Venture capitals on nascent tech startup were fueled by the emergence of independent investment firms on SandHill during the 70s, then burnished its high status during 90s dot.com boom&bust, whose lucrative return at one time even eclipsed those traditional leveraged buyout firms on Wall Street. The infinite possibility of technology advancement coupled with current “Unicorn Era” means the idea should stay, nevertheless some are already implementing a new way of doing the old business.
–Venture Client Model
CVC (Corporate Venture Capital) often elicited a snort of contempt as it seemed to fail to deliver tangible results despite massive resources and C-level attentions, though ironically it does not deter the rise of CVC or CVC powered accelerators lately. A Recent article from HBR expatiated some of the chilling facts:
1. Ill-famed CVC or CVC powered accelerators have great difficulty in luring the first-class startup away from private venture capitalists.
2. Only around 20% of technologies funded by CVC or related accelerators seize enough attention of business units to start co-innovation pilot projects.
3. It takes at least a year between the first touch point of the CVC with a startup and the kick-off of a pilot with the business unit.
Capital, coaching and client, they are the three jigsaw puzzles that haunt every startup dream. Nowadays it might be easy to lavish startups with money or provide entrepreneurship training, but what about the clients? Truth be told, we have seen well-funded startup struggle to even sign a pilot contract with a corporate client. Therefore, according to HBR, BMW saw this as its opportunity to shine and revamp the CVC into the venture client model-BMW Startup Garage.
In essence, the venture client, instead of equity, purchases the early technology of a startup. The program would not consider a startup unless it
- has graduated from a prestigious accelerator
- has received professional venture funding, or includes a successful serial entrepreneur as its senior leadership team.
It is said less than 5% of all potential candidates will make it after the screening process. For those that do, the last step prior to acceptance into the program is to define a pilot project between the startup and a business unit. Once the startup proposal is approved, it becomes a real paid supplier for BMW, from day one of the program.
HBR wrote BMW’s initiative gained some momentum: over 1,000 startups have been evaluated since the launch of the program in 2015, and the number is expected to increase to more than 2,000 per year, with over 80% coming from outside Germany.
-Venture Studio Model
In the late 90s, the concept of Idealab was born. It aims to work alongside with startup founders for years and help them to take off, from product design, go to market strategy and fund raising until a liquidity event arises to sell its invested shares. According to The Information, such wind is blowing stronger again. The so called “Venture Studio Model” has become more popular and even played itself to a whole new level as in return it requires a larger equity share than a conventional venture capital firm, that is more than the 10% to 20% of a typical seed round.
A list of these American venture studios received media spotlight includes but not limited to:
• Human Ventures(Link)
• Science(Link): based in Los Angeles, founded in 2011, it has achieved remarkable success, totaling 6 exits so far under its portfolio, with 3 most notable purchases from Unilever, Google and New York Times.
The Information said the studio model provides an opportunity for in-house teams to offer their services to multiple companies at once. The approach might slightly differ among them. But all are more involved than traditional VCs in the early stages of company building&operation. And unlike accelerators, studios don’t graduate classes of startups after a certain period of time.
Although some traditional VCs remain skeptical of the approach, this new breed of VC studios feels confident they are filling a niche that this industry has long overlooked. Many entrepreneurs are neither veteran nor savvy, or perhaps just a first-time founder, and they do have a yearning for hands-on guidance or simply getting into the mud together for the long haul.
-Machine learning Model
AI and machine learning have been such hype these days that if you do not have such labels on your company profile, you are obviously not a tech company. So can VC analysis be AI driven as well? In fact, some VCs are already creating a machine learning model to process startup selection, and the most radical approach might be fully reliance on AI, i.e. AIVC.
An article from McKinsey Quarterly showed an example of a Chinese background VC firm Hone Capital based in Silicon Valley ( an arm of one of the largest venture-capital and private-equity firms in China, CSC Group) building machine learning data analytics model to make a better investment decision in early stage startups. Recent master stroke of Hone Capital in the Valley was showering Angelist with USD400 million.
According to the McKinsey interview, Hone Capital constructed a machine-learning model from a database of more than 30,000 deals during the last decade that draws from many sources, including Crunchbase, Mattermark, and PitchBook Data. For each deal in the historical database, they looked at whether a team made it to a series-A round, and explored 400 characteristics for each deal. Based on this analysis, they have identified 20 characteristics for seed deals as most predictive of future success. They said their model can generate an investment recommendation for each deal they review. McKinsey wrote about some interesting findings Hone Capital concluded, such as:
- start-ups that failed to advance to series A had an average seed investment of USD0.5 million
- the average seed investment for startups that advanced to series A was USD1.5 million (So if a team has received a low investment below this threshold, it suggests that the startup didn’t garner enough interest from investors, and it’s probably not worth their time; or if truly good, it would need more funding to succeed)
- the diversity of the background of founders matters (for instance that a deal with two founders from different universities is twice as likely to succeed as those with founders from the same university)
The Hone Capital told McKinsey 40% of the startups that its machine-learning model recommended for investment raised a follow-on round of fund, which is 2.5 times higher than the industry average. Of course, the best result is the human decision making on top of the machine learning forecast, nearly 3.5 times the industry average.
The VC landscape has been more competitive than ever. The giant incumbents often have more money, network, and influence than you can imagine, which in the end get a much better chance of obtaining a golden exit ticket. To be able to survive under the shadow of Goliath, you must act like King David, aggressive, creative and nimble. All the new approaches are still in their infancy, no guarantee of a solid success in the future, though showing an early sign of gaining traction. Some of the players mentioned above are not even small, if they are already working on something new, then it might mean some traditional approaches to the VC business require change.
Author: Cecilia Wu