SaaS Interviews with CEOs, Startups, Founders
1123 Ecommerce Brands Pay Him $3m To Increase Average Cart Value 12%+
21 Aug 2018
Chapter 1: What inspired Robin to launch Loop54?
Let go of your career faster from Robin who launched Loop 54. It's essentially search for e-commerce, right? And retail. Launched in 2011. The agency went all full in on SaaS in 2013. Has scaled today to about 130 customers paying $30,000 ACVs. So doing about 260 grand per month in revenue, which is 3.1 million bucks in ARR. That's up from 1.6 million in ARR just 13 months ago.
So over 100%, well, about 100%. year-over-year growth, 6% revenue churn annually, and just over 100% in terms of net revenue retention, which is great. His team of 27 people out there in London and Stockholm. 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 hundred thousand dollars to 2.7 million.
I had no money when I started the company.
Chapter 2: How did Loop54 transition from an agency to a SaaS model?
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. Hello, everybody. My guest today is Robin Mellstrand.
He's the CEO and co-founder of an award-winning and quickly expanding startup called Loop 54. He holds a master's degree in industrial engineering and management and has been into sales his whole career prior to Loop 45. Which one is it? Is it Loop 54 or 45? 54. 54, good. He currently lives in Stockholm, Sweden together with his girlfriend and judgmental dog, Robin.
Chapter 3: What is the subscription model and pricing structure of Loop54?
Are you ready to take us to the top? Yeah, I'm ready. All right, so tell us about the company. What does Loop 54 do and how do you make revenue?
So we do search and navigation for retailers around the world, and we basically increase the conversions and revenue online, and they pay us via a subscription model.
Okay, so it's a pure play SaaS model. Yeah, definitely. We deliver it as an API. What do they pay on, like, give me a general sense, on average, what they pay per year or per month.
So on average, we have 30K per year, US dollars.
Okay, so about 2,500 bucks a month, and they're basically buying, what, a number of API calls?
Yes.
Yeah, more or less, and then you can buy depending on which features you choose. If you just do search and if you have the whole category structure being delivered by us, you pay a bit differently. But yeah, more or less, it's mostly volume.
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Chapter 4: How does Loop54 improve search for e-commerce retailers?
Okay, so just so we can get a real stranglehold on what the company does, can you give me an example of how a customer is currently using it? Actually name the customer in the use case?
Yeah, sure. We work with... We work with Office Depot here in the Nordics, as well as the largest grocery chains. And if you go to Co-op, which is a big grocery chain in the Nordics, other parts of the world as well, you go search for Orange. Like a human has a very easy time figuring out what orange means, right? But for a computer, that is exceptionally hard.
Orange, it could be a fruit, a taste, a color, or a telecom operator in France, right? So there's big ambiguity to that word. What do you actually mean? A human figures out instantly that if you're in a food store, you're asking for orange. In that context, you obviously mean the fruit. But for a computer, that's much harder to do that automatically.
So a lot of search implementations today is actually done with a lot of manual work, adding in custom sorting, adding in synonyms, adding in redirects to the right content pages.
Chapter 5: What are the current revenue and growth metrics for Loop54?
So basically today is the truth that if you want to put the most time And money on the search is one that's going to have the best solution. But we managed to build a solution that learns automatically and automates automatically without you having to do anything.
So Robin, tell me specifically how Office Depot used it. Do I go to OfficeDepot.com? Where am I going to see your software in action?
So we are based in the Nordics and we are growing in the UK. We don't have any clients in the US right now. So this is on Office Depot's Swedish site.
Okay, so I'm there. So where do I see your stuff?
Well, just in the search box. You don't really see it. So we are just algorithms that deliver the search results.
Okay, so I'm going to search pen black gel ink.
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Chapter 6: What challenges did Loop54 face during its early days?
What do you do then?
Probably not going to yield a bunch of results since all this catalog is in Swedish, right?
Okay, let's say I type that in in Swedish.
Yeah, then it's going to figure out what is your underlying intent, what do other people who search for similar things mean, and give you the best possible results for each and every query.
Okay, so you're like an e-commerce search engine. Yeah, more or less, yeah. Interesting. And then that would count as one query in terms of Office Depot's paying you for a million queries a year at $30,000 ACV? Yeah, more or less. Interesting. Okay, before I get more of the backstory, so what have you scaled to today? How many total customers are you working with?
We have 130 clients and we are $3 million there or 3.1 maybe.
Okay, so 3.1 today. And are you growing?
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Chapter 7: How does Loop54 plan to scale and expand into new markets?
What were you at about, you know, call it 16 months ago?
16 months ago.
Oh, sorry. Sorry. Sorry. 12 months ago. That's easier. We're at 1.6, I think. Okay. So December 2016, you're at about 1.6 million in AR. So you've doubled almost, more than doubled.
Yeah. Yeah, exactly.
That's great. And you said you're 3.1 today, right?
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Chapter 8: What insights does Robin share about customer acquisition costs?
Yeah. Okay. Wonderful. Let's get more of the backstory here. When did you launch the company?
Long story. So this wasn't actually a search company from the beginning, but he actually launched it as a web consultancy, me and the two other founders. It was a mathematician and a programmer. It didn't go really well since we didn't really know how to sell the mathematician services. So he basically had a lot of time on his hands. And this was back in 2011. So he basically ended up
He had, he's done one sale in the company's history and he talked like a company called bubbler, which is basically the local copy of Netflix. If you would say back then they don't exist anymore. And he pitched them saying that, yeah, I can be the exact same recommendation.
And you know, Netflix has, because back then it was all, everything was in white papers was a big, big bus around that six or seven years ago. Okay. So you said 2012, 2011, I think. And he said, yeah, I can build the same recommendation as they had. And it did, and obviously worked really well on Netflix data, public data.
Problem is that all of these kind of algorithms are built around you having massive amounts of user data. So they have their global company and they have lots of behavior data, basically. So it worked perfectly for them. But when we implemented it at Butler, everything fell apart because they're a regional company with not as much data as everyone else.
And then all the theory behind this algorithm
fell together basically so we gave him the product saying this is how you do it use it when you get bigger and then afterwards since we didn't have a new product for our mathematician he sat down and thought all right is there any way else i can understand how products fit together without knowing anything about the users so we applied new type of mathematical research in this particular problem and found a way to do that long story short it turned out to be quite a horrible recommendation engine still but
beginning to really interesting search engine.
Yeah. So how many, I mean, your system gets smarter the more it gets used because you can do pattern recognition. How many queries did you guys process over the past 12 months across all your base?
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