Earlier this year, the Startup Genome Projectlaunched a survey aiming to to investigate “the science of startups.” Much like the Human Genome Project’s attempts to crack the human genetic code, the Startup Genome Project hoped to be able to uncover the “innovation code” that drives the success of Silicon Valley technology startups.
“Despite the supreme economic importance of scalable startups, we still don’t understand the patterns of successful creation,” say the project organizers. “More than 90% of startups fail, due predominantly to self-destruction rather than competition. For the less than 10% of startups that do succeed, most encounter a handful of near death experiences along the way.”
Through the survey, the Startup Genome Project hoped to be able to have some “hard data” to uncover why some succeed and why some don’t. And now the results are in with a 67-page report based on the responses and profiles of over 650 startups. The report examines what’s worked – and what hasn’t worked – for these particular companies.
Lessons Learned from the Startup Genome Project
Some of the lessons gleaned from the survey seem quite obvious: Founders that have helpful mentors and learn from other startup thought leaders raise 7 times more money and have 3.5 times better user growth, for example.
And some of the findings confirm what are commonly held beliefs, such as the drawbacks of being a single person founder. Solo founders take 3.6 times as long to reach scale stage as do founding teams of 2 and they are 2.3 times less likely to pivot.
But many of the findings offer some pretty intriguing insights into other beliefs, such as the importance of being able to pivot. For example, startups that pivot once or twice raise 2.5 times more money and have 3.6 better growth than startups that pivot more than 2 times or that never pivot.
There are plenty of lessons to be learned with the data from the survey – even if it’s just nuggets like startups need 2-3 times longer to validate their market than most founders expect. There are important details here about why startups fail – not working at the project full-time, for example, not having a technical co-founder, or not having the right team composition for the type of startup.
Types of Startups
The report breaks down these startup types into the following:
The Automizer (Type 1)
Common characteristics: self-service customer acquisition, consumer focused, product centric, fast execution, often automize a manual process.
The Social Transformer (Type 1N)
Common characteristics: self service customer acquisition, critical mass, runaway user growth, winner take all markets, complex ux, network effects, typically create new ways for people to interact.
The Integrator (Type 2)
Common characteristics: lead generation with inside sales reps, high certainty, product centric, early monetization, SME focused, smaller markets, often take innovations from consumer Internet and rebuild it for smaller enterprises.
The Challenger (Type 3)
Common characteristics: enterprise sales, high customer dependency, complex & rigid markets, repeatable sales process.
Along with the release of the data from the Startup Genome Report, the authors are releasing a benchmark test that will help give entrepreneurs a sense of which personality type they fall into, and then based on the data from the Startup Genome Project, advice on what to focus on.