142. Beacon: An Engineering Systems Approach to Investing, Part 1 (Chris Farmer)
Oct 4, 2017
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Chris Farmer, Co-founder of Signal Fire Ventures, discusses their data engine, Beacon, and its unique approach to investing in startups. They address founder pain points, how Beacon drives value, and their research-driven approach to investing in emerging sectors using deep maps and machine learning.
Signal Fire Ventures developed a unique data engine called Beacon to analyze startups at sector and company level.
Signal Fire Ventures provides extensive support to portfolio companies through recruitment, business development, and customer acquisition.
Deep dives
Signal Fire's Unique Approach to Investing in Startups
Signal Fire Ventures, led by Chris Farmer, has developed a distinctive data engine called Beacon to source, analyze, and invest in startups. They have a team of world-class data scientists and use a vast array of data sources to track and analyze over six million companies. Their data platform provides insights at the sector, sub-sector, and even company level. Signal Fire focuses on addressing the four major pain points of founders, including team building, customer acquisition, and tactical expert advice. They also have a systematic approach to offering support to portfolio companies, including recruitment, BD, and customer acquisition.
The Redesign of a Modern Venture Firm
Signal Fire Ventures has taken a unique approach to venture capital by building a centralized infrastructure to support their portfolio companies. They provide resources for recruiting, business development, customer acquisition, and events, allowing founders to have a team player approach rather than relying only on individual partners. They have a dedicated team of experts in various functions and offer a high level of support to portfolio companies throughout their journey, from inception to the public markets.
The Power of Data in Venture Capital
Signal Fire Ventures has developed the data platform Beacon, which collects and structures data from various sources to provide insights for investment decisions. They use this data to analyze different sectors, sub-sectors, and individual companies. The platform helps them identify market trends, evaluate business models, and gain a comprehensive view of companies. While some startups may not have existing empirical data, Signal Fire leverages context from similar companies or industries to make informed investment decisions.
The Challenges of a Broad Investment Landscape
Signal Fire Ventures invests in a wide range of consumer and enterprise sectors. While the varied landscape poses challenges, their data platform allows them to customize their approach to each area. They focus on novel uses of data and technology to solve existing problems and look for indicators of success in each sector. Their platform combines machine learning and statistical analysis to provide deep insights and predictions, enabling them to make data-driven investment decisions.
Chris Farmer of Signal Fire Ventures joins Nick to discuss his firm and their platform, Beacon, a robust data engine that reveals the best sector, market and startup investment areas. We address questions including:
Can you start off with the firm's thesis and your main focus at SignalFire?
You talk about the four big founder pain points that you've observed... what are they?.
Number one pain point is Hiring top engineers... Tell us about his Data platform, Beacon, and how it addresses this pain point?
Sourcing and analysis tool that helps show you where to focus?
Can you give me an example of what you see when you look at a sector, sub-sector or even at the company level?
Does this data really exist with very early-stage startups or nascent sectors, before they've really emerged and have traction?
Sources?
I've worked for companies that had a difficult time getting one database to talk to another. Can you really source unstructured data, from limitless sources and structure it in a way where it's streamlined, uniform, single record and can be used to drive insights?
Do you think about data that is empirical and fixed vs. data points that can be influenced... and if you find an strong startup profile that is missing a couple of key elements that can be influenced, will you engage and attempt to address those factors w/ the founder?
I've looked over the consumer and enterprise sector lists where you invest... and it's a pretty broad list. Can you really have a data engine that works well for such a varied and broad landscape?
Essentially a sector by sector sensitivity and regression analyses?