Earlier this week, we covered Blackbox, the young company responsible for creating The Startup Genome Report, an ongoing, collaborative R&D project designed to take a comprehensive dive into what makes Silicon Valley startups successful — and not. (Read our initial coverage here.) On Tuesday, we covered the company’s launch of what it calls the Startup Genome Compass, a benchmarking tool for startups that helps founders monitor their progress in different growth categories. Since then, more than 6,000 startups have signed up to use the Compass.
Along with the diagnostic tool, Blackbox also released a new research report on the major causes of startup failure, including perhaps more significantly, the primary cause of startups kicking the bucket: Premature scaling. While this was touched on in our prior coverage, we thought it might be worth elaborating on their findings, including presenting a nifty infographic created by the team over at Visual.ly.
To refresh, since February, Blackbox has collected a dataset from over 3,200 high-growth tech startups, with the results of their studies showing that premature scaling is the primary cause of startup failure, afflicting 70 percent of all the startups that went to meet their maker. And, a related point that’s worthy of note: Based on those 3,200 startups, the experience of entrepreneurs, gender, country origin, education and age had no influence on the predicted likelihood of failure.
But, as some astute readers in the comment section of our prior post pointed out, it certainly can seem dangerous to mine a large and diverse set of data created by startups (and in turn by actual — and equally diverse — human beings) to claim that the world has found one single, ultimate cause of failure that can be used as a prescription for startups of every stripe, across the board. While the Blackbox team may disagree slightly, the study is aimed at helping early-stage companies avoid the deadpool. Simple as that. The Startup Genome is an ongoing research project seemingly intended to illuminate, not force-feed prescriptions. It is scientific in its approach, some of its language may seem dry — and it may not work for everyone.
What’s more, “premature scaling” may seem an overly simplistic term, and it may be easy to misconstrue. The Blackbox team defined premature scaling in their research as a way of denoting the fact that a startup’s core dimensions (product, customer, team, finances and business model) are out of sync. That is to say: One (or more) are moving at different speeds of growth than others. As Blackbox Co-founder Bjoern Herrmann pointed out, “in some cases dysfunctional scaling may be a better description”.
With this description in mind, the research found some fairly striking differences between those startups that scaled prematurely (or dysfunctionally) as opposed to those who were more in sync. Most notably: Not a single startup that scaled prematurely passed the 100,000 user mark. Not only that, but 93 percent of those startups never crossed the $100K-a-year-in-revenue threshold. And, perhaps somewhat counterintuitively, startups that scale properly grow 20 times faster than startups that scaled prematurely.
Investor and serial entrepreneur Brad Feld weighed in on premature scaling to say, “Hiring any substantive number of sales or marketing people before there is customer adoption is premature scaling. All the early hires should be technical or product focused. At least one of the co-founders, though, should be obsessed with sales and marketing from the beginning. Adding one sales person after the product is in the market and one marketing person is fine, but these should be ‘doers’ not ‘VPs’”.
As a further means of elaborating on how the Blackbox team defined their research, Hermann said that they defined startups as “temporary organizations that are designed to evolve into large companies”. Once defined, the team then attempted to bring a scientific approach to understanding the lifecycle of those startups — almost like a behavioral psychologist — by defining six stages of development they evolve through: Discovery, Validation, Efficiency, Scale, Sustain, and Conservation.
Early stage startups are designed, Herrmann said, to search for product/market fit under conditions of extreme uncertainty, whereas late stage startups are designed to search for a repeatable and scalable business model and then scale into large companies designed to execute under conditions of much higher certainty. Sounds reasonable.
But they went further: Every startup, they determined, has an actual stage and a behavioral stage, in which the “Actual stage” is measured by customer response to the startup’s product, through looking at metrics like numbers of users, user growth, activation rate, retention rate, revenue, etc. The “behavioral stage”, then, is made up of five top level dimensions that the startup can control, like Customer, Product, Team, Financials and Business Model. Each dimension, both the Actual and Behavioral are always classified, Herrmann said, into one of the six developmental stages.
In terms of how this model relates to premature scaling, a startup received this label in the research when its behavioral stage became “larger” than its actual stage. An obvious example of premature scaling, the Blackbox Co-founder said, would be a startup that rapidly scales up its team to 30 to 40 people before it has any customers. In this example, the Actual stage of the startup would be in Validation but the Behavioral stage of the team would be in Scale.
On the other hand, Blackbox tends to define “dysfunctional scaling” as a case in which the Behavioral Stage is lower than the actual stage. Startup that provide examples of this, according to Blackbox, include: Tokbox, Friendster, Orkut, Wesabe, Digg, SixApart, Myspace, abd Chatroullete.
But rather than go into each of the individual stages, here’s an example from one: Specifically, that of the olde “customer acquisition” category. Blackbox labeled a startup as scaling prematurely in relation to its customer acquisition when it, for example, spent too much money on acquisition before truly refining its actual product or market fit — or, alternatively, overcompensating or missing product and market fit with too much of a focus on marketing and press spending. An illuminating stat in this case is that startups are 2.3 times more likely to spend more on customer acquisition before getting all other categories in sync. Blackbox cited Color, Webvan, and Pets.com as examples of startups that spent too much, too early on the customer acquisition dimension.
For now, we’ll leave it at that. But for those who are interested in more, check out Blackbox’s research on premature scaling here.
And without further ado, Visual.ly’s infographic on premature scaling is below: