How Celect CEO John Andrews grew to $5.8M revenue and 16 customers in 2017.

hello everyone my guest today is John Andrews he spent the last two decades helping retailers distributors and brands optimize their omni-channel strategies and operations before his current company's select John was VP of product marketing strategy for Oracle commerce coming to Oracle via the indica acquisition where John was VP of marketing and product management he started his career with Deloitte and consulting strategy operations practice and he holds a BA in economics and computer science from Boston College and received his master's degree from the Harvard Business School John are you ready to take it to the top that sounds great all right so tell us about select what are you doing what's your business model how do you make money right so we are at the highest level where predictive analytics technology company we are focused primarily on retail the company was actually founded out of MIT by a couple of MIT professors we'd been collaborating on a lot of research for the better part of the last decade specifically around this idea of understanding customer choice which I'm happy to give a little bit of detail on but the the main area that we're focused on with retailers is around inventory optimization so helping retailers optimize inventory optimize turns reduce stock outs reduce markdowns etc if you think about inventory right it's been the largest number on a retailers balance sheet it's also at the end of the day the the most important thing a retailer has to figure out right what products to bring into inventory how much of them in what assortment and then how to allocate them to all their customer touch points in terms of stores fulfillment centers to get them shipped out to customers if you can optimize that so that you've got the right products at the right place at the right time you're gonna make a lot more money really really in in in retail right now yeah that's a that's a that's an important thing yeah I mean look I have always wondered how these retail entrepreneurs figure out not only what style of things to carry but then multiply by another factor of complication because you have sizes right different sizes different colors I mean you have literally infinite choices that is exactly the kind of thing I don't want to be doing because I like simplicity so tell us how you mean can you give us examples or you're working with and how you help them double down on what's working through the data you provided yes yeah absolutely so the the process that we help our customers with right is kind of through the the merchandising and planning process and into the supply chain process right so through kind of a you can think of the inventory optimization cycle as kind of a plan buy allocate and fulfill process right on the planning side we're helping our customers and our customers include folks like Urban Outfitters Anthropologie Free People aldo of montreal up in montreal the shoe manufacturer designer and and and retailer Saks Fifth Avenue etc and if you think about that if you think about that process on the planning side right what we're helping retailers figure out is very specific as they're making their strategic planning decisions if they're figuring out how much inventory dollars to put towards you know following the trends and understand where it understanding where the demand is right understanding should I be going bigger into women's clothes versus men's clothes at the macro level and then figuring out you know something just as specific as attributes of specific items for shoes should I be going with riding boots versus ballet slippers and what colors are trending etc there's you know as you mentioned a minute ago the combinatorial explosion of all of the different decisions that need to be made and the attributes associated with that in terms of you know trends changing the amounts that need they need to spend it's incredibly complex the way that these decisions are made today is generally based on gut instinct and Excel spreadsheets right and you know our tagline is bringing science to the art of retail it very much still as an art form especially with fashion retails which is the area that we've had the most success it's on how are you making the brain smarter there right so your your system your engine what you are paying for is only as good as how well you've trained it and maybe measured by number of lines of data you've put through it right how are you training this thing so it's a good question there's it there's a few elements here right when people think about training one of the things they think about is is machine learning right in terms of you know that the data that you're bringing through and how are you how are you optimizing that model one of the things that we're doing is understanding it sure you know you can you can look at a product that you're looking to sell and train the model to look at images of other products you sold or other data right so kind of come up with a model that's important but more specifically it's getting through a deeper level of detail in terms of understanding customer choice and customer preference so let me give you a specific example right a customer walks into a store and they buy they buy a product right great every retailer is going to use that data and that information that transaction level data to optimize their operations in some way right but what if you knew in addition to what that customer bought what if you also knew it was available to them when they made that selection right what if you know what their options were when they made that choice said another day what what if you knew what they didn't buy in addition to what they bought right I now have a sense of the customer preference I have a sense of what their intent was when they made that when they made that selection and when you look at every single interaction with a customer when you look at the the intent when they choose one product over another and you can do that by understanding you know what was in inventory in a particular store you can do that by looking at browse information if you go to a retailer's website and you look at five product detail pages but then you only put two of them in your shopping cart that's context we're now able to build up a model a pretty robust model we call our our choice engine right is a choice model that allows us to then answer the question not just what did somebody buy but what would they have preferred to buy if given the choice across an assortment of products all right because what's happened in the past isn't necessarily wasn't optimized right maybe you didn't have the the the best assortment in front of them right I bought a purple button-down shirt but maybe the blue one wasn't available and I would have bought that if it if it was part of the overall assortment so being able to normalize against that and then build out that model now you're now you have to as you start to bring in new products into the mix something that you've never you're designing you've never sold before that's where then the machine learning comes into play to say okay help me build a model of this product that I can then bounce against my choice model to I diamond to identify what that demand is going to look like when you do that right we've seen customers with anywhere from you know that points 5 to 7 percent increase in revenue to you know afterwards of you know 13 to 14 percent increase in gross margin yeah I mean look many I mean you sang all this I can't help but think and I'm sure people listening or thinking yeah this is why Amazon is so big they have the best data collection engine anywhere and they can make it smarter than anybody I mean you even mentioned you gave the example you like product pages because you can see what was viewed you can't necessarily see that in the store you know was there and you know what I bought and didn't buy but you don't know if I pick something up and put it back down or I mean I don't think you do unless you have trackers and things in every single store ya know now the technology is getting better where there are in-store sensors there's RFID where you can see what goes into the dressing room what you know what comes out and then what goes up and people buy the reality is nathan is that you don't actually need that level of granularity to get the signal out of understanding customer preference right part of the part of the technology you need to identify what the selection set is that people are low likely looking at just by understanding what was in inventory and then what the customer bought gives us significantly more signal than just the transaction level information of what a customer bought now you you brought up Amazon right and everybody in the retail perspectives looking at Amazon in terms of what they're doing one of the benefits that Amazon has is just an enormous amount of data yeah the challenge that other retailers have even though they feel as though they have a lot of data the issue is is that they actually has very sparse data about an individual customer and individual products and specifically what those customers interacting with those products right so the reality is they need to be able to pull signal out of what is actually very sparse data and that's one of the things that makes this extremely hard to kind of pull that signal out where that idea of understanding choice becomes becomes critical I get it yeah what's the business model how do you make money so we're a SAS based subscription model what we do is we take a customer's data we run it through our engine and then we expose that information via a web-based interface that customers can interact with on real time okay so I'm mentioning earlier from you know the different solutions that I talked about on our own plan optimization by optimization the the interaction on the plan side is it's it's very interactive customers are doing optimization based on their constraints in terms of how much money they have for inventory how much space they have in a store what's John what's the I don't mean to cut you off but I want to get more of your story out before we have to wrap up give me a sense of customer size in terms of what are they paying you usually on average I mean are we talking like 20 million dollars or a thousand or yeah yeah it's it's generally so so the model is when we start working with the customer they'll start with a specific solution and focus initially in a specific category area so as an example they're stopped they'll start with in the women's shoes department right or the men's you know all men's apparel and that you know the starting point is going to be somewhere between you know four hundred to call it 400 to 500 k okay right now it's per year got it right based on you know and there's you know varying based on the customer size the amount of SKUs that don't have a good average right yeah now then it will grow from there as they then expand a usage across different categories and if they grow the across different customer across different products the solution area yeah so how do you probably have a pretty predictable model in terms of a year one contract value will definitely grow by you know call it you know forty percent in year two and another 30 percent in year three etc I mean what are these people ultimately worth to you what's the lifetime value of customer do you model millions of dollars it's generally in the 3 to 4 million dollar range yep now do you have anything I mean when I don't know if you're a high-touch right now or not but do you have enough of a cohort to be looking at and do you care about things like churn absolutely care about things things in terms of current we don't have because our because our our customers are generally much larger in contract size I mean those contract dollars are it's a good it's a good percentage of the customers of a customer's spend right as they start to look at their spend so it's not we don't have the churn that we have had have been retailers that have been struggling financially total I mean that's I mean that's your biggest threat right I mean not exactly honestly that's why I'm guessing here correct me if I'm wrong I'm just being arrogant but that's why fashion is your number one place because it's very hard for an Amazon to kill fashion brands when it's highly personable yeah no absolutely and frankly we have a number of customers who have recently restructured and come out of bankruptcy right and are turning things around and absolutely you know using using the insights that we're helping provide them to do that better Chuck John I would take all that credit if I was you JC anybody come into bankruptcy it's John's it's select it has to be select amen all right John what year was the company founded in companies founded in 2013 okay and you mentioned it kids kind of spun out or something at MIT mean were you there on the founding team or these professors brought you in after they got initial-scale so I came in right right as we were looking at our series a financing right so so basically the the two professor is insanely smart guys had a couple of young developers working with them built up you know beta beta product had a couple of data customers and then I joined on in this the middle of 2014 basically just about a year later and that's when we started scaling out the business how much total have you raised we've raised a total of fifteen million okay yeah one fine now was was that series eight were you an e I are at the VC that led and it was contingent on you joining her no no I wasn't no I was not in not any IR when right when I came on is basically the the the lead for that series a me interviewing them them interviewing me and yeah so the timing coincided there and what do you I now today in terms of team size and is everyone based up there in Boston the majority of the team is based here in Boston we've got about 55 people at this point we have a handful of folks out in the West Coast you've got a couple of folks in a couple of folks in New York as well and then a few sales folks scattered geographically across the US and at all us-based okay and over the past kind of caught three and a half four years what do you guys scaled to in terms of total customers using you so we're in the the middle teens at this point they go to it's very much it's very much a high-touch kind of high contract value model versus the opposite low ARPU high-volume exactly got it so we'll call between 10 and 20 customers enterprise counts yeah okay very fair what is churn right now annually so we've had two customers we've turned over the past four years yeah I always wonder I mean maybe we can educate me here because you're in it I always wonder how companies like yours with so few customers how you actually measure a turn because if you lose one it's a big impact on the business yeah it's it's a really hard thing to it's a really hard thing to to measure right if our model was you know hey download something from you know from our website put your credit card in we've got thousands of customers you can measure based on cohorts much much easier we we it's an enterprise sales model right it is and our revenue is is much it's much lumpier than I would like it to be right but when you're looking at the dollar amounts that were that we that were able to get from customers based on the value that we're providing to them and the high-touch in terms of kind of working closely with them as they get up and running and helping enable their teams right there's a you know there's a lot of value there but it's it's it's very difficult to just kind of have the dashboard of some of those metrics that you really like to measure from a SAS perspective just given the lumpiness your chart when you say lumpy I mean it your your revenue growth chart month over month probably looks much more like a staircase than it does a ski slope right yeah a little more jagged scared staircase and I'd like to it's going up to into the right steps the same size yeah I know I understand and now in terms of size I mean look if you're in the low teens at the contract rise you said you guys are well past the six million dollar a our mark at this point correct if I just multiply those yeah I think we're just a little bit we're just a little bit lower than that okay so somewhere in that range maybes got really discounts or something like that what are you growing at right now year-over-year would you say so last year was it's about two and a half X oh wow holy man yeah yeah but that's okay you know you're you're coming from a small you're coming from a small number the numbers aren't huge yet yeah and 2015 right that number was based on you know a few beta customers starting to you know build out some of the larger contracts and then really you know scaling much more in 2017 yeah I mean so if I'm gonna try and do the math of my head here if you're two and a half X scale back twelve months ago in December 2016 you're somewhere around doing doing about 150 grand a month or around 1.8 million annually and now again you're about 2 and 1/2 X that exactly yeah good stuff good growth healthy growth what do you look at in terms of sorry in terms of revenue churn I imagine your net negative because you have so much expansion power is that accurate yes absolutely not negative yeah that's the one thing that's the one great thing about these high price points right is it's a it's a it's a it's an easy thing to drive expansion revenue on what how many people on your team are dedicated to that sales and expansion probably about 15 or so Oh 15 ok good so about 30 32 percent interesting and then last question here before we wrap up with the famous 5 what do you like to keep payback period under so our payback period it's been you know it can it can be in the six month range yeah that's not bad no it's it's relatively quick yeah it's a success that is you know getting you know getting the getting the customer you know getting their data getting them ramped up and getting them going breeding yeah well look at $400,000 a CV I mean if you spend six months on CAC that's 200 grand in your cover fairly quickly fully on board I mean it's not bad at all interesting okay and when was last round that you raised we closed it in the beginning of 2017 so January 2017 okay so we're coming up on a year so you're either an acquisition talk so you're raising which one is it neither right now come on John we've got we've still got we've still got a lot of money left in the bank that's good we'll start looking later in 2018 okay that's fair enough let's wrap up here with the famous five number one what's the last business book you read so I was reading a bunch of books based on kind of brain and how the brain works knowledge I think was the the last one most recently number two is there a CEO that you're following or studying right now so it's kind of embarrassing but I just I just read the Steve Jobs book not too long ago and it's just kind of fascinating his personality the cult of personality around him number three besides you're on with your favorite online tool I'm sorry besides why decide your own with your favorite online tool online tool it's got to be over all right number four how many hours of sleep do you get every night seven and a half I'm a sleep guy I don't believe in I don't believe in the yeah I don't believe in the I can only you know I only need to kill yourself kill yourself model alright yeah what's your situation married single you have kids married seven-year-old daughter okay one kiddo and how old are you John great question 43 to 43 last question take us back 23 years what do you wish that your 20 year old self knew just that you can you can do whatever you want right there's there's absolutely nothing stopping you from going after something and just doing it right if you want some just say that's what you're gonna do and people will believe you the more confidence you are the more people believe you they don't even question it area guys haven't known John 2013 joined up with some professors as they were raising capital around their company called select which helps a lot of mainly fashion brands but imagine other brands as well but mainly fashion brands understand how to stock right how to manage inventory it's our biggest expense item they've signed up about 16 17 18 enterprise accounts with an ACV of somewhere between 350 and 450 first year revenue they're growing or they about two and a half x two year over a year going from about 1.8 million in ARR run rate in 2016 to about six ish million today so healthy growth super healthy payback period of under six months with their team of 55 up there in Boston John thank you for taking us to the top yeah my pleasure thanks for the time I appreciate it