SaaS Interviews with CEOs, Startups, Founders
1334 This $600k+ ARR SaaS Helps Oil and Gas Companies Predict Oil Well Production
20 Mar 2019
Chapter 1: What is the main focus of Luther Birdzell's company?
founded and got their first customer in 2017. They've raised less than $3 million currently doing about 50 north of 50 grand per month in revenue that's doing you know, between five and 10 customers who pay on average between 10 and 50 grand per month.
It's too early to talk about things like like churn they have less than a year kind of cohort there but scaling quickly 20 folks on the team based between Houston and other remote locations around the country. willing to spend on the low end, you know, five grand all the way up to 50 grand in CAC. Payback period there is obviously fairly rapid with 10 to 50K per month contract values.
This is the Top Entrepreneurs Podcast, where founders share how they started their companies and got filthy rich or crash and burn. Each episode features revenue numbers, customer counts, and other insider information that creates business news headlines. We went from a couple of hundred thousand dollars to 2.7 million. I had no money when I started the company.
It was $160 million, which is the size of many IPOs.
We're a bit strapped. We have like 22,000 customers. With over 5 million downloads in a very short amount of time, major outlets like Inc. are calling us the fastest growing business show on iTunes. I'm your host, Nathan Latka, and here's today's episode.
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Chapter 2: How did Luther Birdzell transition from R&D to market?
Hello, everyone. My guest today is Luther Birdzell. He's an entrepreneur, data scientist, and engineer passionate about energy efficiency, AI, and self-service machine learning. From boardrooms to oil fields to data centers, he's been building tools that transform data from cost to asset for over 20 years.
He founded Oil & Gas Analytics in 2013 to build an AI and self-service machine learning platform for the oil and gas industry. Luther, are you ready to take us to the top? I am. All right. So oil and gas plus software. Typically, you don't hear these things combining, which means maybe you found a gold mine here. So what's the company doing? How do you make money?
So just real quick, my background is electrical engineering, as you noted. And for the past 20 years, I've been building software to make data more valuable to subject matter experts. Coming out of my last company in 2013,
Chapter 3: What is the revenue model for the SaaS platform?
it was very clear to me that AI and machine learning were the most valuable technologies for increasing the value of existing data. Uh, oil and gas was the fastest growing area of the U S economy at the time. Oh, uh, the oil and gas industry was the fastest growing area. What year was that? Oh, excuse me, 2013. So, um, you know, quickly came into focus.
So that, that helped bring the op, those just kind of high level economics helped bring this opportunity into focus and, As we started to peel back the onion a little bit, well-planning optimization and forecasting the wells pre-drill was the area that we thought we could affect the most change, the most benefit with AI and machine learning.
And if we look at the oil and gas industry, Nathan, about $500 billion of cash is spent every year in the upstream part of oil and gas.
Betting on wells that hopefully succeed.
Right. And 90% of that $500 billion is spent 30 days pre-drill to 30 days post-drill. So it's essentially, you know, it's the planning and the execution of these wells. And then the remaining 10% is spent over the remaining 20 to 30 year life of the well on various things with production.
So we started in the most capitally intensive part of a very capitally intensive industry and are consistently finding over 10% optimization opportunities in less than three months with the companies we're working with.
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Chapter 4: How does AI optimize oil well production in the industry?
So on a per well basis, these are ways to change the wells uh, to either reduce costs and get the same amount of oil out or keep costs the same and get more oil out. And we're identifying insights to save a five to 10% of costs per well, um, which is, you know, 400 to $800,000, 400,000 to a million dollars per well. Um, and it's, there's a lot of capital we can, uh, yeah.
So Luther, let me, let me dive, let me dive in here. So is there, are you installing any hardware that you own at, at each of these sites? No. Okay. You're just essentially analyzing data that's already captured, applying some pattern matching to it. AI machine learning to better forecast.
Yeah. So, so our tool, Nathan, our product, the insights workflow is cloud-based data management, advanced analytics, uh,
and insight visualization that was purpose-built for geologists, geophysicists, and petroleum engineers to be able to reduce data management time from days to minutes, to be able to create world-class machine learning models in less than five minutes without having to write a single line of code, and then to be able to challenge and understand those machine learning models and kind of the computer's analysis
through the lens of physics and their experience, that's critical for industries like oil and gas, and then to be able to visualize those insights in a form that directly ties to improved capital allocation in these very large drilling programs.
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Chapter 5: What challenges does the oil and gas industry face with data?
Got it. Yep, no, okay, good. I understand the product now. Give me a sense of your revenue model. How do you make money? So we're a SaaS model.
Okay.
Monthly fee, pure play?
Monthly fee to access our web-based software. For our customers, there's no hardware to buy. There's no new people to hire. No software to install and maintain on the desktop.
What's the average customer paying per month? So I can get a sense of your kind of, you know, mid-market or enterprise.
So we're working with companies.
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Chapter 6: How does the company ensure customer retention?
I'm going to answer a different question first. We're working with companies that don't have any drilling rigs yet. So these are, you know, PE-backed kind of startup oil companies that are looking for land. to companies running one to two drilling rigs, you know, small programs, all the way up to some of the largest independents that are running over 20 drilling rigs across multiple basins.
So our software scales and across that, you know, really almost the whole spectrum of the market, you know, those monthly fees range, you know, the monthly licensing costs It ranges from about 10 to 50K a month.
Okay, got it. And so here's a question I've got for you, right? Once they drill the thing, why do they keep, I mean, do you have a churn problem? Can't they stop using you once they solve the 30 days before drilling and 30 days after drilling and you do your, the biggest kind of impact you have is in that period?
Well, optimization is continuous improvement. So we drill, we learn more from the data we already have. Then we go do some things differently. The only thing you're guaranteed, Nathan, when you drill an oil well is data.
Yeah, my point is, though, if someone drills them and they don't drill more, do they still have a use for your platform?
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Chapter 7: What strategies are in place for scaling the business?
So, no, they'd be out of business.
So they have to keep drilling in order to, well, they wouldn't necessarily be out of business, right? They can go down one drill and drill that for a year or two, right?
Yeah.
That's not really the way the industry works because of the way the land leases are set up. You have to continue to drill to – or otherwise your leases become – get invalidated.
Okay.
So if I'm leasing land to you and you just drill one well, well, there's all that revenue. I only get the – that lease is based on the revenue generated by the wells in that lease. So as the leaseholder or as the lease owner, the landowner, when I lease, I want to make sure that you're going to drill enough wells – for this to make economic sense for me, the landowner.
Okay, so what is that number? What does your churn look like today?
So churn, I mean, we're defining churn as people who've signed up, who then cancel subscriptions.
Yeah, well, I mean, these are like SaaS economics, right?
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Chapter 8: What insights does Luther share about future growth plans?
Churn is one of the bigger ones, right? Which is, again, they're paying today, they don't pay tomorrow, right? This month versus next month.
Yeah, no, that's fair. Yeah. Of the companies who we have signed up in annual contracts, all of them are still in those annual contracts.
And what has there been a renewal period yet? Are they still less than 12 months old?
No, we have, they're still at the, we're getting to our, we're approaching our first renewals and it's looking very good.
That's great. So when, when did you launch the company? Was it less than a year ago?
So we launched our go to market in early 2007, Q1 of 2017. Um, you know, we did a couple of years of R and D and then through the downturn, um, Things just slowed down. So we continued to focus on R&D and improving and keeping things alive. And we went to market in the beginning of 2017, signed up our first three customers by the middle of 17. And we're progressing forward there.
And what are you adding out today in terms of total customers?
Uh, we're still a private company, Nathan. It's, uh, we're, we're between, we're between five and 10.
Okay. Got it. So again, this is very much like a high touch model. These is, this isn't like a high volume, no touch model. So it's, you're having to get on a call. I mean, this is a sales process. It's, it's not like they just go to a website. They never talked to anybody and they start paying you 10 grand a month.
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