We're doing a lot of stuff with data, and then I'll show you the subtle art of it. So I've been 23 years in a pro tech, so basically working for technology companies and money. Wrote my first code when I was 10 years old. Engineer by heart. They make us drop some numbers in here. The company is like related with 100 plus million and it's 200 people altogether, a bit more than 200 people.
We grew five times in the last five years, specifically because of the data. The relevant experience is one of the products that we run, a SaaS product, is called Coupler. It helps you move your data around and figure out your data. So that's where basically the information about this presentation goes from. There's some consultancy that we're doing, including data consultancy.
As the guy said before, it's really great to onboard people into this thing because when you see a blank sheet and you're like, go do your data, it's actually pretty hard.
There's a practical approach, how to kind of make you see what is it that you're working with, and then I'm going to share it with you, and then take your ideas that you think are happening within your business in a lot of different areas and map them with the reality, and actually look at them all the time. Data is not about looking at it one time. Data is about doing it all the time.
and share the lessons that we had across the time. I'm trying to figure out who I'm speaking with. So who's doing analytics? Who's working with data on a daily basis? Can you raise your hand? OK. And then who are the CFOs in here, maybe? Or CEOs and founders? Who's everyone else? Is it? CROs. OK, good. OK.
So this is the team growth, specifically when we were starting to tap into data and pushing the gas in the last few years. And this is the evaluation growth. And I'll take it to my first part. My data path, I'm an engineer. I was writing software. I was super happy until the time when it was the time to raise people's salaries. And I needed to know our revenue for sure.
At that time, we were a consultancy company. So our revenue was supposed to be time multiplied by rate equals revenue. And what happened is that I multiplied time by rate. It didn't equal revenue. We were like $120,000 short. And there was a lot back then in the time when we started. We were a much smaller company. So I looked into the numbers, and I was trying to figure out what happened.
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Chapter 2: How did the company grow its revenue significantly?
And so those three things kind of pushed me into a direction that you need to become a spider of what is it that you're doing, and you need to understand how your data works. And we'll go into it in a sec. And then the art of how to manage the data, because it's great when you see all those dashboards, but where do they come from? How do you build it, right? And we're going to go into it.
So this is the payroll nightmare that happened. She was spending like 12 hours per person, and then after the automation, it dropped into like five and three hours per person. Now we're a much bigger company. We're just leveraging our automation much better. Basically, I did tell you about the $120K deal that pushed into data.
So everyone has those horror cases where you actually understand that you're going into a very different direction than you were supposed to be going. And then basically, from that moment on, it's better to create your own data. We'll jump into that. So basically, P&L is a very interesting case. Our P&L was spread between the payroll.
We had multiple entities in multiple countries, multiple accounting software. We were transitioning from one software to the other. And it pays off to combine all of it together, unify it, and to create yourself a data that you can trust in. Most of that is being done with a data analyst, of course.
And build a foundation model with which you can reflect of what's happening within your organization. And have an ability to drill down and actually see kind of the insights of where you're at. So this is something I looked at every day. This dashboard is basically, it contains everything that I, as a CEO, look at. And this is the dashboard that I built for myself.
This is my spider web that I gained control through. It's a spreadsheet. And it gives you flexibility to you understand your own data. Everyone can do spreadsheets on some level. And you can basically create a base model for what can be useful for you, instead of just working with analysts. And we'll go into that in a sec. This is like a typical setup that I have where there's a lot of monitors.
You can step back. And then you can see the correlations of what's happening within your business around multiple dashboards of them. But the main one, you should have your main one that you refresh every day. And then you'll get a sense of it. And there's some more stuff, as I said. We have 600 dashboards.
I didn't have 600 slides, but one of them is the employee satisfaction, and the other one is time tracking and basically what is the quality of time tracking in the company. How to become a spider of your own data. So whatever you think is important to you, you can break it down using extraction tools. And you have to place it in front of you.
You need a place where your reality, the reality of your world, is going to reflect it using some hopefully color-coded data. And it's actually very easy to start. It's much easier to start than everyone realizes. And you should always redesign your data for your visibility.
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