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90% of startups fail. What are the causes? What does this mean for entrepreneurs? What happens with investors' money? Go on reading to learn more.
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These are some of the most common statements on the topic of startup failure. While those stats could certainly be helpful, if you put them in the wrong context, they could also be misleading.
In this article, we’ll try to go to the source of the data as well as Failory’s unique experience of talking directly to hundreds of successful and failed startup founders to shed light on the question of startup failure.
In its broadest sense, it is a new business in its earliest stages of development.
This definition is too general, however, and as a result - misleading. A new hairdresser salon is also a new business in its early developmental stages, but most people in the startup community would tell you a hairdresser salon isn’t a startup.
A startup usually has two important characteristics:
So, a startup is in essence a business experiment with potential. This means that real startups are prone to failure by definition. They are testing assumptions, and it’s very likely these assumptions are wrong. The more innovative the startup, the riskier the assumptions it’s testing, the more likely it is to fail.
When you put this new kind of risk on top of the traditional risks of starting a business (finance/cash flow risks, operational risks, team risks, marketing risks, etc.), it’s no surprise most startups fail.
Example: New Startup vs Non-startup Projects
Imagine you have a new IT consultancy that builds software for your clients. Even though you are a new business and you work with technology, you are not a startup because:
One day, you notice that all your clients have a similar problem, so you decide to invest some time in developing your own software product aimed at solving that problem.
This is a startup project, because:
The likelihood of your consultancy business failing is lower than the likelihood of your new software product failing because the software project is still trying to find product-market fit. Once validated, however, the software project could have bigger returns because of its potential for exponential growth through leveraging technology instead of human capital.
So, when you talk about startup failure rates, it’s important to understand one thing:
Statistical sources coming from government institutions are largely concerned with the failure rate of new businesses as a whole. This is useful if your project is closer to a traditional business. In this case, your baseline failure rate would be lower than 90%. One of the most quoted statistics, in this case, is the Business Employment Dynamics report coming from the Bureau of Labor:
Most new registered businesses aren’t true startups, so you shouldn’t assume your likelihood to fail in the 1st year is only 20% if you’re trying to do something innovative.
N.B. Some articles out there are quoting those statistics in the context of startups, which is misleading, so be careful!
Statistics coming from Venture Capital funds are mostly concerned with real, innovative, scalable startups. However, venture funds invest mostly in growth-stage startups, AKA scale-ups. They are true startups, but most of them have gotten past one of the biggest risks for startups: the search for product-market fit. They have tangible proof that people want what they are offering (this proof is how they attract venture capital).
This means that their failure rates would be lower than the failure rate of early-stage startups. Harvard Business School lecturer Shikhar Ghosh says in a WSJ article that 75% of venture-backed companies never return cash to investors and in 30-40% of the cases investors lose their whole initial investment (he works with a dataset of 2000 venture-backed startups).
That said, only 0.05% of startups get VC funding (Source: Fundable), so this statistic is not applicable for the vast majority of new businesses, especially if they are in the early idea stage.
Early-stage (idea stage) startups, of course, bear the highest risk and have the highest failure rates. It’s hard to claim accuracy about failure rate statistics for those kinds of projects because a large chunk fly below the radar. They don’t raise capital from funds or other entities who maintain a dataset - most early-stage businesses are funded from the founders, their family, and friends. A large chunk of early-stage startup projects don’t even register a legal entity – you don’t need one to test an assumption. You need one once you start making money.
The regularly quoted number is that 9 out of 10 startups fail, and it seems to originate from the Startup Genome project (in some of their more recent reports, however, they even say only 1 in 12 entrepreneurs succeed).
The exact accuracy of the statistic is beside the point for most people. The fact remains that startups are extremely risky, as can clearly be seen by our growing collection of interviews with failed startups founders as well as our Startup Cemetery, but equally rewarding, as can be seen in our startup success story interviews.
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So why can investing in startups be profitable even with the abysmal failure rate?
It’s because the successful startups make up for the unsuccessful ones.
If a startup fund has a portfolio of 100 companies, most of its returns would come from the 1 biggest success (ideally, a unicorn), followed by the 9 successful-but-not-huge companies. The 10 successful startups more than compensate for the 90 failures.
The implication here is that startup investors are searching for the home-run, and are willing to lose money on most of their investments to find that company. This means that as a founder, you’re unlikely to get funding from startup angels and VCs if you don’t show a lot of ambition and scalability.
This doesn’t necessarily mean that your idea isn’t worth pursuing if it doesn’t fit the investment criteria of VCs. Being a successful founder of a lifestyle business is way better than being an unsuccessful founder of a traditional go huge or go home startup.
If you’re doing anything remotely innovative, you need to accept the fact that you are very likely to be wrong. The world is very complex, most ideas (and the assumptions they carry) turn out to be bad. A great example of this is when Twitter acquired Vine with the aim of disrupting the video-sharing and social network ecosystem and ended up shutting the app down only a few years later (here's why did Vine shut down, btw).
That said, simply accepting that you have a 90% chance to fail doesn’t seem like a healthy mentality. There are plenty of ways you can maximize your chances of success. The fact that the average is 90% doesn’t mean you can’t nudge this number in your favor.
Some of the concepts that would help you the most:
You are searching for a product-market fit. The principles of the Lean Startup are extremely important at this stage. The goal is to validate your assumptions as quickly and cheaply as possible and to give yourself time to pivot if necessary. Get a good grasp of the meaning of MVP, validation experiments, validated learning. Get used to the agile project management principles when you are in the process of building. Learn to prioritize and change your priorities based on customer feedback.
Here are some findings from the Startup Genome Project:
One of the biggest traps is premature scaling. It means over-investment of resources (in the broadest sense) too early in the startup journey. The Startup Genome Project breaks the startup stages in four: Discovery, Validation, Efficiency, Scale. It calls startups that scale prematurely inconsistent. Here are some examples of their findings:
In the in-depth study of our interviews with the founders of 80+ failed startup projects that you can read in full in our Startup Mistakes article, we found that the most common reasons for failure are the following:
Marketing Problems (56%): marketing mistakes were the biggest killers, and the biggest problem by far is the lack of product-market fit. Don’t invest a lot of time and resources before you are certain people want what you are offering. Validate your assumptions quickly and cheaply, and if needed - pivot.
Team Problems (18%): problems like lack of domain knowledge, lack of marketing knowledge (and plan), lack of technical knowledge, and finally – lack of business knowledge, are the biggest killers. Friction within the team, lack of motivation, and lack of availability are also common, but less deadly.
Finance Problems (16%): more than 50% of the interviewed founders didn’t have a budget for their project, and 75% were self-funded, yet only 16% point at financial problems as the reason for failure. That’s because you don’t really need a lot of money to test and validate concepts (you need effort). You need money to grow an already validated concept, so financial problems plague mostly exclusively later-stage startups.
Tech Problems (6%): rarely a big killer even though the vast majority of the interviewed startups have some kind of technology in their core. The biggest mistake is over-investment in expensive technology (developer time) before the marketing assumptions have been validated.
Operations Problems (2%): for software startups like most of our interviewees, operational problems are understandably rare. For startups that work with physical products, this might not be the case.
Legal Problems (2%): largely overestimated, and very rarely the reason for failure. That said, heavily-regulated industries like food and finance still present legal obstacles.
Disclaimer: most of the projects we interview are true startups (rather than new traditional businesses) and have some form of technology (usually software) in their core. This means our conclusions might not be that useful for new projects closer to traditional brick-and-mortar businesses. Moreover, we gather the data by interpreting qualitative interviews (rather than surveys), so allow for some error.
When talking about traditional businesses, statistics from the Office of Advocacy show that new business failure rates are very similar across industries (source).
The Statistic Brain Research institute has other data tracking how many new businesses are dead after 4 years of operation in different industries:
The highest failure rate is in the Information industry, which might be surprising at first glance. The information industry, however, has a relatively low barrier to entry and includes a large portion of the true high-risk startups, which might be bumping the average failure rates up.
The statistics above should be useful if your idea or business is closer to a traditional business. For true innovative tech startups, there aren’t good sources of failure rates divided by industry. Nonetheless, this graphic coming from the Startup Genome 2019 report might prove very valuable. It divides startups into sub-sectors, and measures if the sectors are growing, mature, or declining based on the early-stage funding they tend to receive and the 5-year exits:
Agtech & New Food
Example failed project: The Poultry Exchange
A big challenge Agtech startups are facing is introducing new technologies (especially digital) to a mature, traditional industry that might be short on early adopters.
Example failed project: 300Cubits
Blockchain has obvious potential. Yet, the reality of the overly-volatile and speculative coin market as well as the unfamiliarity of potential stakeholders with the technology makes it hard to put theoretically sound ideas into practice.
AI, Big Data, & Analytics
Example failed project: Roadstar.ai
One of the industry giants in trouble: MapR
Even though the long-run potential of AI is unquestionable, the technology is in its infancy, and finding economically viable applications for it fast enough has proven to be a hard nut to crack. A lot of the most famous AI startups (e.g. OpenAI) resemble a fundamental science research team more so than a business team. A lot of the investors in the field are playing the long game.
Advanced Manufacturing & Robotics
Not a formal statistic, but industry experts believe the robotics startup failure rate is 99% (!).
There are many reasons why, but it boils down to “robotics startups are tackling an extremely hard technical problem”.
So, are these sub-sectors the best choice for would-be startup founders?
The startup sub-sectors from above have one thing in common: they might be some of the best to find funding to get a project going (if you have an impressive team), but they are also some of the hardest to create a self-sustaining business in.
The hot subsectors reveal the philosophy of the startup industry as a whole. They represent the toughest technological challenges, the biggest upside potential, but also the biggest chance for failure.
In other words, becoming a unicorn in Digital Media or Edtech is less likely, and finding sufficient funding could be more difficult. Yet, creating a successful, self-sustaining business in those fields might actually be more realistic.
All of that said, if you are an entrepreneur, choosing your sector should be dictated by your area of expertise rather than industry trends.
There isn’t a good source for global failure rate stats for true startups, but the Global Entrepreneurship Monitor report provides very useful information for the overall entrepreneurship environment all over the world tracking statistics as availability of finance, government policies, education, R&D, internet market dynamics and burdens, infrastructure, etc. Needless to say, new business failure rates are likely lower in countries with a favorable entrepreneurial environment.
Here are a few examples:
Entrepreneurial environment USA:
The USA is above the global average, especially in terms of culture and finance. This is not surprising - the American dream is strongly connected to entrepreneurship, and the actual concept of a tech startup comes from the US (Silicon Valley). The surprising fact is that the US is not that far ahead of the curve according to the report, especially in the categories where government is involved.
Entrepreneurial environment India:
India is one of the biggest emerging markets in the world, which makes it full of untapped entrepreneurial potential. It seems that the country as a whole is doing a great job of fostering a healthy entrepreneurial environment. While culturally (and financially) it’s behind the USA, it’s doing better than the US in a lot of criteria, especially where government support for entrepreneurship is concerned.
Entrepreneurial environment Russia:
Russia is another big emerging market, and yet it seems to be the polar opposite of India as far as the 2019/2020 GEM report is concerned. The only category in which Russia is ahead of the curve is Internal Market Dynamics.
The statistics from above are once again more relevant for traditional new businesses rather than true startups.
If you are starting an innovative tech-based startup, your best bet is to operate in one of the leading startup ecosystems in the world. There you will find the greatest density of invaluable resources like know-how (mentors, IT talent, marketing talent) and startup finance (angel investors, VCs).
Here is the 2019 Global Startup Ecosystem Ranking, as given by the Startup Genome project:
Silicon Valley leads the way worldwide and in the USA. London is the strongest European startup cluster, while Beijing is the most prominent startup hub in Asia. That said, startup culture is spreading all over the world, and while once the only viable place to found a true startup was the Valley, nowadays viable alternatives are much closer to home for most entrepreneurs.
We hope that we succeeded in clearing up some of the confusion about startup and new business failure rates!
Startups are without a doubt very risky, but with great risk comes great potential. Potential not only for financial returns, but for progress and innovation that could improve the quality of life of people all around the world. So, don’t let the risk of failure discourage you! Be audacious!
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Author: Kyril Kotashev
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