????在風險投資界，無論向誰問起業(yè)務模式問題，他們給你的答案可能都是關鍵在于“命中率”。在風投領域，“命中”是指初創(chuàng)公司發(fā)展壯大，并帶來風投基金初始投資的許多倍的回報。包括投資者、創(chuàng)業(yè)者和求職者在內(nèi)，命中對所有人來說都美妙無比。然而，問題在于這種情況概率不高。曾對蘋果、Genentech和谷歌進行過早期投資的風投創(chuàng)奇人物威廉·哈姆布雷特認為，命中的可能性為十分之一，“有那么幾個會成功，但許多都是賠本買賣。”

????但是，如果風投資本的命中率能達到50%，甚至三分之二呢？2014年，風投公司拿出了480億美元資金。如果能達到那樣的命中率，它們就不會投資那些在殘酷競爭之下本沒有什么生存機會的初創(chuàng)企業(yè)，從而避免巨額損失。難點在于要趕在市場否決這些創(chuàng)業(yè)者的點子之前，早早地發(fā)現(xiàn)這些可能掉隊的公司；更重要的一點則可能是要先于別人發(fā)現(xiàn)那些有望大獲成功的項目。長期以來，風投資本一直靠主觀方法和直覺來評估初創(chuàng)企業(yè)。不過，隨著越來越多的風投公司把數(shù)據(jù)科學用于決策并且保持決策的一致性，這種情況正在發(fā)生改變。

????哈姆布雷特于1968年創(chuàng)立了自己的投資銀行。說到按計算結果進行投資，大家可能不會首先想到他，但近來他確實在采用這種方法。哈姆布雷特是風投公司W(wǎng)R Hambrecht Ventures的合伙人，后者隸屬于專門從事IPO的投資銀行WR Hambrecht and Company。他和常務董事托馬斯·瑟斯頓緊密合作，創(chuàng)立了一種投資策略，把預測模型和克雷頓·克里斯滕森的顛覆性創(chuàng)新理論合二為一。瑟斯頓曾在英特爾擔任業(yè)務開發(fā)經(jīng)理，還是Growth Science的創(chuàng)始人，這是一家由三名成員組成的公司。瑟斯頓說，這是一家營利性智囊機構，“它的科研工作及其開發(fā)的工具都圍繞著一個問題，那就是怎樣才能更好地預測初創(chuàng)公司、新產(chǎn)品或者收購計劃等創(chuàng)新活動能否存活下來，還是會以失敗告終？”Growth Science采用專有數(shù)據(jù)庫、數(shù)據(jù)采集方法以及算法在數(shù)據(jù)統(tǒng)計領域進行創(chuàng)新，以此計算業(yè)務模式和新技術獲得成功的可能性。哈姆布雷特和瑟斯頓的合作方式非常有針對性——WR Hambrecht Ventures投資的所有公司都通過了Growth Science的預先檢驗。瑟斯頓解釋說：“在這個過程中，所有環(huán)節(jié)都沒有人的主觀性參與，我們通過各種算法，最終得出肯定或否定的結果。”

????這些結果取決于很多因素，但瑟斯頓不愿意進行詳細說明。不過，他把這些因素分為兩類，一類在初創(chuàng)公司內(nèi)部，另一類來自外部。瑟斯頓說：“我們發(fā)現(xiàn)，來自初創(chuàng)公司本身的預測因素只有20%左右（比如團隊），另外80%則是初創(chuàng)公司以外的東西”，比如市場、消費者、競爭者、技術趨勢和時機等。他們還把這種測算方法設計為動態(tài)模式，而非靜態(tài)。瑟斯頓指出：“我們更關心事物可能出現(xiàn)的變化，而不是目前的情況如何。”

????那么，哈姆布雷特的風投基金表現(xiàn)如何呢？下定論為時尚早。通常，風投基金需要10-15年才能把資金返還給投資者，而哈姆布雷特和Growth Science的合作時間只有八年（他的投資對象包括價值15億美元的移動短信服務商Tango）。不過，已經(jīng)出現(xiàn)了有利跡象。哈姆布雷特介紹說：“基于后續(xù)發(fā)行估算，我們的投資組合資產(chǎn)已經(jīng)是原來的五倍，，而且是在還沒有哪家公司上市或以高價轉(zhuǎn)讓的情況下。所以我們認為這些基金的回報率將非常高。我們覺得有幾家公司將會上市，可能是在明年。”

????Growth Science的另一個特點是它和幾家“成員”公司合作，后者包括英特爾、3M和Cray Computer等。這些公司付費使用Growth Science的預測工具，具體做法是登錄到Growth Science的網(wǎng)站上，就某項創(chuàng)新或新業(yè)務回答一系列問題，然后由Growth Science提交報告，告訴它們這項創(chuàng)新或新業(yè)務有多大的幾率獲得成功。

????羅恩·霍夫納是3M戰(zhàn)略業(yè)務開發(fā)集團的高級經(jīng)理，負責3M和Growth Science的聯(lián)絡事務。加入3M前，霍夫納在聯(lián)合健康集團的創(chuàng)新實驗室工作，研究旨在消除市場不確定因素的技術。得知Growth Science可以對業(yè)務模式進行模擬后，霍夫納順理成章地將其用于風險管理。3M首先在不同環(huán)境下對Growth Science的預測工具進行了測試，比如建立新業(yè)務、實施并購或進行創(chuàng)新，隨后才把這項技術用于指導實際決策。3M對后者的準確性感到滿意，已將其用于自己的醫(yī)療保健業(yè)務，而且頻繁地用它進行創(chuàng)新管理。對霍夫納來說，Growth Science的模擬回答了三個關于新產(chǎn)品或創(chuàng)新的關鍵問題：市場規(guī)模大嗎？采用的技術合適嗎？使用的方法和3M的業(yè)務模式一致嗎？

????對于最后一個問題，Cray Computer首席執(zhí)行官彼得·恩加羅舉了一個具體的例子，說明了這樣的模擬工具如何改變了該公司推出新產(chǎn)品的方式。Cray Computer生產(chǎn)超級計算機以及數(shù)據(jù)存儲和分析平臺，它剛剛進入某個市場時推出了一種性能更強的產(chǎn)品，還降低了購置總成本。考慮到Cray Computer屬于新生勢力，Growth Science對產(chǎn)品定位的建議是購置成本最低，而不是在高性能上做文章。恩加羅說：“這真的很有意思，特別是對Cray Computer這樣的公司來說，因為我們的立足之本就是盡可能為用戶提高性能。這個產(chǎn)品也是如此，但在那個市場，我們采用了另外一種定位。到目前為止，效果確實很好。”

????霍夫納指出，這種方法說到底就是選擇式?jīng)Q策，在這個過程中，可以通過有效信息推導出不同的行為，以及這些行為可能產(chǎn)生的結果。這樣做最終會帶來戰(zhàn)略上的靈活性。霍夫納說：“我確實覺得它屬于更高的層次。在未來的管理活動中，人們會把它視為第一個帶來數(shù)據(jù)驅(qū)動型管理的工具。”

????瑟斯頓把Growth Science的風投和企業(yè)業(yè)務視為同一枚硬幣的正反兩面。無論在哪個領域，管理者都想在投入資金前預測一下未來的情況。通過分析美國小企業(yè)管理局的數(shù)據(jù)以及他本人掌握的初創(chuàng)公司數(shù)據(jù)，瑟斯頓發(fā)現(xiàn)20-30%的新公司能生存10年。他說，和新開業(yè)的泰式餐廳、干洗店或者從大公司中剝離出來的業(yè)務相比，獲得風投支持的初創(chuàng)企業(yè)好不到哪兒去，“我們[創(chuàng)業(yè)經(jīng)濟]在對業(yè)務進行預測方面做的更好一點兒嗎？就我們的數(shù)據(jù)而言，答案是不，從統(tǒng)計角度來說數(shù)據(jù)不算亮眼。”不過，Growth Science表示它正在改變這種局面。瑟斯頓指出，在預測10年后公司能否存活的問題上，Growth Science的正確率是67%，而剩下的三分之一則預測錯了。

????那么問題來了，如果Growth Science大幅提高了投資新技術和新公司的成功率，那為什么整個風投界并沒有趨之若鶩呢？瑟斯頓回答說：“許多風投資本家都持懷疑態(tài)度，就像電影《點球成金》里的那些球探對運動員統(tǒng)計數(shù)據(jù)的態(tài)度一樣。”

????Ask anyone in venture capital about their business model and they will probably tell you it’s all about the “hits.” In the VC world, a hit is a startup that makes it big, returning many multiples of a venture fund’s initial investment. Hits are great for everyone—investors, entrepreneurs, job seekers—but the problem is they don’t happen very often. William Hambrecht, a legendary venture capitalist who made early investments in Apple, Genentech, and Google, says the odds of a big hit are about one in 10. “A few others will work out, and you’re going to lose in a lot,” he says.

????But what if venture capital could boost its odds to 50-50, or even two out of three? With $48 billion in VC investment in 2014, such an improvement would prevent huge amounts of money from being lost on startups that never had much of a chance of surviving the harsh competitive environment. The challenge is to identify those likely laggards well before the market rejects their idea and, perhaps more importantly, to see the big hits before anyone else. Venture capital has long relied on subjective, intuitive methods of assessing startups, but that’s changing as more firms are bringing data science and consistency into their decision-making.

????Hambrecht, who started an investment bank in 1968, isn’t the first venture capitalist you’d expect to be investing according to the results of an algorithm, but that’s exactly what he’s doing these days. As a partner at WR Hambrecht Ventures, the VC arm of the IPO specialist WR Hambrecht and Company, Hambrecht works closely with managing director Thomas Thurston on an investment strategy that combines predictive modeling and Clayton Christensen’s disruption theory. Thurston, a former business development manager at Intel, is also the founder of Growth Science, a three person company he calls a for-profit think tank that’s “trying to do the science, build the tools, and do the research all around this one question: How can we better predict when innovations will survive or fail, both for startups and when corporations launch new products or do acquisitions?” he says. The company uses proprietary databases and data harvesting, along with algorithms, to bring innovations into the world of statistics, delivering probabilities on the success of business models and new technology. Hambrecht and Thurston join forces in a very specific way: Each company that WR Hambrecht Ventures invests in has gone through the Growth Science prediction engine and passed. “There’s no human subjectivity involved anywhere along the line,” Thurston explained. “All the algorithms converge on a discrete yes or no.”

????That yes or no depends on a lot of factors, and Thurston declined to be very specific about what they are. But he did separate them into two categories: those inside the startup, and those external to the startup. “We’ve found only around 20% of the predictive value to come from details specific to the startup itself (e.g., the team),” he says, “whereas 80% comes from things outside of the startup,” which he listed as the market, customers, competitors, technology trends, and timing. The model is also designed to be dynamic rather than static: “we care more about how things are likely to change, rather than how things are today,” he says.

????So how are Hambrecht’s funds doing? It’s too early to say for certain. VC funds typically take 10 to 15 years to return money to investors, and Hambrecht has been using the Growth Science method for eight. (Among the firm’s investments: Tango, a mobile messaging service worth $1.5 billion.) With that said, the signs are positive. According to Hambrecht, “the portfolios are up five times, just based on subsequent offerings, and nothing has gone public or sold out big yet, so we think these funds are going to have very high returns. We have several we think will go into the public market, probably within the next year.”

????Another facet of Growth Science is its work with “member” companies, such as Intel, 3M, Cray Computer, and a few others. These firms pay Growth Science for access to its prediction engine; they can log in to a website, answer a set of questions about an innovation or a new business, and Growth Science sends them a report on its likelihood of success.

????Ron Hoffner is a senior manager in the strategic business development group at 3M , and the company’s liaison with Growth Science. Prior to joining 3M, Hoffner worked in UnitedHealth’s innovation lab, researching techniques to decrease uncertainty in markets. When he learned of Growth Science’s business model simulation, it made perfect sense to him as a risk management tool. Before using it to guide real decisions, 3M did a few tests with the model in different environments, such as new ventures, mergers and acquisitions, and innovations. Satisfied with its accuracy, 3M has put the predictive model to use in its health care business group, where it’s used frequently to manage a portfolio of innovations. To Hoffner, the simulation answers three key questions about a new product or innovation: Is the market going to be big? Is this the right technology? Does the approach align with the company’s business model?

????On that last question, Peter Ungaro, the CEO of Cray Computer, provided a concrete example of how the simulation changed the company’s direction with a new product rollout. Cray, which makes supercomputers, data-storage, and analytics platforms, was a new entrant in a particular market, introducing a product with an increase in performance, but also a reduced total cost of ownership. Based on the company’s status as a new entrant, the model recommended positioning the product as the one with the lowest cost of ownership, instead of playing up its high performance. “That’s a really interesting thing, especially for a company like Cray, which is built on providing our customers with the most performance possible,” Ungaro says. “The product still does that, but we position it in the market in a different way. So far, the outcome has been really good.”

????This approach, Hoffner says, boils down to an options-based form of decision-making, where the right information can translate into various courses of action and their likely outcomes. The result, he says, is strategic flexibility. “I really think it is the next level,” Hoffner says. “If you look at the future of management, this is something people will look back on as the first tool to introduce data-driven management.”

????Thurston views Growth Science’s VC and enterprise work as two sides of the same coin. In each case, managers are trying to predict the future before investing money in it. According to Thurston’s analysis of data from the Small Business Administration, and his own data on startups, between 20% and 30% of new businesses survive to their 10th birthday. Startups with VC-backing, he says, aren’t doing much better than new Thai restaurants, dry cleaners, or spin-offs from large corporations. “Have we [the entrepreneurship economy] gotten any better at predicting any business?” Thurston asked. “Our data suggest no, not in any statistically significant amount.” Growth Science, however, claims it is turning that around. When it comes to predicting survivorship of companies after a 10-year period, Thurston says Growth Science has been right 67% of the time, and totally wrong on the remaining third.

????That raises the question: If Growth Science has vastly improved the odds of investing in new technologies and businesses, why isn’t the entire VC world knocking on its door? Thurston’s answer: “A lot of venture capitalists, like the scouts in Moneyball reacting to sabermetrics, are skeptical.”