I wrote a book called Billion Dollar Unicorns a few years back. Writing this book took me through the extensive process of talking to entrepreneurs who have built tech companies with valuations above a billion dollars. While there is a tremendous amount of serendipity involved in any extraordinary success story, one recurring theme comes up in these case studies. I am particularly excited to share this nugget because it applies broadly to all classes of entrepreneurial ventures.
Bootstrap first, raise money later.
That’s what Fred Luddy did when he founded ServiceNow back in 2005. Leveraging his domain knowledge and expertise in IT ServiceDesk software, he rapidly acquired 12 customers before raising funding. Initially, he started charging $25 per seat and the 12 customers paid up. He raised $2.5 million in venture capital WITH 12 customers, and ample validation.
Sramana Mitra: At what point did the US business start happening in your evolution? You started in 2010. At what time did the US business take off?
Camilla Ley Valentin: I would say two to three years into the company.
Sramana Mitra: What strategic nuance did you make to catalyze the growth of the US business?
Camilla Ley Valentin: One of our growth enablers is the queue page – the page that people see when they’re in the waiting room. This is the main enabler for us. As soon as we have a couple of customers in a certain geography, that bridges into a new country. What we’ve done, specifically to cater to the US, is we’ve decided early on that we want to keep it simple and keep all our communication in English only. That alone makes some countries less interested in what we do because everything is provided in English.
We also make it easy for American companies to contact us as we created a US phone number early on. You can do that with services online so that people can easily get in touch with us without necessarily having the ability to call internationally. Some companies have a policy where a lot of their employees can’t dial international numbers.
In order to make that easy and to signal that we are interested in the US market space, we created a US number that people could call us on even before we had an office in the US. That was one thing we did on a more practical level to cater to the US market. When we talk about e-commerce and ticketing topics on our website and on our social media, we try to do it with a US angle.
As an example, we’ll talk a lot about Black Friday, which for a number of years has been a very American phenomenon. Now it’s spread to more parts of the world but it’s mainly a US thing. We do it now in our communication and also in the outreach that we do.
Sramana Mitra: Is your business mostly inside sales or do you have bag-carrying sales people as well?
Camilla Ley Valentin: It’s inside sales.
Sramana Mitra: The US inside sales business that you do, is that out of the US or out of Denmark?
Camilla Ley Valentin: Now it’s out of the US. We opened our US office in Minneapolis in the summer of 2017.
Sramana Mitra: Very interesting. In the subsequent rounds of financing that you’ve raised, did you raise that money in Europe or did you raise some money in the US as well?
Camilla Ley Valentin: The funding was raised in Copenhagen.
Sramana Mitra: So your entire funding is from Copenhagen?
Camilla Ley Valentin: We don’t have much funding. We’ve been very capital efficient.
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Navid Alipour: Our LP’s said “We want you to start other companies that are applying artificial intelligence, machine learning, and deep learning to clean structure data.” By applying this AI, you can make a prediction or you can detect something that doesn’t belong.
By doing one of those things, you’re going to increase revenues, decrease costs by bringing operational efficiencies. Or in the healthcare sense, you are not just saving money in bringing efficiencies, but you’re also prolonging life and saving lives as well. In breast cancer example, if detected earlier, the odds of survival are significantly higher. That’s where we’re proud. >>>
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Sramana Mitra: What is the average size of each customer? What is an average deal size? I’m trying to understand your business. Is it a business that generates $2,000 or $200,000 per customer?
Camilla Ley Valentin: What I can say is that our customers range. An example of a small case could be a university that provides student housing and you have the student apply for that. That’s typically low price. When we protect an iPhone by a major telecom company, then you can imagine that that’s quite a different revenue scenario. Most of our customers are in the enterprise range. We can’t disclose any specific numbers but it’s mainly large enterprise businesses.
Sramana Mitra: It’s mainly an enterprise software business, right? >>>
Responding to popular request, we are now sharing transcripts of our investor podcast interviews in this new series. The following interview with Navid Alipour was recorded in January 2019.
Navid Alipour, Co-Founder and Managing Partner at Analytics Ventures, talks about their AI-focused venture studio in San Diego.
Sramana Mitra: Tell us about Analytics Ventures and yourself. What are you looking at? What is your investment thesis? Let’s get acquainted.
Navid Alipour: We are based here in San Diego, California. I like to describe Analytics Ventures as a three-legged platform. One leg is our fund, which is a dedicated fund. The mandate from our investors is to invest in companies that we co-found with other scientists for academic entrepreneurs or other public or private corporations under the venture studio model. >>>
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Sramana Mitra: Was your first customer from Denmark?
Camilla Ley Valentin: Yes, it was. It was a government company and that was the first customer. It was a company called Nature Agency and they manage all the hunters in Denmark. The hunters have to register all the animals that they shoot in a complicated legacy system that’s managed through a web interface by all the hunters individually.
This was quite tricky because they all do it at the same time. The site will be unresponsive. They use Queue-it for protecting it against crashing. This was the first use case which is not one of the ones that we predicted when we did the business plan. >>>