In this episode of the Masters of Data podcast, I speak with another expert in the world of data whose brand uses data to help fuel the fire of innovation and technology growth. In this episode, I sit down with Jeremy Kaufmann, Principal at Scale Venture Partners. Scale Venture Partners is a venture capital firm that invests primarily in technology companies that are starting to bring in revenue, and who focuses on companies who are doing business on the Internet, in the cloud, on mobile, and delivering their product as a service, commonly known as SaaS. Scale Venture Partners prides itself on being a data-driven Venture Capital firm, and we discuss how Scale leverages data to make better investment decisions and to make their portfolio of companies more successful.
For starters, Jeremy offers some insight as to how he came into the world of data, specifically at a VC firm. “I would describe myself as somebody who’s always been fascinated by data and I think of data as…the search for truth,” he says. “I spend a lot of my time looking at the world of AI, looking [at the] progress in AI. And one of the things that I’ve come to learn in my career and what I’ve learned in venture is sometimes it’s always super important to bet on the mega trend. And when I think about what are the core trends affecting our lives today, I think [about] some of the progress that we’ve observed since 2012 with kind of the deep learning revolution [which] has been the most likely thing to affect the world around us.” As Jeremy alludes to, it is undeniable that the growth that tech and AI have seen as of late is something that is fueled by the demand for more and better data. And as Jeremy identifies, this was a primary reason for his joining the Scale team. “What really attracted me to Scale was Scale is a firm that’s very focused. It puts data first.” This makes him a well-needed contributor to the podcast.
While there are a growing number of VC firms, tech brands and startups fueling the current tech explosion, truth be told there are a lot of people on the field but few who are finding ways to differentiate themselves. But this is something that is a priority to Scale. “We like to invest in what we define as business software. So basically software being sold to a business buyer…And what we’re fascinated in is kind of making investments right when there’s a product in markets.” He continues to explain that, “We’re not taking…technical product risk, but what we are taking is kind of business distribution ‘go to market’ risk…So for us we are thinking a lot about market size, market trends and kind of what are the most efficient ways for companies to go to market.” And to make these evaluations about the brands they work with requires a lot of data. So who are some of the iconic brands Scale has gotten behind? Their growing portfolio includes brands like Box, DocuSign and Hubspot to name a few.
But how does Scale find ways to innovate and differentiate in a competitive field like technology? The answer is not surprising but is essential to their model; data. The team at Scale have built Scale Studio which is a tool they use internally to help identify the health of companies they are interacting with. “Scale Studio is a set of basically SaaS metrics that we make available to entrepreneurs and the broader community that help entrepreneurs compare their rate of revenue growth and churn and sales efficiency and cash burn to 300 other SaaS businesses over the course of 20 years.” The metrics they have compiled into the Scale Studio platform are essential for identifying how companies are doing at a critical stage in their development. “What we’ve found is that oftentimes entrepreneurs are looking to investors to be able to answer the question[s]: “So how am I doing? How can I be helpful? Tell me…what you think of my financial plan?” “So what we’ve done is we very explicitly built an app…that we showed entrepreneurs and…we’ve picked out the four metrics that matter most. We call [them] the four vital signs of SaaS and we think that this is the way that we can best help our entrepreneurs,” Jeremy notes. And this metric has been so vital to their overall success. So successful in fact that they’ve recently open-sourced the platform for use by companies who may never work with Scale. The reason? “We hope that Scale Studio and Scale as a brand will be kind of seen as a firm where kind of entrepreneurs seek truth and we help show that through numbers.” And a reputation like that is essential in the VC world.
Another topic of discussion between Jeremy and I is what’s often seen as the tension between intuition and data. While some in the VC sphere firmly stand behind what numbers reveal, others recognize that there is something to be said about using intuition. “I think the venture capital community is thinking about the same issues. I mean in the old world, venture was a game of intuition. And we’ve seen newer models come up with social capital. [And] firms like Scale are trying to move the needle in what we hope are entrepreneur-friendly and productive ways,” he notes. As with many things in life, there must be balance. The same can be said about navigating how best to use intuition and data. At the end of the day it is undeniable that the work that firms like Scale are doing are changing the landscape of the technology world and are making a world of difference in the success that brands are having when they come to market, all through the use of data.
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Jeremy focuses on investments across Scale's technology portfolio with a primary focus on companies adding machine…
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Jeremy Kaufmann - Principal - Scale Venture Partners | LinkedIn
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Learn more about the Scale Studio metric platform:
Scale Studio | Growth Benchmarking and Analytics for Cloud Companies
Scale Studio gives cloud companies performance benchmarks and analytics based on a custom database of SaaS metrics.
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- Data can help narrow in on what we call the truth.
- In venture capital, it’s always super important to bet on the megatrend.
- Since 2012 the deep learning revolution has been the most likely thing to affect the world around us at a faster rate than anything else we’ve ever seen.
- Scale likes to invest in what they define as business software; software being sold to a business buyer.
- They are making investments right when there’s a product in markets; not taking technical product risk, but business distribution, go to market risk.
- They are thinking a lot about market size, market trends and the most efficient ways for companies to go to market.
- Usually, this entails investing in series A or series B — usually the first or second round after the products is in the market and has revenue.
- Their portfolio includes Box, DocuSign, and Hubspot as well as more recent investments like Walk Me, JFrog, DataStax, Circle CI. They have brands both on the business application side and the infrastructure side.
- Scale is a firm that’s very focused. It puts data first.
- Many people over the years have talked about the role of data in venture capital and how the two can be linked together.
- The Scale Studio platform is SaaS metrics that are made available to entrepreneurs and the broader community that help entrepreneurs compare their rate of revenue growth and churn and sales efficiency and cash burn to 300 other SaaS businesses over the course of 20 years.
- The benchmarks are based on 20 years of SaaS data across 300 different SaaS businesses so providing that level of clarity is important to entrepreneurs.
- People have been talking about the conflict between intuition and data for decades.
- In the old world, venture capital was a game of intuition. And we’ve seen newer models come up with social capital. Firms like Scale are trying to move the needle in entrepreneur-friendly and productive ways.
- AI and machine learning are some of the most exciting areas Scale is focused today.
- In the world of AI, there are three markets that Scale is spending time in. The first being the world of conversational AI. The second being the increasing ubiquity of the camera. And the third being the world of software to help machine learning engineers more efficiently build, deploy and train machine learning models.