Throughout this anniversary blog series, we’re exploring how the VC industry has evolved since Telstra Ventures came on the scene ten years ago, and where we think it’s headed. In this post, we want to look at an important trend that has been a key part of the bedrock of Telstra Ventures since it was founded. While in its infancy industry-wide, we still believe this trend will have an immeasurable (pun intended) impact going forward on venture capital: data science.
One of the ironies of VC is that, although it is one of the most powerful mechanisms in modern society for catalyzing innovation – particularly in tech – venture capital as an industry is not, itself, particularly innovative. Ours is a business that historically, and even now, is deeply rooted in personal relationships, intuition, reputation and a very analog flow of ideas and information.
And 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, and consider our own approach to the business, we suggest that the dominance of analog, 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.
At Telstra Ventures, we have built data science into the backbone of our business in a variety of ways with the goal of giving our investment team superpowers. We use it to make more informed decisions, faster, and it’s one of the major things that sets us apart from competitors. Why? Because we built it completely from the ground up, in house. We knew that buying an off-the-shelf product would only give us off-the-shelf results: if we used the same tools as everyone else, we wouldn’t have an edge.
Using our data team’s combined experience at Silicon Valley tech companies and quantitative Wall Street hedge funds, we were able to build a platform to guide our decisions around sourcing, due diligence, and in support of our portfolio companies. To paint a clearer picture of just how data science can work on behalf of VCs, we’ll pull the curtain back a bit on how Telstra Ventures has successfully used it to-date.
First, we’re able to use data science directionally to identify promising areas of growth. This could mean 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 fact, we’ve seen that companies sourced with data science significantly outperform companies from other sources, both in terms of valuation uptick and likelihood to raise. With targets marked, we’re able to analyze companies against their competitors, to ferret out which have the most potential, and if the timing is right for us to engage them. Once we’ve entered conversations with a candidate, our platform can automate financial diligence and benchmarking while our investors perform traditional team, product, commercial and competitive due diligence.
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 by identifying and making introductions to sales leads and potential hires that align with their operational needs.
We’ve been hard at work building and refining this data practice for five years, and given the successes we’ve seen, we believe we have a unique window into the future of how data science will support the VC landscape. It’s a specialized and exciting way to support both our investment team and our portfolio companies. Here’s how.
Right now, roughly 90-95% of decisions happening in venture investing are human, but by 2030, that will drop significantly – we’d estimate to 50-60%. Why? Because as artificial intelligence (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. That will be most evident to how we’ve applied it at Telstra Ventures, as other firms begin using data as a tool for finding the right companies to invest in. 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, more quickly. 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. Our insights offer the ability to “travel in time,” analyzing 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.
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.
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 the 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 connection 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.
At Telstra Ventures, we look at data science not in place of our people, but as something that enhances them. And we’ve got big plans for it – as we continue to develop our analytical capabilities – giving our team and our entrepreneurs even more super powers to do their jobs better than anyone else.