Startup Growth, Failure, and Financial Backing
The life of a startup is not always easy. Usually, the goal is to reach “unicorn” status, which simply means that a startup is at the billion-dollar level of earnings. However, before getting there, they must cross numerous financial hurdles. These hurdles often come with financial backing in the form of venture capital (VC) or other investments.
According to Embroker, almost 99% of US-based businesses are small, and an estimated 90% end up failing. 10% fail within the first year, and 1% are estimated to reach “unicorn” status. As more proposals reach venture capitalists (VCs) and angel investors, there is more uncertainty and less guarantee that a startup will be funded.
With exponential startup growth, and the slow-rising early survival rate increasing over time (75.3% in 2009 to 79.6% in 2019), startups might stand a better chance at reaching success. According to current research, startups often fail due to financial considerations, initial entry errors, experience, skills, or talent-based factors.
Some experts propose that success for startups can also be related to the type of business model used — namely, the employed business model. In a study by Haifa Haddad et al., considering different business models for a startup can improve performance and success rates.
Although there is still limited evidence for the correlation between business models and business performance, it is well worth considering. As startups grow, business models should pivot to accommodate further innovation.
According to Silicon Valley Bank’s 2019 US Startup Outlook, almost half of US startups indicate that their funds will come from VC. Recent statistics indicate that 52% of funds originate from VC. Other funding comes from angel, micro, and individual investors (17%), private equity (8%), corporate investors (7%), and organic growth (6%).
Artificial Intelligence and Startups
The US tech market is the largest in the world, generating $1.7 trillion in revenue for 2020. In addition, 20 tech companies start in the US per year, averaging $100 million in revenue.
Artificial intelligence (AI) has become one of the more prominent sectors in startups. According to the 2019 US Startup Outlook respondents, nearly 60% believe AI is one of the “most promising” sectors in innovation, with Big Data coming in second at just under 40%.
Recent studies suggest that AI startups are still in the early stages and have not yet been able to initiate proper patenting. However, the US, alongside China, ranks as the top country to lead AI in startups.
Startups and the Gig Economy
Beyond just technology and AI, startups can provide secondary sources of income with the freedom to choose beyond a 9 to 5 job. An increasing number of people are opting for freelance work, with various platforms popping up to accommodate the industry.
According to recent research, the gig economy model allows people to offer services through their online platforms, not as employees of the specific business, but as independent contractors. This model allows people the flexibility to start their own services, either full-time as a supplementary income.
Well-known companies like Uber and DoorDash utilize the gig economy model through ride-hailing, with Uber being the first ride-hailing platform to launch in the US. Similar platforms include Upwork and Fiverr, where independent contractors can create profiles for various freelance-like services.
“Gig” work is different from freelance work. As John Barrios et al. explains, “gig workers do not need to invest in establishing a company and marketing to a consumer base, operating costs may be lower, and as a result, participation in the gig market is often more transitory than the traditional freelancing market of old.”
Though most US startups tend to fail, new trends may help more companies stay in business longer. By updating their business models, integrating new technology, and opening their workforce to contractors, startups may find themselves set for success.
About the Author
Mariliana has an MSC in consumer analytics and business strategy. She has a special interest in fast-moving industries and big data.