SaaS Interviews with CEOs, Startups, Founders
1020 Urban Outfitters Inventory Management Tool Breaks $6m in ARR
10 May 2018
Chapter 1: What is the main topic discussed in this episode?
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.
Chapter 2: How did John Andrews start his career in retail technology?
Hello, everyone. My guest today is John Andrews. He's spent the last two decades helping retailers, distributors, and brands optimize their omnichannel strategies and operations. Before his current company, Select, John was VP of Product Marketing and Strategy for Oracle Commerce, coming to Oracle via the Indeca acquisition, where John was VP of Marketing and Product Management.
He started his career with Deloitte Consulting Strategy and 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 us to the top? Yeah, sounds great. All right. So tell us about Select. What do you do and what's your business model? How do you make money?
Great. So we are at the highest level. We're a predictive analytics technology company. We are focused primarily on retail. The company was actually founded out of MIT by a couple of MIT professors who'd been collaborating on a line 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 main area that we're focused on with retailers is around inventory optimization. So helping retailers optimize inventory, optimize turns, reduce stockouts, reduce markdowns, etc. If you think about inventory, it's the largest number on a retailer's 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 going to make a lot more money. Really, really tough. In retail right now, yeah, 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, I mean, can you give us an example of a store you're working with and how you help them double down on what's working through the data you provided?
Yeah, absolutely. So the process that we help our customers with, right, is kind of through the merchandising and planning process and into the supply chain process, right? You can think of the inventory optimization cycle as kind of a plan, buy, allocate, and fulfill process.
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Chapter 3: What is the business model of Select and how does it generate revenue?
I bought a purple button-down shirt, but maybe the blue one wasn't available. And I would have bought that if it was part of the overall assortment. So being able to normalize against that and then build out that model.
Now you have to, as you start to bring in new products into the mix, something that 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 identify what that demand is going to look like.
When you do that right, we've seen customers with anywhere from, you know, at points five to seven percent increase in revenue to, you know, upwards of, you know, 13 to 14 percent increase in gross margin.
Yeah, I mean, look, you saying all this, I can't help but think, and I'm sure people listening are 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 it was there, and you know what I bought and didn't buy, but you don't know if I picked something up and put it back down. I mean, I don't think you do unless you have trackers and things in every single store.
Yeah.
No. 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 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.
Part of the technology, you need to identify what the selection set is that people are 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 brought up Amazon, right?
And everybody in the retail perspective is looking at Amazon in terms of what they're doing. One of the benefits that Amazon has is just an enormous amount of data, right? The challenge that other retailers have, even though they feel as though they have a lot of data, the issue is that they actually have very sparse data about an individual customer
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Chapter 4: How does Select help retailers optimize their inventory?
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 SaaS-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. So I'm mentioning earlier from the different solutions that I talked around on plan optimization, buy optimization. The interaction on the plan side, 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. Sure.
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 $10 or a million dollars or a thousand or?
Yeah. Yeah, it's generally, so the model is when we start working with a customer, they'll start with a specific solution and focus initially on a specific category area. So as an example, they'll start within the women's shoes department, right? Or the men's, you know, all men's apparel.
Um, and that, you know, the starting point is going to be somewhere between, you know, 400 to call it 400 to 500 K. Okay. Kind of on average per year. Got it. Right. Based on, you know, and there's, you know, varying, uh, based on the customer size, the amount of SKUs that they have.
That's a good average, right?
Yeah. Now, then it will grow from there as they then expand the usage across different categories and as they grow the across different across different products, the solution areas.
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Chapter 5: What challenges do retailers face in inventory management?
It has to be select. Amen. Amen. All right, John, what year was the company founded in?
The company was founded in 2013.
Okay, and you mentioned it's kind of spun out or something at MIT. I mean, were you there on the founding team or these professors brought you in after they got initial scale?
So I came in right as we were looking at our Series A financing, right? So basically, the two professors, insanely smart guys, had a couple of young developers working with them, built up a beta product, had a couple of beta customers. Uh, and then I joined on in, uh, 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?
Uh, we've raised a total of 15 million.
Okay. One five.
Yeah. One five.
Now was, was that series eight, were you an EIR at the VC that led and it was contingent on you joining or no?
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Chapter 6: How does machine learning enhance inventory optimization?
I'm a sleep guy. I don't believe in, I don't believe in the, uh, yeah, I don't believe in the, I can only, you know, I only need to kill yourself, the kill yourself model.
All right. And what's your situation? Married, single, you have kids, uh, married, a seven year old daughter. Okay. One kiddo. And how old are you, John?
Uh, great question. 43, 43.
Last question. Take us back 23 years. What do you wish that your 20 year old self knew?
Just that. you can do whatever you want, right? There's absolutely nothing stopping you from going after something and just doing it, right? If you want something, just say that's what you're going to do and people will believe you.
I love that. It's kind of crazy, but that's just how it is. Have the confidence. The more confidence you are, the more people believe you. They don't even question it.
There you guys have it from 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 I imagine other brands as well, but mainly fashion brands understand how to stock, right? How to manage inventory. It's their 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 with about two and a half X year over 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.
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