{"id":29783,"date":"2018-11-13T15:15:55","date_gmt":"2018-11-13T20:15:55","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/venture-capital-rumors-of-my-death-have-been-greatly-exaggerated\/"},"modified":"2018-11-13T15:15:55","modified_gmt":"2018-11-13T20:15:55","slug":"venture-capital-rumors-of-my-death-have-been-greatly-exaggerated","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/venture-capital-rumors-of-my-death-have-been-greatly-exaggerated\/","title":{"rendered":"Venture Capital: Rumors of my death have been greatly exaggerated"},"content":{"rendered":"

\u201cVenture Capital is Dead.\u201d<\/p>\n

The first line of Mucker Capital co-founder, Erik Rannala\u2019s, 2015 article raised eyebrows and questions regarding the sustainability of traditional venture capital (\u201cVC\u201d). Though hyperbole, Rannala\u2019s statement underscores a recent evolution in VC that is forcing firms to adapt to survive. The pace of VC fundraising reached historic highs over the period 2014-2017, peaking at $41 billion raised in 2016. [1] [2] Most notably, smaller, earlier stage funds have proliferated, with more than 500 sub $200 million funds raised since 2011. [3] An increasingly crowded internal landscape is further complicated by the rise of external competition from crowdfunding, super angels and most recently, ICOs. [4]<\/p>\n

\"\"<\/a><\/p>\n

To compete effectively, VCs can no longer wait for entrepreneurs to reach out. \u00a0Instead, they must create a \u201cpush\u201d sourcing model to proactively identify, diligence, and win deals at earlier stages.<\/p>\n

Veronica Wu, an experienced Motorola, Apple, and Tesla executive, faced this challenge in 2015, when founding Hone Capital (\u201cHone\u201d), a Silicon Valley based venture fund backed by Chinese private equity firm CSC Global. Wu needed to develop a sourcing strategy that would enable Hone to find and win deals in the world\u2019s most competitive venture market.<\/p>\n

To accomplish this goal, Wu turned to machine learning. \u00a0Hone created a, \u201cmachine-learning model from a database of more than 30,000 deals.” [5] Hone used this dataset to examine 400 deal characteristics, eventually identifying 20 that predicted a seed deal\u2019s success (defined as a subsequent Series A). Hone \u2018s model evaluates these characteristics, including, \u201cinvestors\u2019 historical conversion rates, total money raised, the founding team\u2019s background, and the syndicate lead\u2019s area of expertise,\u201d to generate an investment recommendation for each deal.<\/p>\n

Although it is early in the firm\u2019s life – VC is an industry that measures success in decades \u2013 Hone\u2019s model displays promising early results. Of the seed-stage investments recommended by the model in 2015, 40% have raised follow-on capital within 15 months, nearly 2.5 times the industry average of 16%. Remarkably, this performance was nearly identical to that of Hone\u2019s human investment team. Perhaps most importantly, the model identified successful companies that the investment team originally missed.<\/p>\n

Moving forward, Wu and the Hone team intend to refine the model and experiment with the optimum balance between machine learning recommendations and investment team judgement. According to Wu, her team\u2019s analysis of 2015 investment cohort performance, \u201cfound that the best [follow-on]\u00a0performance, nearly 3.5 times the industry average, would result from integrating the recommendations of the humans on our investment team and the machine learning mode.”<\/p>\n

Hone has only scratched the surface of machine learning\u2019s potential impact on VC sourcing.<\/p>\n

According to Roberto Bonanzinga, co-founder of InReach Ventures, in a traditional inbound sourcing model, promising companies fall under the radar because they are not actively looking for money, not based in a tech hub such as Silicon Valley, or not led by well-connected founders. [6] In fact, VC has systematically underrepresented large founder populations \u2013 female entrepreneurs, for example, receive only 2% of venture dollars. [7] Machine learning is a powerful tool for addressing this problem. [8] For example, Alice, an AI platform, provides underrepresented entrepreneurs with benchmark performance figures and personalized recommendations for their startups. [9]<\/p>\n

Hone can similarly harness the power of its model to proactively target high potential companies in underrepresented groups. Not only will this strategy positively impact entrepreneurs, it will also provide Hone with unique deal flow. Chris Farmer, founder of SignalFire, lauds machine learning for its ability to identify \u201ccompanies that we never would have seen otherwise.” [10]<\/p>\n

Although successfully raising a Series A is an important step for a seed investment, it is only the first stop on a startup\u2019s journey to generating a strong end-of-day return for an investor. The true power of Hone\u2019s machine learning model lies not in its ability to predict a Series A raise, but its potential to predict ultimate return. As Hone gathers data from maturing cohorts, it will be able to build upon its model, assess a startup\u2019s return potential in real-time, deliver valuable feedback to its portfolio companies on critical success factors, and utilize this data to make smarter initial investment and follow-on decisions.<\/p>\n

Machine learning\u2019s impact on VC is still in its infancy and outstanding questions remain. Will machine learning accurately capture rapidly changing investor preferences? \u00a0Can it generate a comprehensive blueprint for startup success at each lifecycle stage?<\/p>\n

Perhaps most importantly, will it fundamentally change the requisite VC skillset \u2013 deemphasizing broad personal networks in favor of the ability to exercise investment judgment?<\/p>\n

Wu thinks so.<\/p>\n

At Hone, \u201cWe\u2019re betting on a paradigm shift in venture capital where new platforms provide greater access to deal flow, and investment decision making is driven by integrating human insight with machine-learning-based models.” [11]<\/p>\n

(797 words)<\/p>\n

 <\/p>\n

[1]\u00a0\u201c2Q 2018 Pitchbook NVCA Venture Monitor.\u201d\u00a0Pitchbook, 2018, files.pitchbook.com\/website\/files\/pdf\/2Q_2018_PitchBook_NVCA_Venture_Monitor.pdf.<\/p>\n

[2] \u201cPitchbook 2017 Annual PE VC Fundraising Report.\u201d\u00a0Pitchbook, 2018, files.pitchbook.com\/website\/files\/pdf\/PitchBook_2017_Annual_PE_VC_Fundraising_Report.pdf.<\/p>\n

[3]\u00a0Lemkin, Jason. \u201cHow Will the Proliferation of Micro Funds Led by First-Time Investors Change the Early-Stage Funding Landscape?\u201d\u00a0SaaStr, 5 June 2018, www.saastr.com\/will-proliferation-micro-funds-led-first-time-investors-change-early-stage-funding-landscape\/.<\/p>\n

[4]\u00a0Rannala, Erik. \u201cVenture Capital Is Dead. Long Live Venture Capital.\u201d\u00a0Recode, Recode, 23 July 2015,\u00a0www.recode.net\/2015\/7\/23\/11614964\/venture-capital-is-dead-long-live-venture-capital.<\/p>\n

[5]\u00a0\u201cA Machine Learning Approach to Venture Capital.\u201d McKinsey Quarterly, McKinsey & Co, June 2017.<\/p>\n

[6]\u00a0Palmer, Maija. \u201cArtificial Intelligence Is Guiding Venture Capital to Start-Ups.\u201d\u00a0Financial Times, Financial Times, 12 Dec. 2017, www.ft.com\/content\/dd7fa798-bfcd-11e7-823b-ed31693349d3.<\/p>\n

[7] Zarya, Valentina. \u201cFemale Founders Got 2% of Venture Capital Dollars in 2017.\u201d\u00a0Fortune<\/em>, Fortune, 2018, fortune.com\/2018\/01\/31\/female-founders-venture-capital-2017\/.<\/p>\n

[8]\u00a0Rahnama, Hossein. \u201cHere’s How AI Is Changing VC Funding.\u201d\u00a0Entrepreneur, Entrepreneur, 23 Feb. 2018, www.entrepreneur.com\/article\/309198.<\/p>\n

[9]\u00a0Dishman, Lydia. \u201cAI Platform Alice Aims To Help 4 Million Underrepresented Entrepreneurs.\u201d\u00a0Fast Company<\/em>, Fast Company, 26 Jan. 2018, www.fastcompany.com\/40522097\/ai-platform-alice-aims-to-help-4-million-underrepresented-entrepreneurs.<\/p>\n

[10]\u00a0Palmer. \u201cArtificial Intelligence…”<\/p>\n

[11]\u00a0\u201cA Machine Learning Approach to Venture Capital.\u201d McKinsey Quarterly.<\/p>\n","protected":false},"excerpt":{"rendered":"

Using machine learning to resurrect outdated VC sourcing models <\/p>\n","protected":false},"author":11802,"featured_media":30195,"comment_status":"open","ping_status":"closed","template":"","categories":[4504,346,2373,706],"class_list":["post-29783","hck-submission","type-hck-submission","status-publish","has-post-thumbnail","hentry","category-investment-sourcing","category-machine-learning","category-process-improvement","category-venture-capital","hck-taxonomy-organization-hone-capital","hck-taxonomy-industry-financial-services","hck-taxonomy-country-united-states"],"connected_submission_link":"https:\/\/d3.harvard.edu\/platform-rctom\/assignment\/rc-tom-challenge-2018\/","yoast_head":"\nVenture Capital: Rumors of my death have been greatly exaggerated - Technology and Operations Management<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/venture-capital-rumors-of-my-death-have-been-greatly-exaggerated\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Venture Capital: Rumors of my death have been greatly exaggerated - 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