How Telstra Ventures uses data science to improve venture capital investing
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The venture capital industry has played a vital role in fast-growing cutting-edge technologies. Yet it has been a laggard when it comes to embracing the new technology itself.
About five years ago, Mark Sherman, CEO of Telstra Ventures, decided to change that by strengthening its data science team. Telstra Ventures has hired Jonathan Serfaty, a former LinkedIn engineer, as head of data science. Serfaty had worked on LinkedIn’s lead prospecting pipeline, which matched well with the deal pipeline used by VCs.
It took a few years to get things off the ground, but Telstra Ventures is already starting to see impressive results:
- Telstra Ventures now sources 15% of new deals from data science recommendations and data science tools have informed 100% of all deals since 2020.
- 57% of data science transactions generated an additional turn within the year, compared to 33% for transactions from the old method.
- Data science source transactions grew fourfold in reported valuation, compared to a 2.4x increase for transactions from traditional channels.
The new approach is still in its infancy, but it is extremely promising. Sherman expects the company to be able to find up to half of its new contracts using the latest data science techniques within five years. This approach works because Telstra Ventures focuses on companies that have already done enough business to generate a data lead.
“It wouldn’t work as well if you tried to do the same thing with pre-seed and seed funding, because there’s not as much digital exhaust,” Sherman said.
What to model
Creating a digital model of a startup in an emerging market is a bit more complex than modeling a public company in an established market, Serfaty told VentureBeat. It has invested significant resources in tools for exploring the Internet in search of public information and organizing the appropriate mix of third-party data services.
They have developed metrics to characterize how companies interact with customers, their growth rate and the connection between players in a market. Serfaty said: “There is so much hidden and unknowable information. We look for proxies that are at least directionally good enough to be useful.
Many of these models take advantage of graphical data modeling techniques that Serfaty has worked with to improve lead prioritization for LinkedIn’s sales team. He told VentureBeat, “We measured a lot of signals from inbound accounts and leads to figure out how to prioritize leads for the sales team. It’s a similar problem to what we’re doing here.
Improve the venture capital pipeline
A venture capital deal pipeline has three key components: sourcing, benchmarking, and adding value. Supply is the process of detecting momentum within a market segment. Benchmarking is comparative financial analysis to understand a company’s strengths and prospects. Adding value is about finding ways to improve business prospects or value. Telstra Ventures has developed data science tools to improve these three processes.
With sourcing, the traditional venture capital approach is to rely on inbound or outbound lead generation. An inbound process may involve gaining exposure in a field that attracts startups in that field. An outbound approach is to research the market and network to find businesses in a specific area.
The data science effort helps identify and prioritize candidates for outreach. This takes advantage of multiple proxies that correlate with various success metrics, but are easier for startups to measure. That’s 15% of companies with the outsized returns mentioned above.
Data science teams also help investors evaluate companies identified through other channels before proceeding further.
“Data science touches every investment we make, whether inbound or outbound,” Sherman said.
Telstra Ventures also makes extensive use of new data science tools in the benchmarking phase. Although venture capitalists have always done analytics, the latest data science workflow takes things to a new level. For example, the data science team has developed tools to generate over two hundred KPIs that can help compare the performance of different companies.
According to Sherman, ten years ago most decisions were based on intuition. Now, by comparing this much richer set of metrics, his team has a much higher confidence interval to make investment decisions.
The data science workflow also helps Telstra Ventures improve the value-add phase by identifying specific weaknesses to mitigate and opportunities to pursue.
Telstra Ventures specializes in helping businesses cultivate more revenue-generating relationships. Serfaty’s team has developed various graphical analysis tools to identify and prioritize pre-qualified prospects and determine the right contact to get the ball rolling.
It took the Telstra Ventures team a while to figure out how these new data science tools could fit into their workflow. Now investors are starting to suggest adjustments for better models and new metrics to track, Serfaty said.
For example, investors have requested network information to help them understand how they are connected to a company and who to contact for an introduction, as well as tools to help research and map sectors to thematic research.
“Additionally, as the VC landscape evolves, we’ve received suggestions on how we can monitor and evaluate Web3 businesses,” Serfaty said.
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