Telstra Ventures And Data Science-Driven Investing

Telstra Ventures is a San Francisco-based venture capital firm that invests primarily in tech firms. As its name suggests, it was originally funded by the Australia-based telecom firm Telstra, though it has other institutional investors as well. When it was founded in 2011, like most venture capital firms it invested in companies on the basis of portfolio themes, early financial results, and detailed financial analysis.

Unlike most VC firms, however, today it has incorporated data science into its mix of investing criteria. Despite the fact that VC firms often invest in startups with a strong focus on AI and data science, relatively few use data and quantitative analysis of non-financial data as a guide to whether to invest. Some VC firms, including Andreessen Horowitz, Sequoia, Signalfire, Tribe Capital, and Google Ventures, have taken steps in this direction. Some focus more on data science as a way to identify investment opportunities, while others help their portfolio companies to build AI capabilities.

What possessed the leaders of Telstra Ventures to stop using only their gut and spreadsheets? They had something of an epiphany in 2017: if the firm believed that data science capabilities were important in the companies in its portfolio, why wasn’t it using those capabilities in its own decision-making? Their first step was to recruit Jonathan Serfaty, a data scientist who had previously worked at LinkedIn.

Mark Sherman, the firm’s managing director, was the primary advocate for hiring Serfaty, and he explained that it’s been a gradual process to adopt the new methods. “We’re layering in more data science over time,” he said. “Now it pervades our investment process as well as the value we add to portfolio companies.” He explained that data science wouldn’t work well for all types of venture investing. It doesn’t work well, he said, in early stage investing, where “there isn’t enough digital exhaust yet.” But mid-stage companies ($1 to $20 million in revenues), which is his firm’s focus, have customers, revenues, employees—in short, they generate some data. With later investments just prior to IPOs, there is even more data to analyze.

The Data and Models

Serfaty, who is now the firm’s head of data science, had to start from scratch when he arrived. The firm had no data that could be used in training models. He began to crawl the web for public information that might be useful in models, and bought data from external providers. Eventually the firm’s repositories held data on millions of companies, but then the real challenge began. They needed to clean, integrate, and organize the data before it could be used to train models. Serfaty said that about 95% of the firm’s data science efforts, at least early in his tenure, went toward data preparation.

Now Telstra Ventures has not only a data store, but an automated pipeline that continually refreshes data on companies. They gather and analyze a variety of different metrics to assess a company’s theme, momentum, market, team, and any other factors that might correlate with success. They are always bringing on additional data as it seems relevant and potentially predictive.

Mark Sherman said that the firm has a clear philosophy that governs its use of data science. One aspect of it is that “people and data work together”—they always look for harmonization between the results of models and the thoughts of investors (both the general partners and more junior staff). They try to give their investors “superpowers” by augmenting their capabilities. The models do generate a score—or actually several of them from different and uncorrelated models. The scores are used to help investors decide whether to investigate a company or not, and then to benchmark investigated companies against each other. Each score is explained in terms of the key features or variables that had influence within it; that way investors can more easily interpret the scores.

For example, some of the environmental scanning done by investors and AI systems suggested that cybersecurity insurance was a segment that was growing in importance several years ago. Serfaty evaluated and scored 28 different companies in the segment. Three companies were identified as being of particular interest, and Telstra Ventures performed a detailed due diligence process (“Like the CIA or MI6,” Sherman said) on them. The firm decided that one firm—Corvus—was worthy of their investment, and the company has done very well since then. It has grown rapidly, established several valuable partnerships, and acquired a business unit of Lloyd’s. Other companies identified in this fashion have also done quite well relative to traditional investment criteria, as a VentureBeat post documents.

Not all of the investors at the firm are highly quantitative; many of them have a traditional financial analysis focus. But Sherman said that there is broad acceptance of data science as a useful addition to the investment decision process. Those investors who do have a strong quantitative focus tend to get more involved in the analysis process. For new hires, “a twinkle in the person’s eye as it relates to data science” (Sherman’s term) isn’t essential, but is considered positive.

AI Help for Portfolio Companies

Telstra Ventures is not only using AI to help make investment decisions, but also to help the companies in its portfolio grow and prosper. After several years of investment analytics, the leaders of the firm deliberated on how they could add value to the portfolio with their data science capabilities. They felt they had two primary assets in that regard: a giant database of company metrics, both financial and non-financial; and a relationship graph of various people and entities within the global economy. They decided that these two resources could be combined to help portfolio companies with demand generation.

The most direct form of assistance with demand generation is identifying customer accounts that portfolio companies should pursue. They may be companies that are competitors of their customers, or companies with individuals having close ties to customers or to the Telstra Ventures-funded company. Telstra Ventures also advises portfolio companies on opportunities to sell through Telstra Corporation to customers they might not otherwise reach. Mark Sherman noted, “Venture firms can add value to their portfolio companies in a variety of ways—hiring, PR, benchmarking, etc. We decided that helping our companies with revenue generation—identifying revenue-bearing relationships—was the area of maximum leverage for us.”

It seems inevitable that other venture firms will begin to make more data and analytics-driven decisions in the future. But by then, Telstra Ventures and the few other firms that employ these tools will have accumulated more data, built better models, and learned more about how best to combine algorithms with human experience and intuition.