Our real world problem
Every year, more than 75% of new and small companies from around the world shut down.
Just in the USA alone, more than 360,000 companies shut down every year (U.S. Small Business Administration, 2018).
"If failure is referred as failing to see the projected return on investment, then the failure rate is 70% to 80%. However, if failure is defined as declaring a projection and then falling short of meeting it, then the failure rate is a whopping 90% to 95%." (Professor Shikhar Ghosh, Management Practice, Harvard Business School)
Without a doubt, the rate of companies shutting down every year is staggeringly high.
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).
"Neither technology nor the disruption that comes with it is an exogenous force over which humans have no control. All of us are responsible for guiding its evolution, in the decisions we make on a daily basis as citizens, consumers, and investors. We should thus grasp the opportunity and power we have to share the Fourth Industrial Revolution and direct it toward a future that reflects our common objectives and values". (Professor Klaus Schwab, Founder & Executive Chairman, World Economic Forum)
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
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
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 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.
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:
- poor business model experienced by 34 companies at 23%
- strong competition experienced by 29 companies at 19%
- no product-market fit experienced by 25 companies 17%
Where did the companies base at?
From our classification of data, the top 3 locations are:
- 70 companies at 46.7% were based in United States
- 43 companies at 28.7% were based in India
- 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:
- 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)
- 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:
- 73 companies at 48.7% were classified as internet industry
- 8 companies at 5.3% were classified as information technology & services industry
- 7 companies at 4.7% were classified as retail industry
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.
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.
Citation and republishing
Audretsch, D. B., & Fritsch, M. (2002). Growth Regimes over Time and Space. Regional Studies, 36(2), 113–124. Retrieved 26 June 2017, from https://doi.org/10.1080/00343400220121909
Baptista, R., Escária, V., & Madruga, P. (2008). Entrepreneurship, Regional Development and Job Creation: The Case of Portugal. Small Business Economics, 30(1), 49–58. Retrieved 26 June 2017, from https://doi.org/10.1007/s11187-007-9055-0
Bloomenthal, A. (2019). World's Top 10 Internet Companies. Retrieved 17 June 2020, from https://www.investopedia.com/articles/personal-finance/030415/worlds-top-10-internet-companies.asp
Brown, S. J., Goetzmann, W. N., Ibbotson, R. G., & Ross, S. A. (1992). Survivorship Performance Bias in Studies. Review of Financial Studies, 5(4), 553–580. Retrieved 13 June 2020, from https://www.researchgate.net/publication/31126889_Survivorship_bias_in_performance_studies
Carree, M. A., & Thurik, A. R. (2010). The Impact of Entrepreneurship on Economic Growth. In Handbook of Entrepreneurship Research, 557–594. Retrieved 26 June 2017, from https://doi.org/10.1007/978-1-4419-1191-9_20
Davis, N. (2016). What is the Fourth Industrial Revolution? Retrieved 13 June 2020, from https://www.weforum.org/agenda/2016/01/the-fourth-industrial-revolution-what-it-means-and-how-to-respond/
De la Banda, M. G., & Dwyer, T. (2019). Reasons Why We Need a Human-in-the-Loop in AI. Retrieved 13 June 2020, from https://www.monash.edu/it/futurist/explore/top-things-to-know/articles/reasons-why-we-need-a-human-in-the-loop-in-AI
Farnam Street (2019). Survivorship Bias: The Tale of Forgotten Failures. Retrieved 13 June 2020, from https://fs.blog/2019/12/survivorship-bias/
Feinleib, D. (2012). Why Startups Fail: And How Yours Can Succeed. Retrieved 29 May 2019, from https://link.springer.com/chapter/10.1007/978-1-4302-4141-6_1
Flipidea Viewpoints. (2019). Glossary of Business Failures. Retrieved 29 May 2019, from https://viewpoints.flipidea.co/glossary-of-business-failures/
Flipidea Viewpoints. (2019). Lack of Funds. In Viewpoints.Flipidea.co glossary of business failures. Retrieved 29 May 2019, from https://viewpoints.flipidea.co/lack-of-funds/
Flipidea Viewpoints. (2019). No Product-Market Fit. In Viewpoints.Flipidea.co glossary of business failures. Retrieved 29 May 2019, from https://viewpoints.flipidea.co/no-product-market-fit/
Flipidea Viewpoints. (2019). Poor Business Model. In Viewpoints.Flipidea.co glossary of business failures. Retrieved 29 May 2019, from https://viewpoints.flipidea.co/poor-business-model/
Flipidea Viewpoints. (2019). Poor Product-Market Fit. In Viewpoints.Flipidea.co glossary of business failures. Retrieved 29 May 2019, from https://viewpoints.flipidea.co/poor-product-market-fit/
Flipidea Viewpoints. (2019). Strong Competition. In Viewpoints.Flipidea.co glossary of business failures. Retrieved 29 May 2019, from https://viewpoints.flipidea.co/strong-competition/
Global Entrepreneurship Monitor. (2019). Entrepreneurial Activity: Percentage of Population aged 18-64. In Global Entrepreneurship Monitor 2018/2019 Global Report, 20, 117. Retrieved 6 June 2019, from https://www.gemconsortium.org/report
Harvard Business Review. (2018). Why the Lean Start-Up Changes Everything. Retrieved 16 December 2018, from https://hbr.org/video/5712986167001/why-the-lean-startup-changes-everything
Janz, C. (2017). WTF is PMF (part 2 of 2). Retrieved 29 May 2019, from http://christophjanz.blogspot.com/2017/07/wtf-is-pmf-part-2-of-2.html
Madsen, P. M., & Desai, V. (2010). Failing to Learn? The Effects of Failure and Success on Organizational Learning in the Global Orbital Launch Vehicle Industry. Academy of Management Journal, 53(3), 451–476. Retrieved 26 June 2017, from https://doi.org/10.5465/amj.2010.51467631
Merriam-Webster. (n.d.). Postmortem. In Merriam-Webster.com dictionary. Retrieved 13 June 2020, from https://www.merriam-webster.com/dictionary/postmortem
Merriam-Webster. (n.d.). Classification. In Merriam-Webster.com dictionary. Retrieved 7 June 2020, from https://www.merriam-webster.com/dictionary/classification
Nobel, C. (2011). Why Companies Fail - and How Their Founders Can Bounce Back. Retrieved 16 December 2018, from https://hbswk.hbs.edu/item/why-companies-failand-how-their-founders-can-bounce-back
Retail. (n.d.). In Wikipedia. Retrieved 17 June 2020, from https://en.wikipedia.org/wiki/Retail
Schwab, K. (2016). The Fourth Industrial Revolution: What It Means, How to Respond. Retrieved 13 June 2020, from https://www.weforum.org/agenda/2016/01/what-is-the-fourth-industrial-revolution/
Startup Genome. (2018). Global Startup Ecosystem Report 2018. Retrieved 20 May 2018, from https://startupgenome.com/reports/global-startup-ecosystem-report-gser-2018
Techopedia. (2012). Scripts. In Techopedia.com dictionary. Retrieved 7 June 2020, from https://www.techopedia.com/definition/10324/scripts
Techopedia. (n.d.). Technology Services. In Techopedia.com dictionary. Retrieved 17 June 2020, from https://www.techopedia.com/definition/5569/technology-services
University of Minnesota. (n.d.). Analysis of Quantitative Data. Retrieved 7 June 2020, from https://cyfar.org/analysis-quantitative-data
U.S. Small Business Administration. (2018). How Many Businesses Open and Close Each Year? Retrieved 19 June 2020, from https://cdn.advocacy.sba.gov/wp-content/uploads/2017/08/04125711/Frequently-Asked-Questions-Small-Business-2018.pdf
Wong, P. K., Ho, Y. P., & Autio, E. (2005). Entrepreneurship, Innovation and Economic Growth: Evidence from GEM Data. Small Business Economics, 24(3), 335–350. Retrieved 26 June 2017, from https://doi.org/10.1007/s11187-005-2000-1
Disclaimer: Any information, data and content on our business intelligence platform, web applications or websites are for general information use only. The information and analyses presented on our web applications and websites do not constitute any legal, business, investment or tax advice. Even though certain information are cited from third-party sources while believed to be reliable, Flipidea has not independently verified such information and makes no representations about the accuracy of the information or its appropriateness for any given situation. References to any securities or charts or graphs and all materials provided in connection with Flipidea’s web applications or websites are provided strictly on “AS IS” basis without any representations or warranties, express or implied, which should not be relied upon when making any investment decision. Any projections, estimates, forecasts, targets, recommendations, prospects, and/or opinions expressed in these materials are subject to change without notice and may differ or be contrary to opinions expressed by others.
Our proprietary data retrieval systems scan the Internet to automatically aggregate public information from news articles, websites, press releases, regulatory filings, and so on. Flipidea’s idea checker, tools, simulations, intelligence and algorithmic systems are powered by artificial intelligence and advanced analytics using our own proprietary data and synergised public data. Flipidea 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 edited on 8 July 2021.