Defining a Tech Scaleup

What is a tech scaleup? The answer to this question is very important for startups and investors, but especially so for governments and innovation agencies, because research has shown that the fastest growing businesses create 80% or more of the jobs and economic impact.

Why the Common Definition of a Scaleup Doesn’t Work

The current common definition of a scaleup, proposed by the OECD for traditional businesses, is “firms that achieve an average growth in revenue and/or employment of at least 20% per year over three consecutive years, and have at least 10 employees at the beginning of the period (at least 18 at the end).”

The definition of the Scale stage for tech startups, developed by Startup Genome through our seminal 2011 global and primary research (see Why Startups Succeed) follows Steve Blank’s startup definition: a temporary organization in search of a repeatable and scalable business model, its main challenge (see The Four Steps to Epiphany).

A startup is at the Scale stage, therefore, when it has defined and proved the scalability of its business model. Through the prior Efficiency Stage and to avoid scaling with a leaky bucket, it has the following fundamental characteristics:

• Achieved product-market fit
• Figured out its Unit Economics: CAC, LTV, Contribution Margin (CM) and Payback period (PP), and if scaled, its business model is sustainable
• Proved that it has a Repeatable and Scalable Sales Model

Startups that successfully reach the Scale stage typically grow at 50% to 100% and more per year. The most successful ones — the top 10% — grow a lot faster, at about or above 100% per year, and typically count 50 employees or more.

From these characteristics we can conclude that the current definition of scaleups, defined mainly for traditional businesses, does not work for tech scaleups. Its main problems are:

• The growth rate threshold is too low to identify only the fastest-growing tech startups  
• The minimum size is too low

The existing definition is also impractical. Outside of Europe and a few non-European countries, private business revenue and employment data is confidential, and in Europe it is available late (1+ years later than the year of activity), so the definition cannot be applied broadly to an ecosystem without the massive cost and effort of contacting startups to ask for this information, and most of them would likely decline to provide that confidential information anyway.

As such, we see a clear need for a new definition of a tech scaleup.

Objectives for the Definition of a Tech Scaleup

The definition must be objective and practical, with its key metrics allowing us to identify a great majority of tech businesses that are ready to scale among a large number of startups at low cost, rapidly, based on externally visible criteria, i.e. publicly or semi-publicly available data on individual tech businesses.

When looking at tech unicorns and large exits we are hard pressed to find several that were bootstrapped, so giving up on counting bootstrapped scaleups will not lead to bad estimates. Regardless, because ecosystems typically have no data on bootstrapped startups (except in Europe but with a long delay), any practical definition of tech scaleups will fail, in practice, to allow the identification of bootstrapped scaleups. Yet they are not specifically excluded from the definition we propose.

Going back to the fundamental and quantitative characteristics of a startup that has reached the Scale stage, tech scaleups have typically raised a Series B or a very large Series A round. VC firms all over the world apply similar criteria, following a global methodology that has spread from Silicon Valley. Typically, when a VC firm offers Series B investment, it is speaking with its money, judging that the startup is ready to scale. This is not quite the case for a typical Series A. However, when a VC firm offers a very large Series A investment, say more than $12 million, it says that the startup is ready or almost ready to scale because this size of investment is more than is needed to complete any remaining work on product-market fit, unit economics, and proving its repeatable sales model. At that Series A check size the average estimated valuation is $50 million (1:4 ratio from investment to post-money valuation) while for Series B the check size would be larger than $8 million (1:6 ratio to post-money valuation).

Redefining a Tech Scaleup

Therefore we propose this simple, practical and objective definition for tech scaleups: Startups that have a valuation of $50 million or more.

Using VC investment amounts offers the advantage of being readily available and widely reported on, in addition to being objective and third-party verified. And unfortunately, there are not many other choices of metric that achieve all of our objectives.

We understand that this valuation is harder to achieve in earlier-phase ecosystems (for instance Activation-phase ones) than in top ecosystems. However, it is also true that in Activation ecosystems, a much smaller proportion of startups reach the Scale stage, having truly defined and proved the scalability of their business models, and that they later grow to produce sizable economic impact. Therefore, valuations are a good estimate that is generally directionally correct and proportional in terms of identifying readiness to scale across ecosystems. Defining a different valuation threshold for each ecosystem phase and region can be done, but is difficult and less practical on a global basis.

This definition can be applied to bootstrapped startups by benchmarking their revenue and growth rate against that of VC-funded scaleups.