Strategy Session is a feature for Crunchbase News, where we ask venture capital firms five questions about their investment strategies.
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Rocketship.vc, an early-stage venture capital firm using data science to democratize venture capital, recently announced it has raised $100 million for its Fund II.
The new fund comes five years after Los Altos, California-based Rocketship raised its first fund of $40 million. The firm has invested in 44 portfolio companies across seed, Series A and Series B rounds, with 46 percent of investments made outside of the U.S. It has also seen five exits, including Audacy, Chewse, Fitmob, Fynd and PaySense.
Fund II is backed by an investor group that includes Vulcan Capital, Adams Street Partners, Marc Andreesen’s family office and Chris Dixon. It will enable the Rocketship team to continue investing globally across different sectors–from AI software to health and space–as well as in various company financing stages and increase follow-on investments.
Investment partner Madhu Shalini Iyer discussed the firm’s strategy with Crunchbase News.
Have you made any investments from this fund?
Yes, we started making a few investments, about four, and we are finalizing a couple more. One was for Crosschq, that is doing human resources tech. We are really excited about this company. We invested with Tiger Global. We wanted to make an investment in Silicon Valley, and that one was a seed extension. Another was Khatabook’s $60 million Series B. It is a $200 million-valued company and they are building something great.
What makes Rocketship’s approach unique?
Our entire approach hinges on traditional investing in many ways. What is not traditional is how we curate our deal flow. We have developed the biggest startup database in the world–it is unique. Our staff is all data scientists and people who built companies, and we are able to crunch and clean up data. We have put a lot of effort into curating the database.
On top of that, we have different models that basically pick out the winner. We look at momentum and the next round valuation that we compute, but our models look at the cohort of startups across the world to see what lines up, and picks up trends. From there, we go after companies we want to invest in.
It gives us an advantage as well. Even in the [Silicon] Valley, we can pick up companies wherever we see activity pick up, early or late. For the Y Combinator Demo Day, I didn’t do any specific sampling, but our general sampling saw that 36 percent rough approximation of the companies are international companies we saw very early, and saw trends in fintech and digital health. Our first fund was ahead because the data was telling us to be outside the Valley.
Why is now a good time for your second fund?
We really wanted to double down on our thesis. With our first fund, we wanted to throw out the data model to see if it works. There were early signs of companies doing well. Now, we want to double down to grow a bigger fund. Our operational style, such as operating on Zoom, is now recognized as more than the norm. It is a new style of venture capital.
Your firm was a pioneer of sorts in doing founder pitches over Zoom. What’s your theory behind why investors had not embraced this method earlier?
Investors didn’t want to invest outside of a five-mile radius. They want to be hyper local. Lending money and giving money out is personal. They want to get to know the entrepreneur and see their facility. We have local networks, too, because we are part of groups VCs tap into. However, our data gets us this far. Traditional investors still want to meet the founder. It’s more behavioral than anything else.
Even with the firm’s ability to uncover breakout companies, what lessons have you learned with regard to finding the right founder to back?
We have been lucky in that we have so much data available for every company we see. We can look at a cohort of companies, pick and choose, and the data tells a story of its own.
We are agnostic, but we are finding momentum and picking up on it, it’s not just a one-off. We are seeing an edtech trend and seeing a lot of companies doing certain things in that trend, and we can pick out even more.
We have mitigated a lot of risk because of the data. Once we have all of the data, we do try to do due diligence with our networks. When we speak with the founder, we get to know finances and we are able to make these foundations stronger. It is not just about the data, there is a lot more, but our directive has not been proven wrong. We might not ultimately take the bet—we can say it is not the right time—but it provided a directional signal. Due to the function of having comparables, even in the same geography, we can make better decisions.
Illustration: Dom Guzman
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