One way to survive a valuation slump is to grow
Unicorn for big data analytics Databricks is in the news again, announcing a new revenue figure and growth rate for 2021. TechCrunch has been following the company for years, curious about its growth and what its rising value says about its market. Today we revisit the latest private round as measured by the most recent financial data. But to do that, we need to do some background work first.
When Databricks raised a $1.6 billion round against a $38 billion hindsight valuation last August 2021, TechCrunch got on the phone with CEO Ali Ghodsi to chat through his company’s latest mega-raise. We had a few questions.
One of mine was how he felt about the inherent pressures that such huge valuations seem to create in the private market – starting valuations are, after all, estimates until they’re discontinued, meaning higher prices mean greater expectations for future success. Ghodsi didn’t sweat.
He said at the time that he didn’t feel much pressure and that he slept well.
He gave a number of reasons for this. First, according to our notes from the conversation, his belief was that his company is really building a new category of service. Second, he hadn’t maximized for appreciation in the fundraising event, and that in both rounds of his business in 2021, there was more demand to put in capital than there was room to accept it.
The above is a bit of standard CEO fare when it comes to startups and unicorns in a hurry. More interestingly, his third point was that fast-growing companies – he cited a 75% growth rate as a point of contention – can overcome market corrections through growth. In simpler terms, if the market changed its tone about the value of software revenues, as long as Databricks continued to grow, everything would be fine.
Well, the market did change since that conversation, with the value of software revenue being re-priced by the public markets from late 2021 and continuing into early 2022. And Databricks continued to grow.
So we can have a little fun this afternoon by calculating the company’s revenue multiples in August and at the end of the year using current market data. The experiment will show us how much, if any, Databricks has to go through before valuation in the private market can translate to the public markets on a 1:1 basis. I promised myself I’d stop making “when will Databricks make public jokes”, so let’s start doing the math.