Why did 150 startups fail? Here are the top 30 reasons for failure

Our real world problem

Over 85% of companies won't survive up to 10 years.

Just in the USA alone, more than 360,000 companies shut down every year (U.S. Small Business Administration, 2018).

Without a doubt, the rate of companies shutting down every year is staggeringly high.

Abstract (TL;DR)

Objectives: Every year around the world, billions of dollars were lost due to business failures. Therefore, the purpose of this analysis was to gain a better understanding on why companies from around the world shut down.

Methods: This was a descriptive analysis conducted with secondary data gathered from the Internet. Due to the current small amount of verified and cleaned data, only 150 inactive companies were observed for this analysis.

Findings: Out of 150 companies, 111 (74%) companies shut down because of poor business model (23%) design, no product-market fit (17%) advantage, strong competition (19%), and lack of funds (15%). Most of the 125 (83.4%) companies were from United States, India, and United Kingdom. Overall, a substantial amount of the 81 (54%) companies operated in the internet, and information technology & services industries.

Conclusions: Many new and small companies fail to design a good business model with competitive advantage. This is because most companies offer products that fail to solve an important problem, without custom work or better than existing solutions, for a significant number of independent customers in a large market (Janz, 2017).

Introduction

Between 2015 and 2017, our global startup economy created USD $2.3 trillion in value, a 25.6% increase from 2014 to 2016, and it continues to grow every year (Startup Genome, 2018).

Evidently, many research and data have shown that entrepreneurship have positive effects on employment creation, innovation, and economic growth in our global economy (Audretsch & Fritsch, 2002; Baptista, Escária, & Madruga, 2008; Carree & Thurik, 2010).

In achieving the Fourth Industrial Revolution, entrepreneurs are leading the way as agents of change, bringing new ideas to the markets, and driving growth through their competitive advantage (Wong, Ho, & Autio, 2005).

However, most business studies and entrepreneurial research seem to have a natural tendency to focus on success stories (Madsen & Desai, 2010), and less on failure stories which may result in a survivorship bias that can lead to over- or understating the predictability of events (Brown et. al., 1992). Consequently, with the lack of in-depth research on business failures, how can we effectively learn from failures to reduce costly mistakes and avoid poor strategies?

Since our inception in September 2017, Flipidea (pronounced as 'flee-pee-dia') is a Data-as-a-Service (DaaS) platform offering analytical information about business failures. In other words, we analyse business failures.

For this reason, we continuously gather information on companies that ceased operations to examine why they shut down, and draw meaningful insights from our regular analysis. Our ultimate mission is to help our audience build successful businesses by making data-informed decisions.

This report is divided into the following sections:

• purpose for this descriptive analysis
• data collection process and its quality
• research questions and methodology
• findings based on the descriptive analysis of 500 inactive companies
• results and discussion
• limitations and future research

Our Purpose

As our data grows, the overarching purpose of our descriptive analysis is to scientifically examine the datasets and gain a better understanding on why companies from around the world shut down. The objectives are:

• to study the reasons for shutdown
• to discover meaningful patterns in the data
• to contribute our findings to existing knowledge and research literature on business failure, entrepreneurship, venture capital and private equity investment, management, and innovation

Our Data

The report is based on secondary data we had gathered from the Internet.

The data are combined datasets which are publicly available on the Internet. Our data retrieval systems identified the failed companies and gathered the data from the companies' websites, blogs, social media, news articles, media interviews, research papers, analytical reports, and so on.

Overall, the datasets comprised of company information, post-mortem reports (an analysis of an event after it is over), investment data, business performance data, founders' profiles, social media data, and so on. However, there are substantial amount of missing data in our data gathering because many of the companies did not publish the information.

Although we take reasonable measures to ensure that our gathered data is accurately reflected in this report, we do not warrant the completeness or accuracy of data provided because our data retrieval systems scan the Internet to identify, monitor, and gather relevant, aggregated, and public information which may be incomplete or inaccurate or not available. Hence, we encourage you to independently verify the accuracy of the information.

In addressing this issue, we leverage on our human-in-the-loop process to verify and manage the integrity of our data while we improve our data retrieval systems, and gradually publish the verified and cleaned data unto our platform. Thus, data in this report is subject to change without notice.

Finally, when interpreting the data, it is important to keep in mind that our datasets include information of companies from around the world, so the data should be interpreted in such context.

Our Methodology

Our descriptive analysis seeks to obtain insights from the 150 inactive companies in our live database, which are also published on www.flipidea.co. Therefore, we only observed these 150 inactive companies for this report.

Firstly, what is descriptive analysis? Descriptive analysis provides information on the basic qualities of data and includes descriptive statistics, such as range, minimum, maximum, and frequency. It also includes measures of central tendency, such as mean, median, mode, and standard deviation. Therefore, it is important to note that descriptive statistics merely describe the observed data.

Since our data are classified (systematic arrangement in groups) and stored in numerous collections, we wrote scripts (aka scripting language, which is a programming language to automate the execution of tasks) to calculate our descriptive statistics.

Secondly, due to our current small amount of verified and cleaned data, we merely conducted descriptive analysis to understand the following:

• what are the top reasons for shutdown?
• where did the companies base at?
• which industries did the companies operate in?

Thirdly, for all of the descriptive statistics, the percentages were calculated based on 150 inactive companies and the frequency (n) of each data features or variables.

percentage % = (n/150)*100

In the event you wish to calculate the frequency (n) of each data features or variables, based on the calculated percentages:

n = (calculated percentage%/100)*150

Lastly, in order to help our audience understand the terminologies and definitions commonly used by entrepreneurs and investors in the tech startup and business scenes, we put together a glossary of business failures.

The glossary is a compilation of terminologies used in business, finance and tech industries that were defined by experts found on the Internet.

Our Findings

We present the results of our descriptive analysis in this section.

What are the top reasons for shutdown?

From our classification of reasons for shutdown, the post-mortem data revealed that a company may shut down due to multiple reasons with the top 3 common reasons listed below:

1. poor business model experienced by 34 companies at 23%
2. strong competition experienced by 29 companies at 19%
3. no product-market fit experienced by 25 companies 17%

From the observation of the data, a substantial amount of 111 (74%) companies suffered from poor business model design, no product-market fit advantage, strong competition, and lack of funds.

Where did the companies base at?

From our classification of data, the top 3 locations are:

1. 70 companies at 46.7% were based in United States
2. 43 companies at 28.7% were based in India
3. 12 companies at 8% were based in United Kingdom

The data showed that the 125 (83.4%) companies were mostly from United States, India, and United Kingdom. There are two main reasons:

1. the Total early-stage Entrepreneurial Activity (TEA) rates in United States (15.6%), United Kingdom (8.2%), and India (11.4%) are substantially high (Global Entrepreneurship Monitor, 2019)
2. currently, our data retrieval systems only monitor companies from all English-speaking countries

Which industries did the companies operate in?

From our classification of data, the top 3 industries are:

1. 73 companies at 48.7% were classified as internet industry
2. 8 companies at 5.3% were classified as information technology & services industry_
3. 7 companies at 4.7% were classified as retail industry

The data showed that 88 (58.7%) companies operated in the internet, information technology & services, and retail industries.

Discussion

The findings of this descriptive analysis show that:

• 111 (74%) companies suffered from poor business model design, no product-market fit advantage, strong competition, and lack of funds
• 125 (83.4%) companies were mostly from United States, India, and United Kingdom
• a substantial amount of the 81 (54%) companies operated in the internet, and information technology & services industries

Statistics shows that many new and small companies failed to design a good business model with competitive advantage. As a result, they failed to achieve the elusive product-market fit, even though the market need was there and the product was compelling, but they just could not reach their customers (Feinleib, 2012).

Most companies also seem to offer products that fail to solve an important problem, without custom work or better than existing solutions, for a significant number of independent customers in a large market (Janz, 2017). Thus, resulting in a poor product-market fit disadvantage.

It is important to note that the interpretation of the reasons for shutdown is not straightforward because we merely observed 150 inactive companies. Therefore in our future analysis, we aim to draw better insights by observing more companies.

Our conclusion

A descriptive analysis of 150 inactive companies have been conducted.

This analysis was limited by our small and incomplete datasets. With bigger and more complete datasets, we will be able to discover more meaningful insights.

Therefore, this report is presented as a work in progress, and our findings presented will be thoroughly expanded as part of our ongoing in-depth research.

As our data grows, we will continue to observe and examine the datasets to gain a better understanding on why companies around the world shut down.

Authors

1. Paul Lee, Co-founder & CEO, Flipidea
2. D. T. C. Lai, Chief Researcher, Flipidea

Citation and republishing

Flipidea's articles and reports may be republished in accordance with our Citation Policy and Terms of Service.

References

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Our proprietary data retrieval systems scan the Internet to automatically aggregate public information from news articles, websites, press releases, regulatory filings, and so on. Roiquant's intelligent decision support system, multimodality systems, database systems, data retrieval systems, algorithmic systems, data processing engines, and many other technologies are powered by artificial intelligence and advanced analytics using our own proprietary data and synergized public data. Roiquant does not warrant the completeness or accuracy of data provided, so you would need to independently verify the information. Nevertheless, we encourage you to check the accuracy of our information before its use. We also gently advise that you obtain sufficient knowledge, market understanding, professional advice and experience to make your own evaluation of the merits and risks of any actions with the information.

Last updated on 21 October 2022.