We partnered with Crunchbase and AngelList and spoke with nearly one hundred early-stage founders who had raised seed capital, or been part of an incubator or accelerator, to better understand the growth benchmarks that are predictive of success and the company types these benchmarks are most relevant to. Our research tells us that much of ‘growth dogma’ can be misleading, incentivizing founders and VCs to optimize for the wrong things.

Aligning early hires behind the key strategic objective of a company is critical to winning a market. Companies with ‘team-problem fit’ have identified a winning playbook and relentlessly hire in service of that playbook.

One common misalignment we observed was a mismatch between a team’s winning playbook and engineering concentration. Startups whose winning playbook was not primarily dependent on engineering, but had a high concentration of engineers, tended to be low-growth. It may be that without an understanding of team-problem fit, some founders default to hiring surplus engineers to advance product when it’s not their most pressing problem.  

While all startups need engineers, high-growth startups were more likely to buck the dogma that more engineers is always better. Instead these high-growth companies pushed the concentration of engineers down over time by hiring other high-leverage talent. On average, high-growth companies we surveyed began increasing the concentration of non-engineering hires after their 6th employee.
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So what non-engineering talent was most often hired by high-growth companies? Diving deeper into the nearly 10,000 job postings on AngelList last year, for the fastest hiring companies with fewer than 10 employees, we found that content, design, product management, customer success and operations roles were over-represented.

Notably, these roles are all common early hires within product-led businesses. Product-led growth is on the rise and often preferable to top-down sales (when possible). But regardless of what go-to-market motion is realistic for your company, it’s important to align your team to advance your company’s winning playbook.

It’s easy to fall into the trap of growth at all costs as an early-stage startup, but over-optimizing for short-term growth can lead to difficulty. High-growth companies have a strong understanding of their target customer and remain focused on that customer even if it means forgoing some revenue.

This pattern can be observed clearly in metrics like pilot-to-contract conversions. Overall, high-growth companies had a lower pilot-to-contract conversion rate than their lower growth peers. But we found that this discrepancy largely disappeared when we removed a cohort of largely low-growth companies with perfect 100% conversion rates.

High-growth companies with lower pilot-to-contract conversion rates explained they fired customers when necessary and decided against deployments they thought wouldn’t scale. This bucks the dogma that a higher pilot to contract conversion percentage is always better.
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Companies that ship new features every two weeks or faster are more likely to be high-growth. Interestingly, we found no advantage for companies shipping multiple times per week compared to companies shipping weekly. The likelihood of being high-growth did drop-off for companies shipping monthly.

The future isn’t Austin or Miami, it’s remote! Across our entire data set representing over 1,000 startup employees, founders expected 66% of employees to remain remote after Covid-19 subsides. Interestingly, this expectation was not impacted by whether a company was high-growth or low-growth.

CAC matters for all businesses. While it may not be true that lower CAC alone guarantees success, excessively high CAC kills businesses. Across all company types, high-growth companies we surveyed on average had 5x lower CAC when compared to low-growth companies.

Customer engagement is a valuable proxy for the value a customer is getting from a product. As a result for many businesses, including top-down enterprise SaaS startups, user engagement is correlated with growth. Among top-down enterprise SaaS startups, 67% of companies we surveyed commanding over 60 minutes of user engagement per day were high-growth. In contrast, only 38% of companies fitting this profile with 60 minutes of engagement or less per day were high-growth.

But engagement isn’t always the best predictor of growth. For transactional businesses (i.e. marketplaces) metrics like CAC, speed to MVP and speed of shipping new features are more correlated with success. Surprisingly, among these businesses, lower growth companies had higher engagement. It’s possible that friction in user experience dragged out engagement at the cost of retention.

Freemium is a high risk, high reward go-to-market strategy for self-serve businesses. Only 18% of freemium companies we surveyed were growing fast. Yet of the high-growth companies successfully executing a freemium playbook, growth averaged 20% MoM.

These companies universally demonstrated top-tier growth metrics including efficient CAC. These businesses reported shipping MVPs in an average of two months, shipping features multiple times per week and having engineers talk directly to customers on a weekly or bi-weekly basis.

When compared to their less technical enterprise SaaS peers, deep tech companies require both large teams and highly disciplined product and growth leadership.

Only 13% of deep tech companies we surveyed, that were actively selling their product in market, had achieved high growth. These companies averaged 52 employees whereas their lower growth peers averaged only 12 employees. Critically, high-growth deep tech businesses universally employed the best engineering practices of shipping new features and having engineers talk to customers on a monthly or faster cadence. Meanwhile, only 25% of low-growth deep tech companies in our dataset were able to achieve these milestones.

Deep tech companies have to execute on ambitious roadmaps with tight runways, leaving little room for delays. Establishing engineering best practices is essential given these companies, on average, take longer to build MVPs and may have fewer opportunities to iterate with customer feedback.

As this report conveys, there is no one-size-fits-all recipe for growth. Growth is achieved by aligning limited company resources with the dynamics of a market. Not every company fits neatly into one of our paths to high growth. Your best bet is to identify the path your company is most closely aligned with, and then evaluate which targets you’re not meeting. If you can justify why a metric isn’t a company priority, you may be right in de-prioritizing it. After-all, optimizing for too many metrics simultaneously can lead to failure. We’re hopeful that this research will be helpful to early-stage founders. No two companies are alike so the more data we have, the more granular our results will be.
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1. Founders, contribute to our research by sharing your growth metrics. All data will remain confidential and takeaways will only be shared using aggregated data.
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2. Everyone, sign up to receive additional updates from BSV Research including information on a similar report we published last year uncovering traits leading to founder success.

Data was collected from 85 early-stage founders, including 79% enterprise and 21% consumer startups across business models including SaaS, marketplace and deep tech. Companies surveyed served markets including enterprise, smb, traditional industry, real-estate and education.

Our AngelList data set included 15,549 job postings from Jan-Dec 2020. Only companies with fewer than 50 employees were included in our data set. We classified job postings into 48 categories.

High-growth companies are defined as averaging 10% MoM revenue growth over a 3-month period. Low-growth companies didn’t meet this bar.

Fast-hiring companies are defined as having 10 or fewer employees and attempting to hire for 6 or more unique roles in a 12 month period. Slow-hiring companies do not meet this bar.

We asked a dynamic survey to founders in a 1:1 setting over Zoom. Questions were pulled from a bank of 43 total questions and asked based on how companies self-identified their business model, target market and early growth milestones.

We performed a hierarchical cluster analysis to find similar clusters within the data and discovered archetypes for growth.