Using data science to reshape the VC industry

On Apr 11, 2022

One of the ironies of venture capital is that, although it is one of the most powerful mechanisms in modern society for catalysing innovation – particularly in tech – the industry itself is not particularly innovative. Venture capital is a business that historically, and even now, is deeply rooted in personal relationships, intuition, reputation and a very analogue flow of ideas and information.

Of course, to some extent, venture capital will always be a people business, focused on fruitful human connections and networks that bring the right entrepreneurs, investors and operators together to make an idea sing.

But as we look forward, the dominance of analogue, purely intuitive approaches to early-stage investing might be at an end. With the tools at our disposal, even now, we can eliminate major areas of uncertainty that currently – frankly – require guesswork, and which slow down decisions unnecessarily.

The potential of data science for VCs

Fundamentally, our business is sourcing and securing the most promising investments. Betting on the right horse is what motivates us VCs and watching that investment go on to flourish is why we do what we do. But intuition alone cannot be our only methodology for success, we must branch out.

Incorporating data science holistically across the venture capital process can enable us to make more informed decisions, faster. How? We can better identify promising areas of growth by evaluating categories or trends that are gaining momentum and finding potential companies within those spaces or identifying the fastest growing companies that are flying under the radar of institutional investors.

In my experience, companies sourced with data science significantly outperform companies from other sources, both in terms of valuation uptick and likelihood to raise. Then, with our target marked, we can use the algorithms we’ve built and nurtured to analyse companies against their competitors, to ferret out which have the most potential, and if the timing is right for us to engage them.

While data gives us a more defined path for sourcing companies to bring into our portfolio, we’re also able to then turn our insights into helping our portfolio companies grow faster. We can identify areas where the company could be more successful, guide them towards this and cherry pick the perfect candidates to join them on their journey. Henceforth, becoming a true partner and cheerleader for our portfolio clients, improving both our roster and reputation simultaneously.

Let’s have a look at each of the aspects data science elevates VCs in more detail.

Better predict the future

Right now, roughly 90 to 95 per cent of decisions happening in venture investing are human, but by 2030, that will drop significantly – I’d estimate to 50 to 60 per cent. Why? Because as AI becomes more sophisticated, it will be a key differentiator for how VC firms operate – further separating those using it to great effect to those who are not. In an industry that has largely depended on who you know and good word of mouth for finding opportunities on the brink of disruption, data science sharpens that lens and makes the identification process faster and more comprehensive. In short, making more informed decisions, quicker.

Algorithms at this stage can be used in a myriad of ways – whether it’s identifying an emerging category that is gaining steam, to identifying competing companies in a particular space, or even if a company is at the right stage for investment. Data Science insights offer the ability to “travel in time,” analysing the past and current state of a potential company – alongside millions of others – to better predict how it’ll look in the future.

By using data science to firstly identify and then validate prospects, VCs are better equipped to make decisions quickly and confidently – and for investors, this instills trust in the choices put before them, knowing that the promise of these prospects is rooted in hard evidence, instead of just impassioned sales pitches.

Greater revenue potential

Simply providing a portfolio company with the right amount of capital is not always enough to guarantee its success, let alone survival. To accurately support their growth and increase their revenue, data science can be useful in sussing out the peaks and pitfalls of their performance.

Using machine-based analysis, we can better understand a company’s ecosystem, and evaluate both internal and external metrics against competitors, thereby finding areas for improvement. For example, this can extend into operational levers such as marketing, to uncover vulnerabilities, or unlock sources of strength that can be exploited to even greater effect. Here, imagine data science as a lens through which we can view the entire environment, guiding what – beyond financial backing – could potentially make a company more successful.

Accurate talent sourcing

A key ingredient to any successful company is its people. When investing in a younger company, this is even more true as they will bring the necessary skills, leadership and relationships needed to make it thrive. While there can never be a full substitute for knowing someone from experience beyond their resume or LinkedIn profile, data science can greatly shrink the candidate pool to more easily find that all important needle in a haystack. Imagine, instead of simply asking trusted colleagues for who may make for an effective CMO, using data to evaluate a person’s past performance to determine how their skillset could support the unique needs of this open position. Using data science, we can make better connections so our portfolio companies can make the smart, strategic hires who can deliver.

Venture capital is a business of people. That will never change. It’s the connections sparked between passionate and purpose-driven entrepreneurs, and inspired investors that will always make great ideas a reality. However, with the technology we now have at our disposal, those sparks will become less of a moving target, and a greater certainty – using data to mine out opportunities, and then build the right ecosystem around them.

Jonathan Serfaty is the head of data science at Telstra Ventures.