Venture Capital

Investing criteria: Can this company produce a +$1B exit?

This year, one of the things that I managed to do was to structure the way I evaluate a company – for investment, partnership or even as a potential employer. This investing criteria framework is a quick way to decide if it’s worth putting money in a company or not, based on a $1B exit goal.

The Investment Criteria

  • Strong Founding Team
  • Team diversity and size
  • Traction / Interest
  • Market size and size of pain
  • Investor signaling, valuation velocity
  • Timing

The Example Startups

  • Mux – video streaming API tool
  • PolicyGenius – insure-tech comparison marketplace
  • CrowdAI – data preparation for computer vision
  • Chatdesk – eCommerce customer support tool

Strong founding team

I look for signaling here – accelerators, incubators, good schools and diverse backgrounds. This de-risks some of you selection bias, since they have been through filtering before. You want to make sure the team is complementary enough and that they get along well. Serial entrepreneurs with successful exits are great, too, since they have provided investor returns before. I also look for relevant go-to-market experience and in-market expertise, when looking at founding teams, to ensure they are able to execute not just the product part, but the distribution.

Mux: YC, Brightcove alums, serial entrepreneur, team worked together before. Strong industry experience

PolicyGenius: Diverse founding team, ex-McKinsey, worked together previously, ex-Harvard educated

CrowdAI: Two people ex-Google, one of the founders had a minor exit in AdTech.

Chatdesk: ex-Google, McKinsey, Vanguard founders, but lacking go to market key people

Team diversity and size

This first investing criteria is important regardless of the size. More diverse teams produce better results in the long run, so going beyond the white male group is important for my investment criteria. I look at both the founding team and their early employees. LinkedIn is a good way to empirically test this, but Crunchbase has started adding diversity signals too.

Mux: Diverse team, but only white male founders. 61 team members to date, gender diversity in the engineering team. Good industry experience for most team members

PolicyGenius: Gender diverse teams, large organization, focus on people and operations, 395 people and growing

CrowdAI: Diverse founding team – gender, ethnicity; small team (20), mostly male; not significant industry experience i.e. strategy person coming from oil and gas

Chatdesk: Diverse founders and team – gender and ethnicity, global operations, strong data science backgrounds. Small team (26). Very little Go-to-market staff headcount and lack of industry experience

Traction / Interest

This is where the evaluation depends stage-by-stage, company-by-company and industry-by-industry. If there are visible signs of paying customers, like logos on the website, and notable case studies, then that’s always a great sign. I also try to look at their web traffic, trends, industry keywords where they show up via SEO, and signs of an active community – on Twitter, Discord, Github, LinkedIn, or any other platform. No activity, especially for community-dependent products, is a red flag.

Mux: Customers like Vimeo, TED, Udemy, Wistia, Reddit, Fox, Robinhood, Scale, Equinox, Amazon as channel partner; 140,000 visitors/month on their website, strong growth month over month, hot industry (API-based)

PolicyGenius: 1.2M visitors / month, over 50% US (core target market); new partnership announcements with the likes of Brighthouse Financial

CrowdAI: Low traffic, low time on site, high bounce rate, no marquee customers

Chatdesk: 13k/visitors per month, but mostly non-US, US traffic accounting for 20%. US growth in traffic recently (4x), a few niche customers in the ecommerce space on their website, as well as case studies, large number of integrations with existing support tools. AI powered

Market size / Size of the pain

Usually, if startups get this far down the decision making path, then they are on to something. But even if they have a great product, team and traction, the market might not be big enough for a $1B exit to happen. Or if the market is big, but the pain is not real – a nice to have vs a must have product, then they aren’t likely to grow into something huge. Competition is a good sign here, if they are able to differentiate and serve the pain in a more efficient, compelling or interesting way. If the product is actually a feature that most people in the target market can live without, then it’s safer to pass.

Mux: +$40B market size for streaming alone, and growing with COVID19 pushing people to rely on video for almost all interactions; Streaming is expensive and their pay-per-use system works for teams big and small

PolicyGenius: +$1 trillion market size in the US alone, hugely outdated and cartel-y; A few key competitors, like Lemonade, which recently went public. Large pain

CrowdAI: Large market (+$25B by 2025), big pain with computer vision being used more and more across all industries, and tech-assisted data labeling being a critical path to growth

Chatdesk: $73B market for customer service automation tools, however incredibly crowded and little differentiation among players. Relatively small impact doesn’t alleviate the customer service pain.

Investor signaling, valuation velocity

Again, this is stage dependent, and if you’re looking at this, then that means the previous hurdles were a pass. If I still have doubts about the market/traction/product/team, then this filter is pointless. When I look at investor signaling and valuation velocity (assuming there were a few funding rounds before the evaluation), I look for top tier VCs/Accelerators/Angels that put money into the company, are advisors. I also look for a stage-to-stage 2x valuation increase, or more. There are industries where valuations will lag early on and then as the company gains more traction, they shoot up. But if you see anything north of 2x round over round, then it’s a good sign.

Mux: Just closed a series C for $37M, with a total of $68.9M to date, with big names like A16Z, Accel joining Susa Ventures in later rounds. PE backing through a TPG Growth arm in Series B round; Valuation in the late tens of millions, early hundreds of millions.

PolicyGenius: 20 investors to date, $161M raised at triple digit millions valuation, including PE backing, signaling potential profitability, on route to unicorn status, strong traction for growth

CrowdAI: Only $2.1M raised to date in 4 years, small team, implied slow growth

Chatdesk: Only 2 funding rounds to date in 3 years, $2M raised, implied slow growth, small team

Timing Criteria

Last but not least, are they starting the company at the right time? Is there enough critical mass, infrastructure, capital, customer appetite to generate growth? This one is harder to quantify and it requires you, as an investor, to look at the market and try to understand its dynamics. Where is it on the Gartner Hype Cycle? Is there even analyst coverage for this sector? What signals can you read from key players?

Mux: Founded in 2016, they are positioned to grow with the COVID19 video tailwind, and have landed sufficient traction to attract even the most picky buyers. Their usage-based, AWS-style pricing model makes sense and they are well positioned to grow

PolicyGenius: Started ahead of the game in 2014, and now benefiting from insurance-tech adoption tailwind and positive COVID19 impact due to people moving away from brick and mortar agents

CrowdAI: Early to market in 2016, but didn’t scale fast enough, and are now being overtaken by competitors.

Chatdesk: Late to market, with a lot of customer service automation hype already captured by Salesforce Einstein and IBM Watson in 2016-2017.

Final verdict for Example Startups

Mux: High unicorn potential.

PolicyGenius: High unicorn potential.

CrowdAI: Low unicorn potential.

Chatdesk: Low unicorn potential.

So far, these investing criteria filters have proven useful for my angel investing and advisory engagements. I’m always looking to improve them, so if I missed any other criteria that you found useful, comment below. I’d love to listen to other perspectives on this.

Photo by De’Andre Bush on Unsplash

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