Intuition or Data: How Do Venture Capital Investors Evaluate Investment Opportunities?
Over the years, the accumulation of data coupled with faster computing power has enabled artificial intelligence (AI)-powered innovations to take industries to a whole new level. The venture capitalist (VC) world too depends on data when it comes to evaluating investment opportunities.
According to a recent survey of 391 VC investors across Asia, Europe and the US by PitchBook, a Seattle-based data provider for the private and public equity markets, global VC investors leverage data to inform investment decisions as well as plans for adopting machine learning for future decision-making.
What Works Best
The survey findings show that investors rely on a mix of data and personal networks to source and evaluate investments. While the majority (86 per cent) of respondents believe data is important when evaluating investment opportunities, late stage VC investors based in the US and Asia are the most likely to leverage data to inform all investment decisions.
Despite growing interest within the asset class to finance the machine learning (ML) and AI category—$24.6 billion was invested in 2018—only eight per cent of investors believe investment decisions will be fully automated in the future. More than 85 per cent of respondents believe there will always be some element of intuition involved in VC dealmaking.
More than one-third (38 per cent) of all respondents, meanwhile, use data to source all venture capital investments, whereas 48 per cent claim data informs some investment decisions and nine per cent don’t leverage data at all. Late-stage VCs show the strongest appetite for data-driven investing, with 50 per cent citing data as extremely important and the primary resource for evaluating and sourcing all investments. Similarly, 46 per cent of Asian firms cite data as extremely important, compared to 37 per cent of US firms and 33 per cent in Europe.
Steve Bendt, vice president of marketing at PitchBook, says, “As VCs flood capital into the ML/AI sector, we wanted to understand how VCs themselves leverage data and machine learning techniques in their own investment sourcing and decision-making process. Our survey shows strong adoption of data to inform investment decision-making and a growing appetite to increase usage. While the majority of respondents believe VC investing will always involve the human element, there’s enthusiasm to explore how machine learning can automate traditional VC.”
The Asia Angle
Personal network remains the most valuable resource for sourcing and evaluating investment opportunities, at 82 per cent, followed by inbound leads (44 per cent) and financial databases (36 per cent). Angel and early-stage VCs are about 20 per cent more likely to lean on personal networks for investment decision-making than late-stage VC and corporate venture capital, which show the strongest demand for financial databases, 20 per cent and 22 per cent, respectively.
Geographically, personal networks remain the top resource for sourcing and evaluating deals. Asian VCs are the most likely group to leverage data for this purpose, with 20 per cent of Asia-based survey respondents citing financial databases as the most valuable resource, compared to 15 per cent in the US and 10 per cent in Europe.
In practice, the most common use cases for leveraging data are financial modelling (20 per cent), refining investment thesis (19 per cent) and sourcing investments (also 19 per cent). As for the top reasons for missing out on a promising investment opportunity are disagreement on terms (32 per cent) followed by insufficient data (17 per cent) and slow decision-making (15 per cent).
“No matter what stage, a VC firm’s process is their IP, which pulls from a combination of data, personal networks and experience. For VC’s interested in leveraging more data and eventually ML/AI technology in their process, the key question they need to answer is whether it will help them overcome competition and generate stronger returns at the firm-level,” says Bendt.
He says, as evident from the survey findings as well, Asia-based VCs are the most enthusiastic about a data and technology-driven approach to VC. “Nearly half of Asian firms cite data as extremely important in decision-making, compared to 37 per cent of US firms and 33 per cent in Europe. Asian investors also had the highest percentage of machine learning adoption, with 29 per cent claiming to currently use AI in decision-making,” he explains.
Bendt, however, points out that it’s too early to determine if this approach will provide a competitive advantage for Asian VCs. “What’s clear is the industry will be watching closely as competition for quality investment targets intensifies.”