如何在人羣中認出一定會創業的人大綱

If Target can figure out a teen girl was pregnant before her father did, venture firms should be able to identify founders before they start companies. All it takes is the right data.

既然塔吉特（Target）可以先於少女的父親知道她已經懷孕，那麼，風險投資公司也應該有能力在初創公司誕生之前，便鎖定潛在的創始人。所需要的只是適當的數據而已。

That’s where venture capital—ever evolving—is headed. As Mark Susterpointed out last week, the venture capital landscape has become increasingly bifurcated. Seed funds are springing up everywhere, representing 67% of all new funds created, and large funds have gotten even larger. For the early stage investors, this means increased competition and frothy valuations. By the time a founder sets out to raise a seed round, the startup’s valuation might be $10 million.

這正是不斷演進的風險投資行業未來的發展方向。正如馬克•蘇斯特上週指出的那樣，風險投資行業的兩極分化日益加劇。一方面，種子基金如雨後春筍般崛起，佔新建基金總數的67%，另一方面，大型基金則變得日益龐大。對於早期投資者而言，這意味着競爭日益激烈，進而導致估值泡沫。等到一位公司創始人開始進行種子期融資時，其公司的估值可能已經達到1,000萬美元。

One way to get around that is to invest even earlier. Invest before the company is a company. Before the founder even knows they’re a founder. Bloomberg Beta, the venture investment arm of Bloomberg LP, has been doing this for a year now.

要避免這種情況，方法之一是將投資的節點提前。在公司還未誕生之前就進行投資。甚至連創始人自己都不知道他們會進行創業的時候，便提前開始“燒冷竈”。彭博資訊（Bloomberg LP）旗下的創投基金Bloomberg Beta已經花了一年時間這麼做了。

After an unsuccessful attempt to build a database of “future founders” on its own, the firm teamed up with Mattermark, the deal intelligence company founded by Danielle Morrill. The results could have ramifications for the way investment decisions, typically driven by gut instinct and intuition, are made.

該基金曾嘗試獨自建立“未來創始人”數據庫，但以失敗告終，因此，它決定與丹尼爾•莫里爾創建的交易情報公司Mattermark合作。其研究結果可能對投資決策方式產生深遠影響。目前的投資決策通常均基於投資者的本能和直覺。

Mattermark identified the most likely career paths of successful founders, creating a pool of 1.5 million people who were connected by one to two degrees of separation to tech startups, but were not founders yet. By analyzing the people that started companies over nine months, Mattermark mapped out the strongest predictors of starting a company: a person’s education, which previous companies they’ve worked for and how senior they were, their geography, and their age. The goal was to find things that didn’t fit the standard path to entrepreneurship. As Morrill points out: “Anything that looks like a pattern, people will already find it.”

Mattermark確定了成功的創始人最有可能的職業發展路徑，並創建了一個150萬人的數據庫，由距離科技創業公司1-2個維度的人組成，但還不是創始人。通過分析八個月內創建公司的人，Mattermark先標出了確定一個人是否會創建公司最強有力的預測因素：一個人所接受的教育；他們之前工作的公司與所達到的職務級別；地理位置和年齡。這麼做的目標是找出那些不符合標準創業路徑的東西。正如莫里爾指出的：“凡是看起來成型的東西，那肯定已經有人找到它了。”

The resulting mix of people were older but less senior than you’d expect. Almost 40% of those in the dataset were over 40 years old. Almost half of the people in the data set had worked for a VC-backed company, but two thirds were not in senior leadership positions. Management consultants were twice as likely to start companies. Bloomberg Beta narrowed the list to 350 potential founders, and invited them to parties in New York and San Francisco.

最終結果顯示，創業者的年齡要高於預期，但從事的職位沒有達到預計高度。數據庫內的羣體，約40%超過了40歲。約有一半的人曾在有風投注資的公司工作，但有三分之二的人未從事過高級管理職務。管理諮詢顧問創業的機率是其他職業的兩倍。Bloomberg Beta最終鎖定了350名潛在創始人，並邀請他們前往紐約和舊金山參加聚會。

Cold-emailing people based on data could feel like a creepy invasion of privacy, like Target’s maternity ads. Hi, our algorithm knows your career dreams! Indeed, some people thought it was a scam. But for the self-selecting group of around 75 people that turned up at each party, it was validating.

根據數據貿然發送陌生郵件，感覺像是赤裸裸的侵犯個人隱私，正如塔吉特的孕婦廣告一樣。嗨，我們的算法能預測到你的職業夢想！事實上，確實有人認爲這是詐騙。但對於自願參加了兩次聚會的75人來說，這是對他們的一次檢驗。

“People would say things like, ‘I thought about becoming a founder but I had never even told anyone,’” Morrill says. “When someone believes in you before anyone else—that’s what is really cool here . . . You can actually reinforce a dream they held very closely but never considered seriously.” Morrill admitted that telling people they were in the study probably changes the results.

莫里爾說道：“人們會這樣說：‘我想過創業，但我從來沒有告訴過任何人。’在所有人都毫無察覺的時候，有人便選擇相信你——這種事真的很酷……他們雖然一直堅持自己的夢想但從未認真考慮過，而你的信任可以強化他們的夢想。”莫里斯承認，告訴人們他們被研究選中，可能會改變最終的結果。

Roy Bahat, who leads Bloomberg Beta, was pleased by the diversity of the group. “The data doesn’t discriminate,” he says. “A lot of the people, this was the first time they ever got tapped on the shoulder for something like this.”

Bloomberg Beta負責人羅伊•巴哈特對於最終結果的多樣性感到欣慰。他說：“數據不會有任何偏見。其中很多人有生以來第一次被賦以這樣的期望。”

Whether any of Bloomberg Beta’s potential founders have actually founded a company yet is another story. (It’s only been a few months; Bahat says “a bunch” are in the process.) Likewise, the project has not resulted in any deals for Bloomberg Beta. (“It was expected to be a long term process of getting to know people, so even if we fund zero people for the next two years, that’s fine by me,” he says.) But using data creatively to get a leg up on deal flow will only become more common. Mattermark re-ran a blind version of its study and found its model has a 25x better chance of predicting a founder.

Bloomberg Beta找出的“潛在創始人”以後是否會創建公司，這還有待考證。（雖然僅僅過去幾個月時間，但巴哈特表示“一大批人”已經開始了創業。）同樣，該項目也沒有給Blommberg Beta帶來任何交易。（他說道：“瞭解一個人是一個長期的過程，因此，即使在未來兩年我們沒有對任何人進行投資，我也可以接受。”）但通過創造性地使用數據，在交易流程中佔據先機，這種做法將變得更爲常見。Mattermark重新進行了一次匿名研究，結果發現，其模型預測創始人的成功率比先前高出25倍。

This is one way to boost venture investing with data. Another way? Add a robot to your board of directors, like Deep Knowledge Ventures, a firm in Hong Kong. The firm’s robot board member uses machine learning to predict the best life sciences deals, taking historical data sets to reveal trends that aren’t so obvious to human VC investors. As senior partner Dmitry Kaminskiyexplained to Betabeat, the robot takes emotion out of the process:

這是利用數據促進風險投資的方式之一。另外一種方式是什麼？在董事會中增設一名機器人。正如香港創投公司Deep Knowledge Ventures的做法。該公司的機器人董事會成員，使用機器學習預測最佳生命科學交易，利用歷史數據來預測對於人類風險投資者來說不太明顯的趨勢。正如德米特里•凱明斯基向美國科技網站Betabeat所解釋的那樣，機器人在這個過程中不帶任何情緒：

“Humans are emotional and subjective. They can make mistakes, but unlike the machines they can make brilliant intuitive decisions. Machines like VITAL use only logic. The intuition of the human investors together with machine’s logic with give a perfect collaborative team. The risk of the mistake will be minimized.”

“人類是情緒化的，帶有主觀性。他們會犯錯誤，但與機器不同，人類也會做出明智的直覺決策。與VITAL類似的設備只能使用邏輯。人類投資者的直覺與設備的邏輯，絕對是完美的組合。犯錯誤的風險將被降至最低。”

Sure, it’s novel. But why not? “Whenever people are skeptical that you can use data to do something that previously only people had done, that makes us want to try it,” Bahat says. “When Bloomberg rolled out its first product, people were saying, ‘No, human beings have to be the ones to price bonds.’ Turns out a computer can do some of those things better.”

當然，這種方式有些大膽。但爲什麼不試試呢？巴哈特說道：“當你用數據完成之前只能由人類完成的事情時，總會有人持懷疑態度，這反而讓我們更想進行嘗試。彭博資訊推出第一款產品時，人們說：‘不，只能由人類對債券進行定價。’事實證明，計算機做某些事情會做得更好。”