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As you were reading the title and summary of this article, 40 new startups were created (and probably even more).
137,000 businesses give birth every day or 5 million per year, as established in this research.
But 90% of them fail... If we do maths, 123,300 fail every day and, in the time you were reading up to here, 85 businesses were probably shut down.
Okay, I know this is quite depressing and doesn't inspire you to start a business yourself. But by reading this article and learning why startups fail, you will be one step ahead than the rest of entrepreneurs.
Failory started as a website where we interviewed failed startup founders (and we still do it every week!). We have been doing this for 2 years now, so we feel confident to claim we are the experts of failure.
You might be thinking this isn't a title we should be proud of, but you need to understand that success is not achieved without stumbling with a few rocks on the road.
You need to get that title too... and this is a great place to start.
This article is divided into two parts:
Let’s dive right in.
TL;DR of the following infographic:
As shown in the infographic, multiple sources have analyzed startup failure data, and found failure rates as high as 90%.
Now, why do so many startups fail?
And more importantly, how can you overcome failure as a startup founder?
In this section, we’ll equip you with an action plan to avoid failure. We’ll show you how you can efficiently analyze startup failure data, and grow your business thanks to these new data insights.
If you want to learn from previous failures, the first thing you need is accurate data on the subject. In this article, we’ll rely on the Startup Cemetery, which assembles startup failure data from more than 100 startups, containing:
In order to properly analyze all that startup failure data, we loaded it into a visual Cumul.io dashboard. In that way, you can explore the data interactively & spot correlations or trends in the blink of an eye.
Below, we’ll further deep-dive into the dashboard and how to use it. However, if you’re eager to start exploring yourself: play around with the embedded dashboard below!
As a founder, how can you derive useful insights from all that startup failure data? A global view on why startups fail is interesting, but how do you take action on such a dashboard?
This startup failure data becomes truly valuable if you start filtering them on characteristics that apply to your own business.
Here are 3 simple steps to help you use the dashboard above efficiently:
1) Assess the status quo of your business
2) Filter the dashboard on this key information about your business
e.g. Look at the most common reasons of failure for an e-commerce business, in which stage they typically fail,... Or act the other way around: filter on your struggles as a reason for failure and get insights into the businesses that failed before due to that same reason
3) Identify threats and possible actions to take
e.g. Let’s say your key challenge is cash flow, and you see that many similar businesses failing because of a bad business model. Together, this might indicate that your business model needs improvement, which could help improve the cash flow and avoid business failure.
Following this path, you can learn from your predecessors and avoid the same mistakes they made. Practice what you preach? Let’s put these 3 steps into practice!
Let’s illustrate this with a fictional example. Imagine you are running JustDress.com, an e-commerce business that’s selling their own brand of women's clothing online. How can JustDress.com make use of our startup failure dashboard?
First, you’ll want to get a solid understanding of this e-commerce business. Let’s have a look first at what we know about the company:
Looking back over the past 3 years of activity, their biggest challenge has been the highly competitive landscape. They lose a lot of customers to competing clothing retailers, both online & offline.
Now, to find useful insights for JustDress.com in the dashboard, you’ll want to filter on characteristics they have in common with previously failed businesses.
Let’s start with JustDress.com’s main challenge: competition. What do they have in common with businesses that failed due to high competition? If you filter the dashboard on competition as the reason for failure, you’ll spot the following insights:
Next, you can filter the dashboard specifically on e-commerce startups, as they ran their business in the same category as JustDress.com. What can we learn from other startups that failed in e-commerce?
You can do the same for other parameters. For example, you can filter the dashboard on startups with 11-50 employees or on startups that were active for 3 years when failed.
So, we found some interesting insights when looking at failed companies that have similar business characteristics as JustDress.com. The next step is: how can they take meaningful actions on these insights? We listed 4 insights, so here are 4 possible actions for JustDress.com.
Now that we’ve looked at a specific use case, it’s also interesting to look at wider trends that might be insightful across all sectors and sizes. We identified a number of general trends in this set of startup failure data.
The chart below looks complex at first sight, so let’s illustrate how to read it properly. By hovering over a specific value, you can see how the data flows for that specific category.
For example, hover over productivity. You’ll see that failed productivity startups had a different number of founders, but no clear pattern. However, after that, you can see the data converging again: most of the failed productivity tools were companies of 11-50 employees.
Now, if you just look at the distribution of the right column, you can already see a trend emerging. The largest chunk is that of 11-50 employees, followed by 1-10 employees. This indicates that failure was more common among smaller companies below 50 employees.
When a startup fails, there can be multiple outcomes: it can be acquired by another firm, the founders can decide to shut it down, or the company can go bankrupt.
Looking at the funnel chart below, you can see that a shut-down is the most common option. Acquisition is second in line, so let’s filter on acquisition to see if there are any similarities among acquired companies.
Looking at the alluvial diagram, you can see that there are quite some acquired startups that had a high number of investors. Let’s dive into that a little deeper: what if we filter outcome only on startups that had more than 20 investors?
The chart above shows that, in case of a very high number of investors, the company is more likely to be acquired than to shut down.
Are there certain patterns to the timing when companies fail? Are failures more common in the beginning phase of a startup, or does it also happen to more mature companies?
The chart above shows that only 10% of startups in this dataset have failed during their first year. Failure is most common for companies that have been in business between 2 and 5 years: a striking 70% of the total.
There’s a whole array of reasons why businesses fail. But can we see patterns among the companies that failed for the same reason?
The chart above shows you the main causes for startup failure. In the full dashboard, you can click on a specific cause to filter only on startups that failed due to this specific cause. A few learnings we spotted while filtering:
Similar to the examples above, you can filter the startup failure data in the dashboard on your current company size, funding situation and sector. In that way, you can see what the most common reasons were for failure of companies similar to yours.
Each company fails for its own reasons. The startup failure dashboard shows that there are a lot of circumstances and variables that can have an impact on why a business fails. But as shown above, there are always larger trends that possibly coincide for multiple businesses.
As a founder, there’s nothing more valuable than knowing the common pitfalls beforehand. You can learn from other businesses’ failures and apply these learnings to your own startup. There will always be challenges to overcome, but being ahead of the game will make your chances of survival much more likely!
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