DataFox Hunts Down $5 Million in Fresh Funding

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DataFox, a 2.5-year-old subscription-based deal intelligence platform, has just raised $5 million in funding co-led by Goldman Sachs and earlier investor Green Visor Capital, with participation from another previous backer, Stanford’s StartX fund.

The funding follows an earlier, $2 million seed round completed via convertible note. It also follows a slight pivot for the 15-person, Palo Alto, Ca.-based company, which was earlier considered a competitor to other deal intelligence platforms like Mattermark but has since begun emphasizing its ability to help accelerate sales. Its informal new tagline: It helps corporations use data science to find their next customers.

We talked with cofounder and CEO Bastiaan Janmaat to learn more. We picked up some interesting details about the financing along the way.

TC: Congratulations on your Series A round.

BJ: It looks like a Series A, but it’s actually a seed round. [The law firm] Fencwick & West has these seed documents that are cheaper, have far less legalese, and place many fewer restrictions on the founders than you typically see in a priced round. I’d highly recommend them to other founders. It makes for a much smoother transition from convertible note territory to your eventual Series B and C round, where you start placing more power in investors’ hands.

TC: One of your co-leads is Goldman Sachs, which isn’t known for its seed deals.

BJ: I used to work at Goldman [as an analyst in its Special Situations Group], and when we started thinking about raising the round, I asked friends, “Who makes investments in tech companies” at Goldman? They introduced me to its Principal Strategic Investments group — principal meaning they invest [Goldman’s] own, not their clients’, money. Its philosophy basically is that if we’re going to be a customer or a partner [of a company], shouldn’t we also be putting our money where our mouth is and investing?

TC: You now have a database of 600,000 private and public companies that you’re tracking. How do your customers mostly use that constantly updated information. What’s the most common use case?

BJ: Actually, in the last year, one of our most common use cases has become to take the client lists of companies that [our customers’] have either had success with or sold to and apply our data science to infer where they should be spending time next. We’re working with three groups: investment banks, growth equity firms, and venture firms, including Scale Venture Partners; sales and marketing departments at companies like Box; and strategy, research, and consulting firms like NetApp, which uses us to find acquisition targets. Say you’ve had success with e-commerce companies with 15 employees that have raised $5 million. On that basis, we’ll present you with the next 100 customers you should be selling to.

TC: Was that the company’s original intent? We’ve talked before. This seems like a subtle shift.

BJ: I used to work in growth equity and my cofounder worked in banking, and we were both having to keep a lot of data up to date manually, which was a problem we wanted to automate. The component that we learned about and didn’t foresee was that clients didn’t want to just look up companies on our platform; they wanted us to suggest, based on their past behavior, where they should be spending their time. That’s still a process that’s done very manually now [elsewhere].

TC: How much do you charge for access to the platform?

BJ: There are three tiers: $600, $1,200 and up to $2,400 a month, which includes access for up to four people. For the cheapest tier, you can track up to 1,000 companies; for the middle tier, you can track up to 12,000 companies. At our top tier, usage is unlimited. Our average client is spending around $10,000 a year as a starting point. Our churn has been negative, too, because we’re adding more seats than canceling them.

TC: But you do presumably get people who sign up for a month, scour the platform, then cancel.

BJ: Yes, that’s always a risk. It’s definitely worth $1,200 to go look up a bunch of companies. But where we deliver [return on investment] is when we onboard them and they take the time to onboard us and we start doing work for them.

Two years ago, we were super manual. We’d take a client’s spreadsheet and, with the help of our analysts here and in the Philippines, find the record for every company listed on it. It would take two weeks. But we’ve been training our algorithms for two years now. That’s how language processing works. So now a client can upload its lists and we can begin uncovering important data points without a human touching it. What was 90 percent manual is now 20 to 30 percent manual. It just takes a lot of training.