How Dar.Win CEO Frank Mcdermott grew Dar.Win to $432K revenue and 36 customers in 2018.

hello everybody my guest today is frank mcdermott he is an entrepreneur and technologist launched hundreds of sites 50 plus apps tens of digital products launched nine businesses four for himself and delivered a line of high-end beverages to the market and consulted in the finance and tech industries a lot of experiences now building a company called darwin that's dar.win frank are you ready to take us to the top i am how the hell do you decide what to focus on yeah uh you know it's been a very interesting career and i've had a lot of exciting opportunities the kind of threat i found is just being able to bring products to market in different industries different technologies and we've gotten really good at that so when we decided to start darwin we really kind of had a core competency in e-commerce we had core competency and technology and cloud technologies and we wanted to try something really new which was bringing machine learning into a production environment uh and so that's kind of where we decided to uh start to ideate on what could we do with that so tell us what the company does sure so darwin is a suite of products and services that uh essentially provide personalization for e-commerce so you install darwin on your store and we instantly start tracking collecting information about the people who visit your site the things that they look at the things that they click on the things that they buy and put that all into our machine learning models to start to segment that audience and then allow you to start to make product recommendations and predictions and what they're going to want to be interested in what they're going to want to buy we take that information and allow the people to segment into doing e-marketing social marketing and being able to drive those specific segments to specific suites of products okay and name some of the data inputs i mean so let's let's is like h m a customer or someone like an h m a big retailer with an e-commerce site it would be someone like a big e-commerce site uh the way garland works is that you know we can scale and have as long as you have a couple of products and a single visitor on your site we can make a prediction but really the way machine learning works is the more uh information you have the more training data you have the better your predictions are going to get and what darwin does is actually retrain itself with new testing data every week to kind of accommodate new people that are coming to your store and uh different types of visitors that are going to be uh you know coming over a period of your season or your year whatever you're doing that's what i ask actually right so like when you talk about a prediction like give me a prediction that darwin made that a company listened to made changes and saw more revenue because of it well it's really at the consumer level so say the analogy that we use say you're rei for example you might have a customer that's a hiker you might have a customer that's a climber if a person starts looking at hiking stuff there's a propensity they're going to want to see more hiking gear and their recommendations and where they see different products and where you're going to be able to advertise different types of products that are associated with both the one you're looking at and ones that they viewed over time and then you take that to the next level and say maybe that sometime time there's another similar type of visitor in the future we can catch that propensity earlier and start to make more refined predictions now if where darwin really excels is that that person is both a hiker and a climber over time there's going to be a hybrid of products that we're going to be able to make recommendations on it's not the static linear model of making a recommendation based upon category or type yeah and then are you is your business model a pure play sas business uh it's a little bit right now it's both a combination of software and dedicated service i think we have a core competency in e-commerce and a lot of our customers as they experience and try to implement machine learning um you know we really need to kind of get hands-on a little bit um to both understand their business and how it works and how their data works but then also how we can best implement darwin within their organization so if you i mean if you look at revenue over the past 12 months and as a big pie what percentage of that was pure sas versus kind of you consulting and helping out professional services as far as revenue goes right now it's all sas based oh it is all sas yeah so we're providing essentially we're kind of in our our what i'll call our secondary beta alpha or kind of more like production at the same time like we still need a lot of uh road map to kind of grow so uh and as far as this data what types of customers are getting and what their interests are how their customers behave this all works with data so we really have to be open about who we're working with and the types of information that we're getting from them once we start to get to a point where we can really um dial that in and and be able to implement different services more quickly um that's where i think we're going to really start to kind of ramp things up so right now we're working you know pretty much for free for all of our customers what the people that are paying on average what are they paying per month or per year to get access to this tech sure on the low end uh you know a base price on shopify as an introductory price is 29.95 a month but we have customers paying in thousands really depending upon the types of volume that we're getting and the types of predictions that we need would you say that's more of a fair average like a thousand bucks a month something like that absolutely and really when we come to actually getting high performance out of this system and you're gonna be in tens or you know hundreds of thousands of dollars a month uh with the hopes that you know you don't have people at that scale yet do you hundreds of thousands a month no yeah yeah but that's the goal obviously absolutely yeah that's great when did you launch the company what year uh we started we wrote our first line of code in june of 2016 and uh we went uh in beta with our first customer in july of 2017. we ran them six-month beta there and then we launched in the shopify app store in november or to be september of 2017 uh and we now have an sdk where we're watching uh through other different e-commerce platforms uh as of november of this year and how many customers have you scaled to uh we're in the hundreds now okay all kind of free work or most of those are paid that's a combination okay so if we just look at people that have moved from like free kind of pilot you like hustling to actually paying for the tech plus you're helping them i mean how many are paying a couple dozen couple dozen yeah okay that's great how have you so walk me through kind of how you funded this right so did you decide to bootstrap the company or have you raised capital uh no we bootstrap it so i run a software development company and we essentially build software products for people and we've been doing that for 10 years called caravan interactive through that we have a propensity and kind of core competency and bringing software products to market and we've managed hundreds of websites hundreds of applications and we built them for startups for big brands et cetera uh time to get in the game uh the analogy i've always used is we sold shovels to miners but now we decided to get byron shovel what's the team size look like today solely dedicated to darwin uh we have four people slowly dedicated okay that's great so four people and they're all based in new york uh yes they're not here in brooklyn there's two in poughkeepsie and one in manhattan that's great and are you i imagine it's probably too early i mean you're not looking at things like cac and churn and things like that yet right i mean we have a different an understanding of what that is and we are now in the process of trying to investigate why that is uh you know on the shopify platform you can install an app and uninstall an app in under two minutes so there's no like good information awarding there but whereas in our other customers in our existing um other e-commerce platforms we have a good uh really long age so we've had the customers now for almost two years um and so it's hard to tell on darwin or something else on darwin i thought you said on darwin the first really paid people was just about a year ago sorry yeah june 2017 you know it's a year and a half i guess okay god i got it got it um no no that's helpful i just want to say like you're you're basically in the stages now where you're really you're still tinkering you're figuring some stuff out you're not like actively trying to optimize for a cac payback period or something like that correct yeah and now in terms of scale you mentioned kind of thousand bucks a month is a good kind of average and a couple dozen maybe caught 30 sixes i mean is it fair to say you're kind of in that range of revenue 30ish grand a month yeah that's good and zero a year ago right correct and what do you hope to scale to by the end of next year you know uh it's a little tough to tell um our sales cycle we're still kind of figuring out sometimes it's six months sometimes it's going to be you know quite a bit longer so i feel like what we're...