SaaS Interviews with CEOs, Startups, Founders
1639 How This Company Hit $10M Valuation Serving Real Estate Value Investors
19 Jan 2020
Chapter 1: What is Enodo and how does it serve real estate investors?
Launched the company back in 2016. Again, Enodo Inc. Again, helping people understand if you've replaced the granite countertop, how much more can you charge in terms of rent per month? He's got about, call it 220 customers. Actually more than that, call it 400, 500, just at different price points, but just past a million bucks in terms of run rate. They're looking to continue to scale.
Currently burning about 50 grand a month, 2.2 million raised, about to raise another 1.2 million on a 10 million pre-priced round. 11 folks on the team, again, helping to drive for its profitability now, all based in Chicago. 5% revenue churn per month, so about 95% net retention, spending $2,400 to get a new customer, so about a six-month payback period.
Again, as they look to increase the amount of rent rolls, TTIs, and additional property data uploaded to their platform every single month. Hello, everybody. My guest today is Mark Rutzen. He's the co-founder and CEO of Anodo, an automated underwriting platform for multifamily real estate.
The company helps users analyze more deals in less time and make better investment decisions backed by data science. Utilizing machine learning, the platform collects, cleans, and analyzes real-time multifamily rent and availability data from over 2 million properties nationwide. Mark, are you ready to take us to the top?
I'm ready.
All right. So interesting tool here. Help us understand maybe a specific use case of a customer using your tool and then what your revenue model is. Is it a pure play SaaS company?
Yeah. So we are a pure play SaaS company. Okay. So what we do, if there's a value-add investor who's looking to tell, you know, if I'm going to do a value-add, what happens if I add the granite countertops, the hardwood floors, the roof deck? What if I do this to a multifamily property? What am I going to be able to get and rent for each of those improvements?
The simplest use case is a value-add investor can put in their deal parameters, toggle those amenities on and off, and actually see what rent lift they'll get before they spend money on those improvements using our machine learning algorithm.
Okay. And what would a value investor like this pay on average, maybe per month or per year to get access to this technology you've built?
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Chapter 2: How does machine learning enhance Enodo's platform?
That's the best data that we can get. You're incentivized when you use the platform to upload your property management from your property management software, your data, so you can model and analyze your portfolio. So users do that. They upload rent rolls and T12s, which we get direct from the source.
We have partnerships with major national lending groups or lenders that feed data in bulk into the platform. And then we get it from anything that we publicly available. And there's about 30 different sources we go to nationwide to get listing data, to get demographic and economic data, demand driver data. All of this feeds into the platform and helps make a better and more informed model.
It used to be like MLS listings that list like the current rent roll on property, things like that.
Oh yeah. So we'll use what's publicly available is harder. You got to clean it a lot, but the best data we get is straight from the source. When'd you launch the company? What year? 2016 was when we announced we were going to do something. It was 2016. I got in front of an OpTech audience and I was like, We're going to quantify amenity values. We're going to predict rents.
We're going to do all this. And they're like, have you done it yet? No. That was kind of the opposite of a stealth launch, I would say. And then we slowly built it over the next year and a half. And then we actually released for subscriptions in 2018, so January. And we've been growing like crazy since then.
That's great. So how many customers have they scaled to today?
Like five or 600 between five and 600. Okay. And that's a little bit cause we had Columbia university's whole class sign up and there's 155 there. So they're all paying 400 bucks a month. No, I wish that would be great. We give them nice to that class. The user base is a little bit lower than that of like standard users.
I would say, can we just take a hundred bucks, a hundred, a hundred, so maybe 400 actually paying full price.
Right.
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Chapter 3: What is the revenue model of Enodo and how much do customers pay?
What you would say is your, your, your average price point that you already gave me is accurate at 400 bucks a month. However, the 400 or 500 customer count is not accurate because they haven't all closed yet.
It's a, it's a little bit skewed because some of them are in pilots where it's like a fixed price and then you get more licenses. Uh, and some of them are university. Well, one is a university contract that is pretty big at 155 at a steep discount. Sure. That's for my alma mater. So I gave him a good deal.
That's, that's very nice of you. Did they give you a deal on, on tuition? No, that's, they're slowly. Come on. You left some money on the table there.
Yeah. Right.
Yeah.
All right. Very good. So walk me through, you're currently at a million bucks about in kind of ARR. You were at nothing about a year ago because you just started selling in January. How'd you get the first customer? Tell us that story.
So we had been talking with people that were interested. We put a beta list out and people could sign up for beta about a year before we actually launched the product for subscription. So we had a good database. We had 300 people that had inquired and said they were interested. It was easy to go to that list afterward.
And then, you know, we started cold outreach and we had at the same time people, new people coming in. And, you know, some of our first big players were just people that had been following it since I made that initial announcement. And it was pretty easy because they kind of knew what we were doing.
That's the benefit of a non-self launch, I guess, is that we had already proven ourselves by doing different studies, different industry publications. I've been in the news all over the place, presented at Harvard, MIT. We did a study with the National Apartment Association on amenity pricing.
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Chapter 4: How did Enodo acquire its first customers?
So it's about 2,400 bucks in terms of spend. And where are you spending that typically?
Uh, it's so we've got commission. We pay to our salespeople, uh, 15%, which is a pretty nice commission, uh, because we built it as a thought leader. We don't have a huge advertising cost. And we've actually, we ramped it up and then we reduced it because we found that it didn't improve a whole lot to spend 10 grand versus five grand. It didn't make a huge difference.
So we're at about a five grand marketing budget spend right now. And we track them from the time they get to our site and all the sales interactions to the time when they actually close. And all in takes us about six months to pay it off.
Fortunately, our first round of customers, the people we signed up that were kind of in our soft launch, which was like end of October last year, these were investors and people that were like evangelists of the product. All of them renewed. So we're in pretty good shape so far as far as those annual renewals.
Now we're going to see in January when we start really picking up those annual contracts, how many of those renew. But I think we'll be doing pretty good. Mark?
You mentioned a part of the value of this is you're getting data from first party source, which is essentially your customers. You could argue then the first customer got the least value because they weren't getting anyone else's data. They were just essentially uploading their own. What did that first customer see when they logged on if there was no other data except their own to see?
Oh, we use public data. We use publicly available data. So they had just more was publicly available. More was from property websites or listing sites or open data portals. Now, as time goes on, we're getting more and more of that coming from the users themselves. So the predictions get better. That's the big thing you see is the predictions get better over time.
So before there was a wider margin of error on amenity predictions. So you'd see, you know, amenity is $20 plus $20, but plus or minus $10, right? So, oh, that's, you know, it's a prediction, but it's not as reliable. Now it's plus or minus $3.
Because you have more customers uploading their data.
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Chapter 5: What challenges does Enodo face with customer retention?
Number one, what's your favorite business book?
I'd say the Challenger sale is my recent favorite, but the Lean Startup is my all-time favorite.
Number two, is there a CEO you're following or studying right now?
Right now... Shoot, there's a lot. Not in particular. I've been reading a lot of business books lately, so I haven't been following anyone in particular. I guess Travis Kalanick is kind of interesting right now because the whole thing he's doing with Cloud Kitchens, that startup that's investing in stagnant retail space.
I think that's kind of interesting, but I wouldn't say I'm following him necessarily. Got it. Number three, what's your favorite online tool for building a company?
I'd say Slack is incredible, and HubSpot is more incredible, though.
I'd have to give you HubSpot. Number four, how many hours of sleep do you get every night?
I try for six. What do you get, though? I have a daughter at home, so I probably get five to six. Okay, five.
And daughter married or single?
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