Can you predict which startup companies will succeed? It’s a mystifying question for sure, but one that the Startup Genome Project is trying to answer. The project just released a 50-page analysis (based on surveys of more than 650 Internet startups) with the goal of “cracking the innovation code of Silicon Valley and spreading it to the rest of the world.”
Co-authored by faculty members from Stanford University and University of California-Berkeley, the report lays out 14 indicators of startup success. Is this the Internet startup equivalent of the Human Genome Project?
It may not be that groundbreaking, but the authors believe that if they can identify successful traits in startup companies — “turning entrepreneurship into a science” — then the pace of innovation could actually accelerate.
The authors write:
Once we started analysing the data it was staggering to see how clearly were able to find the patterns that described why Internet startups succeed and fail. We were able to break down the lifecycle of a startup into 6 discrete stages and identified 4 very different types of startups. Companies that didn’t move through the stages we defined were significantly less successful.
You can see those 14 indicators of success below, but the thing that really interested me was the four classifications of Internet startups.
Take a look at the categories below, along with the descriptions provided by The Startup Genome Project, and let me know where you fall?
–Type 1: The Automizer
Common characteristics: Self service customer acquisition, consumer focused, product centric, fast execution, often automize a manual process.
–Technology heavy founding teams perform better than other teams.
–Market size is 2x bigger for Type 1 (The Automizer) compared to Type 2 (The Integrator).
–More likely to tackle existing markets.
–Need the least capital of all types.
Examples: Google, Dropbox, Eventbrite, Slideshare, Mint, Pandora, Kickstarter, Hunch, Zynga, Playdom, Box.net, Basecamp, Hipmunk.
Type 2: The Social Transformer
Common characteristics: Self service customer acquisition, critical mass, runaway user growth, winner take all markets, complex ux, network effects, typically create new ways to interact.
–Need 50 percent longer than Type 1 (The Automizer) and Type 2 (The Integrator) to reach scale stage.
–Business heavy and balanced teams perform better than technology heavy teams.
–Market size is 2x bigger for Type 1N (The Social Transformer) compared to Type 2 (The Integrator).
–More likely to tackle new market.
–More likely to have large team growth at the scale stage.
–Need more capital than Type 1 and Type 2.
–More likely to have large user growth.
Examples: Ebay, OkCupid, Skype, Airbnb, Craigslist, Etsy, IMVA, Flickr, LinkedIn, Yelp, Facebook, Twitter, Foursquare, YouTube, Dailybooth, Mechanical Turk, MyYearbook, Propser, Paypal, Quora.
Type 2: The Integrator
Common characteristics: Lead generation with inside sales reps, high certainity, product centric, early monetization, SME focused, smaller markets, often take innovations from consumer Internet and rebuild it for smaller enterprises.
–Business heavy and balanced founding teams perform better than technology heavy teams.
–More likely to tackle existing markets with a product that is cheaper.
–More likely to maintain small teams even when they scale.
–Monetize a high percentage of their users.
Examples: PBWorks, Uservoice, Kissmetrics, Mixpanel, Dimdim, Hubspot, Marketo, Xignite, ZenDeesk, GetSatisfaction, Flowtown.
Type 3: The Challenger
Common characteristics: Enterprise sales, high customer dependency, complex and rigid markets, repeatable sales process.
–To reach scale stage they need about 2x more time compared to 1N and 3x more time compared to Type 1 (The Automizer) and Type 2 (The Integrator).
–Market size is 6-7 times bigger than all other types.
–More likely to either tackle existing markets with a better product or tackle a new market.
–Are more likely to either pivot a lot or not at all.
–Need significantly more capital than the other types.
–Monetize a high percentage of their users.
Examples: Oracle, Salesforce, MySQL, Redhat, Jive, Ariba, Rapleaf, Involver, BazzarVoice, Atlassian, BuddyMedia, Palantir, Netsuite, Passkey, WorkDay, Apptio, Zuora, Cloudera, Splunk, SuccessFactor, Yammer, Postini.
And here are the 14 indicators of successful startup companies:
1. Founders that learn are more successful: Startups that have helpful mentors, track metrics effectively, and learn from startup thought leaders raise 7x more money and have 3.5x better user growth.
2. Startups that pivot once or twice times raise 2.5x more money, have 3.6x better user growth, and are 52% less likely to scale prematurely than startups that pivot more than 2 times or not at all.
3. Many investors invest 2-3x more capital than necessary in startups that haven’t reached problem solution fit yet. They also over-invest in solo founders and founding teams without technical cofounders despite indicators that show that these teams have a much lower probability of success.
4. Investors who provide hands-on help have little or no effect on the company’s operational performance. But the right mentors significantly influence a companyâ€™s performance and ability to raise money. (However, this does not mean that investors donâ€™t have a significant effect on valuations and M&A)
5. Solo founders take 3.6x longer to reach scale stage compared to a founding team of 2 and they are 2.3x less likely to pivot.
6. Business-heavy founding teams are 6.2x more likely to successfully scale with sales driven startups than with product centric startups.
7. Technical-heavy founding teams are 3.3x more likely to successfully scale with product-centric startups with no network effects than with product-centric startups that have network effects.
8. Balanced teams with one technical founder and one business founder raise 30% more money, have 2.9x more user growth and are 19% less likely to scale prematurely than technical or business-heavy founding teams.
9. Most successful founders are driven by impact rather than experience or money.
10. Founders overestimate the value of IP before product market fit by 255%.
11. Startups need 2-3 times longer to validate their market than most founders expect. This underestimation creates the pressure to scale prematurely.
12. Startups that havenâ€™t raised money over-estimate their market size by 100x and often misinterpret their market as new.
13. Premature scaling is the most common reason for startups to perform worse. They tend to lose the battle early on by getting ahead of themselves.
14. B2C vs. B2B is not a meaningful segmentation of Internet startups anymore because the Internet has changed the rules of business. We found 4 different major groups of startups that all have very different behavior regarding customer acquisition, time, product, market and team.