How Enodo CEO Marc Rutzen grew Enodo to $288K revenue and 220 customers in 2023.

hello everybody my guest today is mark rudson he's the co-founder and ceo of enodo 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 multi-family 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 um 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 peer play sas company yeah so uh we are a pure play sas company um so what we do uh 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 multi-family property what am i going to be able to get and rent for each of those improvements the simplest use case is evaluated investor can put in their their deal parameters toggle those amenities on and off and actually see what rent lift they'll get before they spend money in 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 the top tier is 500 a month and the bottom tier is 100 a month and then we'll get a middle 300. so would you say like a fair average is like 200 something like that i'd say actually more lean toward the 500 okay okay cool historical data that are included there good then we'll go between you know 300 and 500 there and say call it 400. that's helpful to understand let me ask you when a value-add investor does something like this i mean i assume these renovations are maybe like one time i mean what makes them keep paying you when they're not doing a renovation yeah so we actually help people source deals so people are constantly trying to figure out if a deal is going to be a good undervalued play or not and what our platform allows you to do is very quickly load a rent roll into the platform it'll parse it and analyze and see if your rents are at market or if there's upside load your t12 your uh trailing operating expenses it will load it into the platform very quickly and then compare to market benchmarks and tell you if your expenses are above or below market and then ultimately in just a few minutes you've actually got a full underwriting model of the plat of the deal and you can see if it's you know a good deal or not it's that whole adage that you got to look at a hundred properties make offers on 10 and then ultimately close on one we help you do that hundred properties part in about a tenth of the time it would typically take any tool that boasts machine learning or ai generally is only as good as the inputs that the founder has you've been clever enough to put into the system so how clever have you been so we get data directly from the source that's the best data that we can get is 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 uh so users do that they upload rent roles in t12s which we get direct from the source we have partnerships with major national lending groups our lenders that uh that feed data in bulk into the platform and then we get it from anything 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 this feeds into the platform and helps make a better and more informed model these could be like mls listings that list like the current rent roll on properties things like that oh yeah so we'll use what's publicly available um is harder you got to clean it a lot but the best data we get is straight from the source yeah when did you launch the company what year 2016 was when we announced we were going to do something 2016. i got in front of a op tech 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 like have you done it yet no that was a it was kind of a uh 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 for subscriptions in 2018 so january uh and we've been growing like crazy since then that's that's great so how many customers have they scaled to today we've got like five or six hundred between five and six hundred okay that's a little bit because we had columbia university's whole class sign up and there's 155 there so they're all paying five 400 bucks a month no i wish yeah that would be great we'll give you a nice dis for that class the the user base is a little bit lower than that of like standard users i would say can we just take 100 100 100 so maybe 400 actually paying full price right yeah yeah can i do that math mark can i take 400 times a 400 a month price point you're doing about 160 grand a month right now uh not quite because we got a lot more pilot programs too uh i i wish it was that high um we're closer to a million arr right now okay that's great so would you so let me just repeat that back to you what you would say is 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 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 give them a good deal that's that's very nice of you do 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 all right very good so walk me through you're currently 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 did you get the first customer tell us that story uh so we uh 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 we started cold outreach and we had at the same time people uh new people coming in and uh you know some of our first big players uh 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 than you that's the benefit of a non-stealth launch i guess is that we had already proven ourselves by doing different studies different industry publications i've been in the news all all over the place presented at harvard mit we did a study with the national apartment association on amenity price so you basically became a thought leader in this first and then you slowly built like tech to help you put out reports and then you made the tech basically available for sale yeah smart i like it very good um talk to me about kind of how you've done this right so have you bootstrapped the company or raised no we raised uh 2.2 million uh in two it's it's one round technically but it's um it was over the course of two years so we raised the first we started in like november 2016 and we got like our first deposit of like 25k over the course of the next year we raised the first 1.2 million and it was gradual it was mostly friends and family and then you'd be shocked how easy it is after you actually have a product we were able to raise the second million like in no time at all or those those investors that came in later though obviously they were investing in a company that had less risk because you had sales did they get the same terms though as the early investors uh i wish we would have done it differently uh at times but yes they did yeah the same because it was one round by the way this is very typical no one ever talks about this but it's very typical for this to happen it's essentially called letting the round roll right and you let it roll and you hit your cap did you cap it at 2.2 yeah we capped it yeah and then 20 discount and 5 million value cap on that yeah okay so it's a note yeah that's great what are you at today in terms of team uh we got 11 on the team right now all in chicago yeah we're all in chicago um so we're right west of the river um right by communion station actually that's good mostly developers we got eight developers and the business people are doing what we do that's great you know it turns critical on any sas company right so what's your turn today and how do you keep it low we're a little under five percent and uh at five percent per month uh five yes five percent uh so it's mostly its annual contracts for the most part um on our annuals obviously is it's the first year so we haven't had a ton of churn there um well what's...