Ensemble raises $100M debut fund to bet on startup teams — but not in the way you think
Almost any venture capitalist will say that they back startups based on the strength of the founding team. But what about the rest of a startup’s employees? New VC shop Ensemble thinks you need to go beyond the founders to see if a team will actually make a startup successful.
Collin West, a co-founder and managing partner at Ensemble, told TechCrunch that unlike other VCs, his new venture capital outlet determines if a startup is a good investment based on the depth of its entire team. The firm uses an in-house, data-driven algorithm to narrow down potential investments based on the entirety of a startup’s employees. The Texas-based firm has raised $100 million for its debut fund to try and prove this idea.
West said that when Ensemble’s founding team — which also includes Conrad Shang, a former venture LP at UTIMCO, and Gopinath Sundaramurthy, previously a data scientist at IBM — was trying to nail down what it was about startups that made them stand out from others in the same sector, they all agreed that it was the companies that were able to attract the best talent.
“Even if you do meet the company, you are only meeting the founder, and not the rest of the team,” West said. “A lot of the time, the founders are not the most interesting people on the team. Companies are buckets full of people — people who decide to work on a specific mission together.”
Looking for startups that are good at recruiting isn’t exactly a novel approach, but West said that it’s pretty difficult to actually pull off: Choosing a sector and then going through each company’s entire team is laborious, largely a waste of time, and essentially impossible to scale.
To combat this, West said the Ensemble team built a data algorithm that tracks employees at a firm and helps narrow down companies with investment potential based on the depth of their team. The human members of the Ensemble team start diving in from there. West pointed out that this data-driven approach isn’t a silver bullet, though, which is why the team still conducts regular due diligence; Ensemble gets a smaller curated list as a jumping-off point.
“Using software, we can track all of the people at all of the startups,” West said. “That ends up giving a whole lot more information than any human brain can handle, especially any venture firm. [We] effectively sort the industry by team quality in a very objective way, knowing what companies to focus on and spend a lot more time on a lot fewer companies.”
Because the algorithm focuses on teams and not on metrics that may be more or less relevant to specific sectors, it allows Ensemble to be industry agnostic. West said one example of that would be the firm’s investment in 3D home printing startup ICON Technologies.
“It’s an incredible company to work with, but it is a big mission, and changing the way home construction was done. To a lot of VCs, it was too big of a risk,” West said. “The beauty of using data is it helps you push your bias away and show up with curiosity.”
The data doesn’t stop at due diligence, either. West said one of the firm’s value-adds for their portfolio companies is that after they invest, they launch a full report on a company’s team. The report is meant to find the bright spots, or areas of improvement, to give the startup the best idea of how to hire in a way that could directly affect growth.
The firm’s focus on teams comes at an interesting time in the market as the industry grapples with both layoffs and a war for talent. While every firm is trying to track where the best laid-off professionals land, Ensemble’s data-driven approach may help it keep better tabs than traditional VCs. But that doesn’t guarantee a stronger portfolio.
“We just want to be where the great teams are, full stop,” West said. “That is the beauty in having a data-driven team approach. Every company has a team.”
Ensemble raises $100M debut fund to bet on startup teams — but not in the way you think by Rebecca Szkutak originally published on TechCrunch