Can Google take the risk out of venture capitalism?

Unlike venture capitalists of old, Google Ventures the venture capital investment arm of Google Inc. focuses not on gut instinct and luck, but on the science of the deal. First, data is collected, collated, analyzed. Only then does the money start to flow.

Started in 2009 Google Ventures take on investing represents a new formula for the venture capital business. Originally skeptics were doubtful on the company’s reliance on computer algorithms over those old venture capitalism intangibles of instinct and good fortune but over the past 4 years Google Ventures has been busy proving the skeptics wrong standing out in an industry that has been dealing its investors a bad hand. In recent years, an investor would have done better investing with a bog standard mutual fund linked to the stock market than going with a risky venture capital fund. According to Cambridge Associates, venture capital funds posted an annual average return of 6.9 percent from 2002 to 2012, trailing major stock indexes.

Google Ventures in terms of its parent Google is still a baby with a mere $1.5 billion under management — compared with Google’s $50 billion 2012 revenue. It employs a skeleton staff of seven people who gather data, analyze it and present the results to the investors. A prominent statistician at Stanford, Jerome H. Friedman, consults for a few hours a week.

The firm gleans data from academic literature, past experience and due diligence about start-ups and their founders, this data then feeds its algorithms. Even college dropouts who have never started a company have a quantifiable track record, Bill Maris, Google Venture’s managing partner said.

Though the algorithms Google uses to parse the data are top secret. The company has learned a few venture capital lessons its willing to share:

A riddle from Graham Spencer, a general partner at Google Ventures who oversees its data work: Is it better to invest in someone who started a company in a mediocre year for returns and did well, or started one in a good year with mediocre results?

Most people say the first case. But results from academic studies show it is the second, because that indicates the founders have a better sense of market timing, Mr. Spencer said.

Other lessons seem obvious. An entrepreneur who has started a successful company is more likely to do it again, Google Ventures found, and start-ups based in tech hubs like the San Francisco Bay Area are more likely to succeed.

But the key to a identifying a good investment, Mr. Spencer said, is to be able to understand which elements are most important, as opposed to coming up with black-and-white rules. Google’s algorithms have shown that while location matters, for instance, past success is much more important. There is always an exception to  the rule though investing only in experienced entrepreneurs, would have meant overlooking Mark Zuckerberg for example.

“I’m distrustful of any single factor in determining entrepreneurial success,” Mr. Spencer said. “I don’t like any of these pat, simple answers.”

Google says intuition and chemistry do still play a crucial role, and can even override the data.

“We would never make an investment in a founder we thought was a jerk, even if all the data said this is an investment you should make,” Mr. Maris said. “We would make an investment in a founder we really believed in, even if all the data said we’re making a mistake. But it would give us pause.”

Google Ventures, like all venture funds, does not publicly reveal returns. But its partners can count on one hand the number of its 170 investments that have failed, though it is too early to know how many will succeed.

Other venture capital companies are now taking heed of the importance of data mining. Established funds like Kleiner Perkins Caufield & Byers, Sequoia Capital and Y Combinator are all now using data analysis to some extent to help in their investment choices, and new firms like the Ironstone Group are following Google Venture’s lead.

Ironstone, a venture firm started by William R. Hambrecht, one of the pioneers of tech investing relies heavily on its algorithms to identify investments

These algorithms have produced conclusions that run counter to traditional venture capitalism wisdom.  They say, for instance, that a start-up’s founding team has only 12 percent predictive value, even though most investors rank that as one of the most important factors. And just 20 percent of Ironstone’s analysis focuses on the start-up itself, and the rest is on the market it is entering.

Traditional VC companies now recognise the role of data in influencing investments but insist there is still a place for luck and good instincts:

“V.C.’s, just like all of our portfolio companies, need to be analytically intuitive in the modern era of data analytics,” says Matt McIlwain, managing director of Madrona Venture Group, which has invested in companies like Amazon.com and Redfin, the real estate site. “But the intuition part is ultimately the biggest factor. And even with all that, a little good luck goes a long way.”

Bill Maris of Google Ventures disagrees:

“If you can’t measure and quantify it, how can you hope to start working on a solution? We have access to the world’s largest data sets you can imagine, our cloud computer infrastructure is the biggest ever. It would be foolish to just go out and make gut investments.”

Source: The New York Times, Google Venture, Wikipedia

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