Do Burn Rates Matter?

June 13, 2015

10:00 pm

The tech blogosphere went alight when Bill Gurley stated that private companies are raising giant sums of money – too much money, some as much as $500 million. In the interview with The Wall Street Journal, Gurley noted that “companies are upping their ‘burn rate’ or the amount of money they’re willing to lose to grow their businesses.”

There’s no argument that with ever-rising valuations and tons of money in the bank, Silicon Valley companies are raising their burn levels to stratospheric heights. Let’s, however, start with a basic question: what’s the typical burn rate in Silicon Valley?

According to Fred Wilson, you simply multiply $10K by number of team members. Seems reasonable, although yearly rising costs of living make that number a little higher.

A web search for burn rates revealed a thread on Danielle Morrill’s blog last year that came up with widely varying results in various industries.

What was obvious in the numbers is the variation between amount burned versus total number of employees; amount burned in revenue making versus non-revenue generating companies; amount burned versus total cash in the bank; and amount burned versus total funds raised.

For example, a SaaS company burned $204K with 12 employees for a $16K per employee spend. Runway (monthly operational cash flow) based on $2.5M cash in the bank was a little over 10 months. But with projected monthly recurring revenues, the runway increases to 17 months. The second example, also SaaS, had $240K burn, 24 employees for an average of $10K per employee spend. Runway based on $960000 cash in the bank is a mere 4 months. But they broke even and are self-sustaining with monthly run revenues of $370K. A third example burns $35K a month, with six employees for an approximate average of $6K per employee spend and raised a total of $1M. Runway is a little over 30 months. Conservative burn? Or perhaps is there a point in the employee count where the Wilson method makes sense to be applied since private companies are shedding staff even as they hit product market fit?

Correspondingly, can it be argued that with a minimum threshold employee count in place, the more mature/capitalized the business the more efficient their ability to drive down spend per employee? Certainly not so in practice though from the tales of burn baby burn and costly 10 year leases in the Valley. Wilson reminds us that Tumblr had an annual burn equating to 50 percent of the total Union Square Ventures fund.

To arrive at a benchmark, what if we used a simple measure with numerator (burn rate) and denominator (total amount raised and name it burn rate utility)? That would shed some insights on an alternate method below 10 employees. So in the last example, that company burned 3.5 percent of total available funds. What’s even compelling is when burn rate utility is plotted against spend per employee.

Finalizing with a small sample of anonymized data from Europe (to adjust for variation, minimum raised is between $500,000 – $3M)

Company A: $100,000 burn; $2.5M total cash funding; 10 Employees, Software as a Service business

— Burn Rate Utility: 2.5% (0.025); Per Employee spend: $10K

Company B: $110,000 burn; $1.4M total cash funding; 14 Employees, Publishing business

— Burn Rate Utility: 8% (0.08); Per Employee spend: $8K

Company C: $70,000 burn; $700,000 total cash funding; 15 Employees, Software as a Service business

— Burn Rate Utility: 10% (0.10); Per Employee spend: $4700

burn rate

As you will see in this very small sample for illustration (European companies), as more money is raised, ideally (up to certain point) the lower the burn rate utility. Correspondingly, employee spend should move in a linear matter. In this example, company B (the publishing business) is an outlier, trending upwards right (burn rate utility/employee spend nexus is not great relative to other companies). Disclaimer: Some companies will always have unique circumstances depending on other operating/market variables. This post originally appeared on ChalkRow‘s blog.

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Chaney is Founder and CEO of VendorMach, a supply chain trust scoring platform. Former technology integration and risk product lead at Humana Inc, he is a sought after speaker on big data and AI trends. He has degrees from Booth, Kings College London and Howard University.

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