Descriptive Analysis: Why Did These 500 Trending Startups Shut Down?

OUR REAL WORLD PROBLEM of companies won't survive up to 10 years. Over 85% Just in the USA alone, companies shut down every year (U.S. Small Business Administration, 2019). more than 360,000 "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. ABSTRACT (TL;DR) : 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. Objectives : 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 500 inactive companies were observed for this analysis. Methods : Out of 500 companies, 59 ( ) companies had no product-market fit advantage, 75 ( ) companies suffered from poor business models, and 96 ( ) companies faced strong competition. Respectively, 73 ( ) companies shuttered due to the lack of funds, while 40 ( ) companies from failed fundraising. The shutdown rate was high among 131 ( ) startups, 141 ( ) small companies, and 82 ( ) SMEs after they had secured seed, early stage, and M&A funding. Non-innovative new and small companies generally shut down within 1-3 years ( ) and 3-5 years ( ) of operation. Overall, the 385 ( ) companies that operated in technology related industries were predominantly co-founded by 821 ( ) males founders. Findings 11.8% 15% 19.2% 14.6% 8% 26.2% 28.2% 16.4% 11 companies at 2.2% 26 companies at 5.2% 77.2% 87.5% : A majority of the reasons for shutdown are technical shortcomings which could be avoided through experiential learning, coaching, and mentoring. This report implicates that failure may be part of the process of building a successful company, especially an innovative one. Conclusions INTRODUCTION "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 2016 and 2018, our global startup economy created USD $2.8 trillion in value, a 20.6% increase from 2015 to 2017, and it continues to grow every year (Startup Genome, 2019). 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 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? survivorship bias Since our inception in September 2017, (pronounced as ' ') is a Data-as-a-Service (DaaS) platform offering analytical information about business failures. In other words, we analyse business failures. Flipidea flee-pee-dia 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 and prudent strategies. 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, reports ( ), 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. post-mortem an analysis of an event after it is over 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 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. human-in-the-loop 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 500 inactive companies in our live database, which are also published on . Therefore, we for this report. www.flipidea.co only observed these 500 inactive companies Firstly, what is 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. Descriptive analysis Since our data are ( ) and stored in numerous collections, we wrote ( ) to calculate our descriptive statistics. classified systematic arrangement in groups scripts aka scripting language, which is a programming language to automate the execution of tasks 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? what were the of the companies? lifespans what were the last of the companies? funding statuses what were the total funding amounts raised by the companies? how many employees did the companies hire? what is the among the companies' founders? gender statistics For all of the descriptive statistics, the percentages were calculated based on 500 inactive companies and the frequency (n) of each data features or variables. percentage % = (n/500)*100 For the gender statistics, the percentages were calculated based on 938 data objects ( ) and the frequency (n) of each data features or variables. which comprised of 903 founders, 11 corporations, and 24 no data percentage % = (n/938)*100 In the event you wish to calculate the frequency (n) of each data features or variables, based on the calculated percentages: gender statistics: base on 938 data objects the other descriptive statistics: base on 500 inactive companies n = (calculated percentage%/100)*[500, or, 938] For funding status, we currently classified the different as: financing stages : angel, pre-seed, seed seed : pre-series A, series A, series B early stage : series C and onwards, late stage mezzanine 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. Lastly, we classified the companies into different categories by the number of employees in accordance to the criteria of : Organisation for Economic Co-operation and Development (OECD, 2020) : lesser than 10 startups or micro-enterprises small enterprises: 10 to 49 medium enterprises: 50 to 249 : lesser than 250 small-and-medium enterprises (SMEs) large enterprises: 250 or more 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 5 common reasons listed below: experienced by strong competition 96 companies at 19.2% experienced by poor business model 75 companies at 15% experienced by lack of funds 73 companies 14.6% experienced by no product-market fit 59 companies at 11.8% experienced by failed fundraising 40 companies at 8% From the observation of the top 5 reasons for shutdown, the outcome of and could seemingly be related to design, advantage, and . lack of funds failed fundraising poor business model no product-market fit strong competition Where did the companies base at? From our classification of data, the top 5 locations are: were based in 236 companies at 47.2% United States were based in 115 companies at 23% India were based in 28 companies at 5.6% United Kingdom were based in 12 companies at 2.4% Canada were based in 10 companies at 2% Indonesia The data showed that the companies were mostly from North America, India, Europe, and Southeast Asia. There are two main reasons: the Total early-stage Entrepreneurial Activity ( ) rates in North America ( ), Europe ( ), and India ( ) are substantially high (Global Entrepreneurship Monitor, 2020) TEA United States 17.4%, Canada 18.2% United Kingdom 9.3%, Germany 7.6% 15% currently, our data retrieval systems only monitor companies from all English-speaking countries, but we are gradually populating our database with data from non English-speaking countries as well Which industries did the companies operate in? From our classification of data, the top 5 industries are: were classified as 289 companies at 57.8% internet industry were classified as 25 companies at 5% blockchain industry were classified as 18 companies at 3.6% retail industry were classified as 17 companies at 3.4% food & beverages industry were classified as 16 companies at 3.2% consumer electronics industry The data showed that 385 ( ) companies operated in the technology related industries, such as , , , , , , , , and . 77.2% internet blockchain consumer electronics transportation information technology & services interactive media & services media , renewables & environment machinery application software What were the lifespans of the companies? From the timeline of companies' earliest formation date to the latest shutdown date, we recorded July 1841 to 31 May 2020. The lifespans of the companies are: : less than 1 year 11 companies at 2.2% : 1 to 3 years 42 companies at 8.4% : 3 to 5 years 26 companies at 5.2% : 5 to 7 years 8 companies at 1.6% : 7 to 10 years 14 companies at 2.8% : 10 to 15 years 4 companies at 0.8% : 15 to 20 years 2 companies at 0.4% : none 20 to 30 years : more than 30 years 4 companies at 0.8% : no data 389 companies at 77.8% The data showed that majority of the companies shuttered within 1-3 years and 3-5 years. Many companies did not share their operating dates, or either their formation or shutdown dates were missing. Hence, 77.8% have no data. What were the last funding statuses of the companies? From our classification of data, the funding statuses are: : seed 107 companies at 21.4% : early stage 63 companies at 12.6% : late stage 17 companies at 3.4% : initial coin offering 1 companies at 0.2% : M&A 83 companies at 16.6% : initial public offering 3 companies at 0.6% : private equity 5 companies at 1% : no data 221 companies at 44.2% The data revealed that many companies shuttered after they secured their , , and financing rounds. Unfortunately, many companies did not share their investment information. Therefore, we were not able to verify the number of companies that did not receive any investment due to at seed stage, and those who struggled with at early stage. seed early stage M&A ( ) merger & acquisition failed fundraising lack of funds What were the total funding amounts raised by the companies? Overall, a total of USD $11.8 billion ( ) were invested in the companies: currency converted into USD : less than $1 million 52 companies at 10.4% : $1 million to $5 million 56 companies at 11.2% : $5 million to $10 million 26 companies at 5.2% : $10 million to $50 million 68 companies at 13.6% : $50 million to $100 million 21 companies at 4.2% : $100 million to $500 million 19 companies at 3.8% : $500 million to $1 billion 2 companies at 0.4% : $1 billion to $5 billion 2 companies at 0.4% : none more than $5 billion : no data 254 companies at 50.8% From the funding amounts raised by the companies, the investment size can be associated to the , , and financing rounds in the venture capital market. Nevertheless, USD $11.8 billion were invested in 246 out of the 500 companies, and were forever lost. seed early stage M&A ( ) merger & acquisition How many employees did the companies hire? From our classification of data, the number of employees hired are: : 1-10 employees 131 companies at 26.2% : 10-50 employees 141 companies at 28.2% : 50-100 employees 45 companies at 9% : 100-250 employees 37 companies at 7.4% : 250-500 employees 25 companies at 5% : 500-1,000 employees 14 companies at 2.8% : 1,000-5,000 employees 7 companies at 1.4% : 5,000-10,000 employees 2 companies at 0.4% : more than 10,000 employees 6 companies at 1.2% : no data 92 companies at 18.4% From the number of employees hired by the companies, the data indicated that majority of the companies were startups or micro-enterprises ( ), small enterprises ( ), and SMEs ( ). 1-10 10-50 lesser than 250 What is the gender statistics among the companies' founders? From our classification of gender, we gathered a total of 938 data objects: : male founders 821 males at 87.5% : female founders 82 females at 8.7% : founding corporations 11 corporations at 1.2% : no data 24 at 2.6% The data clearly showed that majority of companies were co-founded by male founders, particularly in the technology related industries. DISCUSSION The findings of this descriptive analysis show that: companies with poorly designed business models have no product-market fit advantage and generally face strong competition as a result, the companies have a lower chance in securing investment funds and eventually shut down due to lack of funds shutdown rate is high among the startups, small companies, and SMEs during the early stage and post-M&A stage non-innovative new and small companies generally shut down within 1-3 years and 3-5 years of operation founders are predominantly male ( ) in co-founding a technology related business 87.5% Furthermore, the statistics seems to suggest that many new and small companies failed to identify their before they could effectively validate their businesses to achieve . problem-market fit ( ) aka problem-solution fit product-market fit Problem-market fit is achieved when a company identified an existing real-world problem ( ) that is solved by its solution offering through deliberate processes of and . critical pain points suffered by its customer segment experimentation validation Now, the interpretation of the reasons for shutdown is not straightforward because we merely observed 500 inactive companies. However, it is safe to say that most of the new and small companies often fail to design a good business model, and more often than not, there is for their product-or-solution offering. no market need In fact, Marc Andreessen explained that "you can always feel when isn't happening. The customers aren't getting value out of the product, word of mouth isn't spreading, usage isn't growing that fast, press reviews are kind of 'blah', the sales cycle takes too long, and lots of deals never close" (Griffin, 2017). product-market fit As a result, many of the companies 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). That said, a majority of the reasons for shutdown are technical shortcomings which could be avoided through experiential learning, coaching, and mentoring. Therefore, this report implicates that failure may be part of the process of building a successful company, especially an innovative one. Moreover, Triebel et. al. (2014) remarked that "every company founder should be aware of the fact that an important factor for establishing a company is the fault tolerance". They further explained that "fault tolerance, in the context of company foundation, generally describes a process of recognising, accepting, and learning from errors" (Triebel et. al., 2014). In our future research, we will further investigate the effects of failure as part of the process of building a successful and innovative business. Especially when "failure must be accepted as a secondary effect" to founding successful companies, as advocated by Triebel et. al. (2014). OUR CONCLUSION A descriptive analysis of 500 inactive companies have been conducted. Based on this analysis, we have shared our findings which highlighted a majority of the reasons for shutdown are technical shortcomings that can be avoided. This analysis was limited by our small and incomplete datasets. With bigger and more complete datasets, we will be able to include inferential analysis, and eventually predictive analysis, to discover more meaningful insights. Therefore, this report is presented as a work in progress. Nevertheless, 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. Written by , Co-founder & CEO, Paul Lee Flipidea , Business Research Analyst, Elina Kamaluddin Flipidea , Business Research Analyst, Sharifah Nadzirah Flipidea D. T. C. Lai, UBD Citation and republishing Flipidea articles and reports may be republished in accordance with our and . 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