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http://techcrunch.com/2011/09/01/a-deeper-look-at-blackboxs-data-on-startup-failure-and-its-top-cause-premature-scaling-

infographic/

By Rip Empson

A Deeper Look At Blackboxs Data On Startup Failure And Its

Top Cause: Premature Scaling [Infographic]

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

companys launchof 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: Prematurescaling. 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 thats 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 seemdangerous 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.

Whats more, premature scaling may seem an overly simplistic term, and it may be easyto misconstrue.

The Blackbox team defined premature scaling in their research as a way of denoting the fact that a startups

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.

Withthis 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.

https://www.startupcompass.co/http://blog.startupcompass.co/http://techcrunch.com/2011/09/01/a-deeper-look-at-blackboxs-data-on-startup-failure-and-its-top-cause-premature-scaling-infographic/http://techcrunch.com/2011/09/01/a-deeper-look-at-blackboxs-data-on-startup-failure-and-its-top-cause-premature-scaling-infographic/https://www.startupcompass.co/http://blog.startupcompass.co/http://techcrunch.com/2011/09/01/a-deeper-look-at-blackboxs-data-on-startup-failure-and-its-top-cause-premature-scaling-infographic/http://techcrunch.com/

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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 startups 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 itsbehavioral 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, heres an example from one: Specifically, that of theolde 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, well leave it at that. But for those who are interested in more, check out Blackboxs research on

premature scaling here.

And without further ado, Visual.lys infographic on premature scaling is below:

by StartupGenomevia

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