Venture Capital (VC) Firms invest in start-ups and new ventures. These investments provide capital for running start-ups and growth stage companies. The funds are invested in ventures with exceptionally high growth potential so as to achieve high value of return through successful exits. Generally, the time-frame is 4-7 years.
However, especially the start-ups are one of the riskiest asset classes and VCs take a big risk while investing in a start-up or an entrepreneur. Hence, finding VCs and convincing them to invest in their ventures is a difficult task for entrepreneurs. I have also observed that there is a difference between the risk perception of the investors and that of the entrepreneurs. As a result, there is difference between value of return expected by an investor and value of return estimated by an entrepreneur. This contributes to the fact that no VC generally funds more than 3-4 % of the investment proposals received.
The risks for the VCs fall into multiple categories, for e.g., long term capital risks, liquidity risks, technology risks, market risks due to market dynamics etc. For VCs, managing risk means managing the uncertainty. Though most of the VCs have a framework to assess and manage risks, many of these risks can’t be determined correctly in the initial stages, while making a decision whether to invest in a venture or not. This is due to lack of quantifiable and accurate market data. As a result, the success rate in VC business is low.
VCs get a number of investment proposals every month. I personally get about 25 to 30 investment proposals every month. The process of evaluation and mitigation of risk starts right at the beginning, right at the screening stage. However, not every time there is enough time or resources to collect data about various business aspects related to the proposal. This puts investors under pressure, since they have to make a decision either way. Data helps investors change the odds in their favour.
Based on my experience, the best way to have a practical risk mitigation mechanism is “Market Intelligence”. Some of the parameters I consider while evaluating a proposal are timing of the entry, market size, competitive landscape, clarity of the business model, unit economics, ability to execute impeccably, technology obsolescence etc. However, sufficient information is generally not be available in the open source to make or substantiate a decision.
I always depend upon Primary Research and Human Intelligence for such information. I use my ecosystem of corporate connects, prospective buying personas, thought-leaders, fellow investors, distribution partners and consultants. This helps me build a specific case with actionable data or intelligence and gives me a much better idea about economic trends, demographics, product-market fitment, segmentation, market readiness, purchasing power, addressable market size, optimum pricing, market players, possibility of being able to build sustainable competitive advantage and more importantly, the revenue projections. This helps me determine the cost of acquiring the opportunity and the value of return that I can expect.
Prima facie, it sounds easy. But it is not. We live in an imperfect world, and it is almost impossible to predict the market shifts. But an investor has to make a rational choice and “Market Intelligence” allows him to make that rational choice.