How Telstra Ventures uses data science to improve venture capital investing

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The venture capital industry has played a key role in the rapidly evolving, sophisticated technology. However, it lags behind when it comes to adopting new technology.

About five years ago, Mark Sherman, managing director of Telstra Ventures, formed his data science team and set out to replace it. Telstra Ventures has appointed Jonathan Surfetti, a former LinkedIn engineer, as its head of data science. Serfaty was working on LinkedIn’s lead prospecting pipeline, which maps well with the use of Deal Pipeline VC.

It took a few years to get things off the ground, but Telstra Ventures is already beginning to see some impressive results:

  • Telstra Ventures is now sourcing 15% of new deals from data science recommendations and Data Science Tools has provided 100% information on all deals since 2020.
  • 57% of deals sourced by DataScience added an extra round in one year, while 33% of deals sourced in the old fashioned way.
  • The data science source deal saw a fourfold increase in valuations, compared to a 2.4 times increase in sources using traditional channels.

The new approach is still in its infancy, but it shows tremendous promise. Sherman expects the company to source up to half of their new deals in five years using the latest data science technologies. This approach works because Telstra Ventures focuses on companies that are already doing enough business to generate a trail of data.

“Even if you’re trying to do this with pre-seed and seed funding this won’t work 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 little more complicated than modeling a public firm in an established market, Surfetti told VentureBeat. It has invested considerable resources in Internet crawling tools for public information and curated the right mix of third-party data services.

They have developed metrics to show how companies connect with customers, their growth rate and the relationship between market players. “There is a lot of information that is hidden and unknown,” he said. We’re looking for proxies that are good enough to be useful, at least in a directional way.

Many of these models take advantage of graph data modeling techniques that work with Serfaty to improve lead priorities for the sales team on LinkedIn. He told VentureBeat, “We measured a lot of signals from incoming accounts and figured out how to prioritize leads for the sales team. The problem is the same as what we are doing here. “

Improvement of Venture Capital Pipeline

The Venture Capital Deal pipeline has three main components: sourcing, benchmarking and value-added. Sourcing is the process of sniffing for momentum in a market segment. Benchmarking is a comparative financial analysis to understand the strengths and potential of a company. Value-add involves finding ways to improve the prospects or value of companies. Telstra Ventures has developed data science tools to improve all three processes.

Along with sourcing, the traditional venture capital approach relies on inbound or outbound lead generation. The inbound process may involve becoming known in the domain attracting startups in that area. The outbound approach involves researching the market and working on a network to find businesses in a specific area.

The Data Science endeavor helps identify and prioritize candidates for outreach. This takes advantage of many proxies that relate to different measure of success, but are easier to measure for startups. These are the 15% companies with the outsize returns shown above.

Data science teams also help investors evaluate companies identified by other channels before moving on.

“Data science touches every investment we make, whether it’s inbound or outbound,” Sherman said.

Telstra Ventures also makes extensive use of new data science tools in the benchmarking phase. Although VC firms always do analytics, the latest data science workflow takes things to a new level. For example, the Data Science team has developed tools to generate over 200 KPIs that can help compare the performance of different companies.

According to Sherman, ten years ago, most decisions were based on intuition. Now, compared to this more affluent group of metrics, their team has a much more confident interval in making investment decisions.

The Data Science workflow also helps Telstra Ventures improve the value-added phase by identifying specific vulnerabilities and opportunities to move forward.

Telstra Ventures specializes in helping companies cultivate higher-income relationships. Surfetti’s team has developed various graph analytics tools to identify and prioritize pre-qualified prospects and determine the right contact to get the ball rolling.

It took a while for the Telstra Ventures team to figure out how these new data science tools could fit into their workflow. Now investors are starting to suggest better models and adjustments for new metrics to track, Serfetti said.

For example, investors have sought network insights to help them understand how they relate to the company and who to contact for introductions, as well as tools to help them find and map out areas for themed research.

“Furthermore, as the VC landscape evolves, we’ve received suggestions on how to monitor and evaluate Web3 companies,” Serfaty said.

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