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How to catch a unicorn? an exploration of the universe of tech companies with high market capitalization.

Technology companies with high market capitalization (often called unicorns) have been getting a lot of attention and media coverage recently. In 2013, Aileen LEE wrote a post in "Tech Crunch" about companies born after 2003 that have been evaluated, at some stage of their life-cycle at a very high threshold of market capitalization. She coined them "unicorns". Since then, a number of other publications have looked at the new and high market capitalization companies. These publications include Atomico (2014 & 2015), Bloomberg/FT (2014), EZRATTY (2014), Fortune (2015), A. LEE (2013, 2015), GILLES and MARCHANDISE (2013), Wall Street Journal (2015). These reports pick a 1 billion US $ market cap threshold and offer some justifications for such threshold.

This paper originates from a research (1) that aims to document the phenomenon by investigating a qualitative sample of 30 (2) companies that have recently been valued above the billion dollar threshold. This is a simple exploratory and descriptive exercise that cannot capture all aspects of unicorns. It does, however, identify some of their characteristics, offer hypothetical interpretations and discuss lessons learned. Case studies have also been included in the research in order to bring qualitative elements to the analysis.

Section one introduces the sample and explains how it has been built. Section two displays the models of growth identified in the study of company documents. The following sections look at the companies from a wider angle and investigates their business environment. The paper concludes with a tentative definition of unicorns.

The sample

A sample of companies was initially identified, without aiming to build a statistically representative sample. The sample was designed to capture enough diversity to explore some of the main features of the high-market capitalization phenomenon. The sample first considered a set of companies proposed by LEE (2013), which was concentrated on U.S.-based software companies. A report by Atomico (2015) opened up the global spread of these software companies identifying 182 such companies worldwide.

The sample was built around four criteria: sector, age, geographic location, and financial (variations around the $1 billion threshold). The sector categories follow the previous literature on high market capitalization. Specifically, LEE (2013, 2015) categorizes companies according to their business models. She distinguishes between (1) consumer companies, which are companies where the primary customer is a consumer, and (2) enterprise companies. Each category is further divided into two: consumer companies are split between e-commerce companies and audience business models; and enterprise companies are divided between "Software as a Service" (SaaS) companies and enterprise businesses. She defines the four resulting business model categories as follows:

1. Consumer companies

- E-commerce: companies where a consumer pays for a good or service through the internet or mobile, including companies like Uber and Airbnb;

- Audience: free for consumers, monetization through ads or leads (the freemium model is important for apps such as games like for King or Zynga).

2. Enterprise business companies

- SaaS: users pay (often via a "freemium" model) for cloud-based software, Akamai is the leading provider of cloud;

- Enterprise: companies pay for larger scale software, Criteo is a case in point.

The age category was set at above/below 10 years old in order to follow the previous literature. As the Atomico report (2015) revealed that China, with 30 (out of 54 for Asia) ranks second behind the USA (with 99 companies) globally, the sample attempted to rebalance with a global sample, adding companies from every region, but slightly tilting in favour of the EU. Finally, the financial criterion revolves around the 1 billion dollar valuation.

The 23 unicorns in our initial sample can be regrouped in two sets of companies: (1) companies 10 years old or younger with a USD 1 billion market valuation; (2) older companies with high market valuation over USD 1 billion. The following paragraphs explain in more detail how these companies were selected.

1. Our first set of 13 "young" companies was selected out of the Atomico 2015 report: Airbnb, Cloudera, Criteo, Flipkart, Garena, KakaoTalk,, Rocket Internet, Spotify, Twitter, Uber, Xiaomi, Zynga. These companies were chosen because they were young tech companies with high capitalization, from a large variety of sectors.

However one should stress that the companies were selected globally, with a bias towards EU companies: Criteo, King, Rocket Internet, Spotify. In addition, the European fast growing e-commerce company, Allegro (1999), was identified during the investigation and included in the sample.

2. A second set of 10 "old" companies included Google (1998), Apple (1976), Facebook (2004), and Amazon (1994). These four companies are often referred to as GAFA. Some would not have qualified as unicorns within the first 10 years of their existence. However as Table 1 reveals, they are now leading companies in terms of market cap, and provide a useful benchmark.

In addition to the above four companies, we included the Chinese generational equivalent of the GAFA group to balance the sample geographically. These were the BAT companies, Alibaba (1999), Baidu (2000) and Tencent (1998).

Since Allegro (Poland), and Tencent (China) were included in our sample and as both are subsidiaries of Naspers (South Africa), the third largest global player in e-commerce, this company (1915) was also included. Shazam (1999) was also included as an example of an "old" European software company.

Finally, to cover all business models as described by LEE, we also included Akamai (1998), a US-based "Software as a Service" company. Table 1 presents these 23 companies (3), categorized by business model (as per LEE, 2013, 2015) and ordered by region and market capitalization.

The sample although not representative does acknowledge the dominance of the US and of Asia. The sample comprises.

* USA: Ten unicorns and high-cap companies,

* Asia: Seven unicorns and high-cap companies (India, Singapore, South Korea and four for China),

* EU: Five unicorns for the EU and high-cap companies (France, Germany, UK and two for Sweden),

* Rest of the world: One African high-cap company.

Growth models: organic vs inorganic (M&A)

If one looks at the growth models adopted, the companies in the sample can be split into two groups: those with organic growth (OG) and those with inorganic growth (mergers and acquisitions - M&A).

The companies which adopted the first model (OG) achieved growth by increasing output and enhancing sales. Most of the companies we looked at are growing with the high-tech markets in which they operate: their business expands with the market. Companies that opt for organic growth are often technology-centric companies like Xiaomi, Tencent, and other games companies like King. 18 companies in our sample have adopted this model.

Most Asian companies opted for the organic model, which seems to be close to a "standard" model for industrial growth. Often starting in their home country, these companies are betting on the expansion of demand-driven markets, driven by:

* growth of the mobile market (10),

* emerging economies with fast income growth (11),

* emerging middle-classes (12),

* and young customers.

Companies in the second group grow through the acquisition of new businesses by way of mergers, acquisitions and take-overs. We have only five companies that have opted to adopt an M&A growth model (Google, Facebook, Flipkart, Naspers, and Rocket Internet) within diverse global reach strategies.

Table 3 indicates the main approach taken by the 23 unicorns, ranked by region and market caps. It also gives some indication (proxies) of the size of their market. It should be noted that companies often blend the two approaches, either historically (starting to acquire companies after a period based on organic growth: Google, Facebook, Apple) or synchronically. Hence, we have classified each company according to what appears to be its "dominant" model. For instance, among the GAFA companies Apple and Amazon appear to have grown mostly organically. Google and Facebook, however, have grown mostly through investments and acquisitions. Some companies, however, have stuck to a single model: for example, Xiaomi or Zynga have consistently opted for organic growth, whereas Flipkart and Rocket Internet have chosen to grow through mergers and acquisitions.

A number of companies have blended organic growth and mergers and acquisitions, either in the same period or after a certain amount of time, as illustrated by Tencent investing in South Asian companies. Both Alibaba and Xiaomi are linking partnerships to enter the Indian market. Xiaomi announced its intention to become an "Indian company" by establishing a start-up, with local R&D and manufacturing in India (WARING, 2015a). In April 2015, Indian investor and industry leader Tata invested in Xiaomi. Xiaomi continues to grow organically, but at the same time it is investing in more and more start-ups, especially in the burgeoning business of smart homes.

A strong access to finance

The important role of venture capital (VC)

Most of the investors in each of the unicorns of our sample were US VC companies. Other major investors included Naspers (South Africa) and the Japanese group Softbank (funding Alibaba, Zynga, and Criteo).

US VC companies also invested in EU firms: Bessemer Ventures (Criteo), Apax (King), KPCB (Shazam), Technology Crossover Ventures (Spotify). The only notable exception not to receive funds from US VCs was Rocket Internet (Germany): instead, it received funds from a much higher number of EU investors (from Nordic countries).

UK and French VCs invested in Criteo (Idinvest, and Elaia, two French VCs), in King and Shazam (Acacia Capital and DN Capital, both London based).

In Asia, investors are mostly regional, specialised companies like Singapore's Temasek, or the US company based in Beijing, IDG. Some leading US VC firms, like Sequoia or Accel, also lead in Asia.

GAFA companies are very active investors (directly as companies or indirectly through their CEOs personal investments) and play a major role as investors in start-ups. For example, Google invested in Baidu, Cloudera and Uber (14), and Amazon in Airbnb and Twitter. When companies reach the size of Alibaba or Tencent, they are more likely to become investors than receive investments: Tencent in Kakao Talk, and Garena. Alibaba and Xiaomi are investing in a growing number of start-ups, thus extending their ecosystems.

Some VCs have recorded more hits than others. For instance, VC Kleiner Perkins Caufield & Byers has been investing in a wide range of leading IT companies: Amazon, AOL, Electronic Art (EA), Google, Spotify, JD Com (China), Uber, Zynga, Twitter, Waze (i.e. six unicorns in our sample). Others invested in three of our unicorns, for example, Andreessen Horowitz (Twitter, Airbnb, and Zynga), IDG Ventures (Flipkart, Xiaomi, and Baidu), SV Angel (Facebook, Airbnb, and Zynga) Morgan Stanley (Flipkart, Twitter, and Zynga) or Sequoia Capital (Google, Alibaba, and Airbnb).

Several unicorns have been financed by numerous major investors, in particular Uber (financed by Baidu, Bezos expeditions, Goldman Sachs, Google, Microsoft), Facebook (Accel partners, Goldman Sachs, Microsoft, Peter Thiel, SV Angel), Zynga (Andersen Horowitz, Google, Kleiner Perkins Caufield & Byers, Morgan Stanley, Peter Thiel, SV Angel) or Airbnb (Amazon, Andersen Horowitz, Sequoia Capital, SV Angel).

While the data covers only our small sample of unicorns, it offers some strong indications about the existence of a dense VC ecosystem supporting the unicorn phenomena. Together with IT companies and other industrial players, VC companies have built a favourable environment for start-ups, as illustrated by the creation of the sFund launched by KPCB with Amazon, Facebook, Comcast and Liberty Media.

The extent to which the companies in the sample rely on VC funds varies. Some receive large funds, which attract news coverage whereas others collect more modest amounts and rely instead on self-funding: for example, Baidu, which has only received USD 161.2 million since its inception in 2000. The amount of money needed is linked to the strategy adopted. Some companies like Uber or Rocket Internet have aggressive commercial strategies and use the funds collected for buying market shares in new tech markets where it is expected that first mover advantage will generate a "winner takes all" result.

In the EU, the UK (15) has the most venture capital available, followed by France. This (relative) strength may account for the presence of companies from the UK (Shazam) and France (Criteo, Blabla car) in this sample, and also in the Atomico report. It may be difficult to state that the absence of unicorns from other countries like Italy or Spain is directly linked to their weaker VC sector, but it could have had an impact.

The EU/US gap

VCs have raised more funds in the US than they have in the EU. In 2014, according to an AFME/BCG report (AFME, 2015: 27), EUR 488 billion of private equity were ready to be invested in the US, whereas in the EU, only about half the amount--i.e. EUR 245 billion--was available. The level of VC and angel investments is significantly higher in the US than in Europe: in the US, EUR 26 billion is invested annually by VC firms and EUR 20 billion by Angel investors, while in Europe these amounts are EUR 5 billion and EUR 6 billion respectively (AFME, 2015: 27); an even lower ratio (almost 1 to 5). Historically, the US benefits from an early mover advantage because modern venture capital was born there in 1946, arriving in Europe later--around the 80s (VERON 2012: 26).

The level of VC investments in the US, between 2007 and 2013, hovers at around USD 30 billion (this fell in 2009 with the crisis - EY, 2014: 9). In Europe, it fluctuated at around USD 7 billion in Europe (16) during the same period (EY, 2014: 9). The global level of investment per year fluctuated around USD 50 billion, falling to USD 35 billion in 2009 (EY 2014: 8). Investments from the top 10 US investors between February 2013 and 2014 (EY 2014: 39) added up to USD 14.47 billion - three times the amount that AFME claims is invested by EU VC funds.

In 2015, venture capital soared to record heights. The venture capital ecosystem deployed USD 58.8 billion across the United States in 2015, marking the second highest full year total in the last 20 years (Moneytree report, 2016 quoted by Marketwired). CB Insights (2016: 40) indicates an even higher number: USD 74.2 billion across 4,890 deals. CB Insights (2016: 31) points out that there is a correlation between this high global record and the number of unicorns:
"The peak of these investments promoted 72 VC-backed companies to
achieve Unicorn status [...] during the course of 2015. By comparison,
53 companies reached Unicorn status in the year previous".

The amount of funds available in Europe has increased over the last decade. Equity financing for European VC-backed companies reached EUR 7.9 billion, up from EUR 6.3 billion in 2013. BOLLEN adds that the number of venture-backed IPOs (17) in the region more than tripled to 55 and these VC-backed IPOs raised a total of EUR 3.7 billion. In 2013, 18 IPOs raised a total of EUR 500 million. The online platform Rocket Internet was Europe's largest venture-backed IPO in 2014, raising EUR 1.4 billion on the Frankfurt Stock Exchange. CB Insights (2016: 64) reveals a similar all-time high for the EU in 2015, as VC-backed companies in Europe raised USD 13.4 billion in funding across 1,387 deals.

VC investment in Asia has also become more substantial. VC companies are backing Asian start-ups more and more, especially in China and India. In China, the VC investment level went from USD 3.9 billion in 2007 to USD 3.5 billion in 2013--but it had peaked at USD 6.5 billion in 2011 (EY 2014: 9). It went up again in 2014 to another peak of USD 11.2 billion (T. LEE, 2016). T. LEE notes the striking amount of VC funding in China, where there was more VC funding in 2015 than in all the other Asian countries (18) combined: USD 41.8 billion out of USD 55.3 billion for Asia (19). T. LEE highlights that the amount invested in Didi (the Chinese competitor of Uber) alone (USD 3 billion) is higher than the total amount invested in Singapore, Japan and Israel. In India, VC investment went from USD 0.9 billion in 2007 to USD 1.8 billion in 2013 (EY 2014: 10), and up to USD 7.9 billion in 2015 (T. LEE, 2016).

The stronger relationship between China and the US may not really improve the position of the EU or the access to funds for EU tech start-ups. The scale of the funds involved in Asia and the US, as we have just noted, is staggering.

Table 4 (Exits (20) of the unicorns) shows the year and amount collected on company exits in our sample. 11 out of the 23 selected unicorns went public. The remaining 12 are still private companies. The amounts raised vary hugely from the modest USD 54-55 million collected by Amazon in 1997 to the skyrocketing USD 25 billion raised by Alibaba in 2014, the world's largest-ever IPO.

In general, the amounts raised in the initial IPO exits (21) have increased over the years for the sampled companies. When Akamai went public in 1999, it collected USD 1.3 billion; it was, then, the fourth-largest first-day increase (22). Rocket Internet raised EUR 1.4 billion, the largest EU IPO in 2014. However, even though the number of VC-backed IPOs in Europe has tripled, it is still very modest or low compared to the US (below the 1 billion threshold most of the time).

Beyond raising funds, FILLOUX considers that the main issue for most of Europe (but not the UK) is the exit for successful companies: European stock markets cannot compete with the Nasdaq (FILLOUX, 2015). According to a study by France Digital, he quotes, "9 out 10 start-up companies financed by VCs are sold to foreign acquirers (US and Asia)".

A dense ecosystem of founding fathers (and mothers?) (23)

Start-up entrepreneurs often introduce themselves as "serial entrepreneurs". Through their experience and their university backgrounds, they have built over time networks they can use for their new endeavours.

The number of serial entrepreneurs is significant in our sample: 24 of 63 listed founders have created other companies before. The average entrepreneur in our sample is a seasoned businessman with a strong academic background from a top university (e.g. Cornell, Harvard, MIT, Stanford, Yale in the US or similar in the EU e.g. Ecole des Mines). Among her US 2013 sample, LEE notes that within these selective universities:
"Stanford leads the roster with an impressive one-third of the
companies having at least one Stanford grad as a co-founder. Former
Harvard students are co-founders in eight of 38 unicorns; Berkeley in
five; and MIT grads in four of the 38 companies".

Our sample also includes 7 entrepreneurs from Stanford. This highlights the role played by the Silicon Valley cluster.

This supports what LEE (2015) wrote in her 2015 edition of the unicorns list:
"Take heart, "old people" of Silicon Valley: Companies with educated,
tech-savvy, experienced 30-something, co-founding teams with history
together have built the most successes."

She further notes:
"[...] 76% of companies have founders with entrepreneurial history and
a track record of founding something else previously".

She stresses the role of education:
"Education seems kind of important. About half our list have extremely
well-educated co-founders, who are graduates of a "top 10" U.S.

However, she also finds that "19% also have a co-founder who dropped out of college". There are some of these outliers in our sample: Facebook's founder, Mark ZUCKERBERG is a college drop-out; the founder of Uber, Travis KALANICK, also dropped out of the University of California, Los Angeles; and Jack YUN MA, the founder of Alibaba, was an English teacher. And of course, Steve JOBS.

The importance of this kind of background and technical expertise is also emphasized in the Atomico Report (2015) that notes that
"144 out of 156 (92%) companies started out with a tech- or
product-driven founding team, signaling the overwhelming likelihood
of success in technology with engineers at the top".

With regard to location, Atomico has been saying since its first report that, although Silicon Valley was obviously important, the majority of companies (65%) originate from other places. For the EU, Atomico highlights the role of "tech hubs like Berlin, Helsinki, London and Stockholm".

Still, Atomico acknowledges that "in absolute terms, Silicon Valley is the single most prominent tech hub with 54 companies (24). The top six hubs are rounded out by Beijing (17), New York (7), Stockholm (5), Los Angeles and London (4)". Within Silicon Valley, Stanford dominates the other universities and seems to be the stronghold of the cluster.

Conclusion: what are unicorns and how to understand the phenomenon?

These descriptive features allow us to attempt to define unicorns. In general, unicorns are IT-based (software mostly, but also hardware). They are often rather young global companies that match unsatisfied demand with supply through the production (which can be scaled up) of innovative and usually affordable services and products. These are usually part of the mobile internet wave, and rely on connectivity (high speed networks, mobile and fixed), new devices (smartphones, tablets, phablets...) and the opportunities these bring. They are grounded in network effects, and demand-side economies of scale and scope. They depend on a strong favourable business environment, developing organically and building on fast expanding markets (emerging economies, middle classes). They are VC-dependent and the competition for funding can generate impressive (inflated?) valuations. These companies can be disruptive for other sectors and firms. Besides, as regards competition and regulation, the newcomers often operate in grey areas.

This heterogeneous universe of unicorns, allows us to discuss some potential implications for innovation policies:

1. The development of unicorns is clearly market-led and does not appear to be the result of any specific policies, at least directly. Indirect support, however, seems highly important. These companies benefited from existing measures like tax shelter, special support for SMEs in the EU and in the US, support for infrastructures, etc. Support for the business environment seems to have been crucial: for example, access to capital and also to the expertise needed from universities and research centres (some of the unicorns were spin-offs from labs like Akamai from the MIT).

2. The mobile wave has also been market-led. Most of the policies in the EU and the US were designed for the fixed telecommunications markets, to liberalize the sector or to regulate it. However, the outcomes have been different on each side of the Atlantic. One can then ask the question: did the EU miss the mobile Internet turn (SIMON, 2016, b)? Was this because too much focus had been put on the deployment of new fixed networks such as ultrahigh broadband? The question is relevant not only from the point of view of policy but also manufacturing (the fall of Nokia).

3. Lastly, the rise of these companies, especially of the disruptive ones, raises the question whether digitization means positive disruption ("creative destruction") or negative disruption (job losses). It is unclear whether the disrupters bring harm and whether incumbent outputs have been diminished. The economic debate is, by and large, dominated by sheer rhetoric, not evidence.

At the same time, striking a balance between technological innovation and the protection of existing rights is a challenging task for policy makers. Therefore it is not surprising to find that governments are reacting in various ways: siding with incumbents, backing mavericks, or simply adopting a wait-and-see approach. In the short term, extending legacy protection for incumbents could make the unicorn bubble burst, as disrupters and new start-ups would be barred from entering the markets. Protecting incumbent businesses is likely to be a short-term strategy, but it may not help these businesses to make the transition.


AFME (2015): Bridging the growth gap: Investor views on European and US capital markets and how they drive investment and economic growth, Association for Financial Markets in Europe/Boston Consulting Group, February. institutions growth bridging growthgap afme report/

Atomico Report (2014, 2015):

AUSTIN, S., CANIPE, C. & SLOBIN, S. (2015): The Billion Dollar Start-ups Club, Wall Street Journal and Dow Jones.

CB Insights/KPMG, (2016): Venture Pulse Q4 2015. Global Analysis of Venture Funding.

EY (2014): Venture Capital Insights 2013 year-end. Global VC landscape, angel and incubator participation trends and exit landscape.$FILE/EY-venture-capital-insights-2013-year-end.pdf

EZRATTY, O. (2014): "La dimension financiere de l'uberisation"

FILLOUX, F. (2015): "Funding Innovation. France's Image Problem".

GILLES, L. & MARCHANDISE, J.F. (ed) (2013): La dynamique d'Internet, Prospective 2013, Commissariat General au Plan, Paris.

GRIFFITH, E. & PRIMACK, D., (2015): "The Age of Unicorns", Fortune.

HUANG, Z. (2015): "China's middle class has overtaken the US's to become the world's largest".

LEE, A.: - (2013): "Welcome To The Unicorn Club: Learning From Billion-Dollar Startups".

- (2015): "Welcome To The Unicorn Club 2015: Learning From Billion-Dollar Startups".

MADDISON, A. (2011): Resource Revolution: Meeting the world's energy, materials, food and water needs. Mc Kinsey Global Institute.

Marketwired (2016): "$58.8 Billion in Venture Capital Invested Across U.S. in 2015, According to the MoneyTree Report".

Simon, J.P.: - (2016a): Catching a rising star. Techno-platforms study of companies with high market capitalisation (HICAP) running global digital platforms, EC JRC IPTS. 2 vol.:

- (2016b): "How Europe missed the mobile wave", info, Vol. 18 Iss: 4, pp. 12-32.

VERON, N., (2012): "Access to finance" in VEUGELERS, R., Van POTTELSBERGHE, B. & VERON, N. (2012): Lessons for ICT Innovative Industries: Three Expert's Positions on Financing, IPR and Industrial Ecosystems, EC JRC IPTS:

WARING, J. (2015): "Uber raises $1B for China expansion". campaign=MWL AS20150901.html&utm medium=email&utm source=Eloqua

Jean Paul SIMON

JPS Public Policy Consulting, Sevilla

(1) The paper draws on a report for the European Commission's JRC Institute for Prospective Technological Studies on companies with high market capitalization (over $1 billion), the so-called unicorns (SIMON, 2016a). The views expressed are purely those of the author and may not in any circumstances be regarded as stating an official position of the EC. The author would like to thank Marc BOGDANOWICZ (IPTS) for his suggestions and careful reading and editing of the report.

(2) 23 unicorns and high-cap companies, as well as 7 gems see footnote 3. In this paper we only introduce the 23 unicorns.

(3) In the study a third set of companies complements the set of unicorns. It is composed of "young" companies (10 years old or younger) with market valuations estimated just below the one billion dollar threshold.

(4) If year differs it appears in the table.

(5) 2015, for GAFA companies, the second figure (in bold) gives the total assets.

(6) We have included Alibaba under the "consumer companies" category as an e-commerce company which seems logical as the company claims to be the largest online and mobile commerce company. However, at the same time, the Chinese global leader makes it clear to be a B2B and not a consumer oriented company.

(7) Excluding associates and joint ventures.

(8) If year differs it appears in the table.

(9) 2015, for GAFA companies, the second figure (in bold) gives the total assets.

(10) With the exception of Indian Flipkart.

(11) MADDISON (2011) argues that incomes are rising in developing economies faster and at a greater scale than at any previous point in history: 12 years to double per capita GDP in China, and 16 for India since the late 90s.

(12) China's middle class reached 109 million in 2015, and overtook the US's for the first time ever, according to a Credit Suisse report released 13 October (quoted by HUANG, 2015).

(13) Metrics vary. The table gives what looks like the relevant metrics for the market where the company is operating.

(14) Microsoft invested in Uber in July 2015. The company has investments in Facebook.

(15) The 2016, House of Lords report on "Online Platforms and the Digital Single Market" quotes TechUK stating that within the EU the UK was particularly successful at producing unicorns: "13 new unicorns emerged in Europe in 2014 and eight of them were founded in the UK". Shazam is one of the unicorns quoted by TechUK.

(16) EY gives Europe and not the EU.

(17) On IPO one of the best sources of historical information is Jay RITTER'S website:

(18) India: USD 7.9 billion, South Korea: USD 1.19 billion, Singapore: USD 970 million, Japan: USD 569 million, and Taiwan: USD 440 million.

(19) E-commerce dominates with USD 15.9 billion.

(20) The method by which venture capitalists or business owners get out of an investment that they have made in the past. In other words, the exit strategy is a way of "cashing out" an investment. Examples include an initial public offering (IPO) or being bought out by a larger player in the industry. Also referred to as a "harvest strategy" or "liquidity event".

(21) An initial public offering (IPO) is the process through which a company makes the transition from a privately held entity to a public company with stock traded on one of the major stock exchanges. Typically, a company going through an IPO is young and relatively unknown, therefore IPOs generally are considered riskier investments. However, established private companies occasionally decide to "go public" in order to raise more capital.

(22) Or first day pops. The "pop," also referred to as the first-day price spike, is the price differential between the offering price of an IPO stock and its closing price on the first day of trading.

(23) None! A. LEE (2015) notes that there were no female CEOs on her 2013 Unicorn list, and only 3 in 2015, adding a nice understatement: "There's still too little diversity at the top in 2015".

(24) Out of 140 in this particular version of the Atomico report
Table 1 - Distribution of the sample of 23 selected unicorns
by business model and region, ranked by
market cap (2014)
Company      Year     Year     Market cap,     Revenues
             created  valued   billion USD
                      1        unless
                      billion  otherwise
                      USD      indicated
                               (2014 (4)) (5)
Apple        1976              USD 737.54 /    USD  183 billion
                               231. 8
Google       1998              USD 362.56/     USD   66 billion
Facebook     2004              USD 226.37/     USD   12.46 billion
Amazon       1994              USD198.28/      USD   88.98 billion
Uber         2009     2013     USD 41          USD  415 million

Airbnb       2008     2011     USD 10          USD  45 million
Alibaba (6)  1999              USD 204         USD   8.4 billion
Tencent      1998              USD 200         EUR  12 billion
Baidu        2010              USD  71.58       US   7.9 billion
Xiaomi       2010     2011     USD  46         RMB  74.3 billion
                                               (around EUR 11
Flipkart     2007     2012     USD 3 in        Rs 28.46 billion
                               2014            (around EUR 413
                               USD 15          million)
                               expected end
                               of 2015
Rocket       2007              EUR 2.6         EUR 137.9 million
Internet                       billion
Naspers      1915              EUR 64          EUR 105 billion
(South                         billion
US           2006
Twitter      2007              USD 33.25       USD 1.4 billion
Zynga        2007              USD 2.27        USD 690 million
Asia         2006
KakaoTalk    2009              USD 5           USD 203 million
Garena       2009              USD 2.5         USD 200 million
EU           2006
Spotify      2003     2011     USD 8.4         USD 747 million
(Sweden)                                       (2013)     1999              USD 5.28        USD 2.6 billion
Shazam (UK)  1999     2015     EUR 1 billion   GBP 9 million
Company      Profit  Number of
             (-/+)   employees

Apple        ++       92600 +

Google       ++       40000

Facebook     +         6337

Amazon       -/+     117300

Uber         --        1000/
Airbnb       -         600 +
Alibaba (6)  +        22072
Tencent      ++      27 690
Baidu        ++      40500
Xiaomi       +       8000

Flipkart     -       20000

Rocket       -       1282
Naspers      +       28000 (7)
Twitter      - -     3.638
Zynga        -       2000+/-
KakaoTalk    +       700+
Garena       NA +    2000+
(Singapore)  likely
Spotify      -       NA
(Sweden)     +       600+
Shazam (UK)  -       200

Company     Year     Year     Market cap,  Revenues
            created  valued   billion USD
                     1        unless
                     billion  otherwise
                     USD      indicated
Enterprise  2008
Akamai      2005              USD 13.42    USD 1.96 billion
Cloudera    2008     2014      USD 1.2     USD 100 million
Criteo (e)  2005               USD 1.8     EUR 612 million
Company     Profit  Number of
            (-/+)   employees

Akamai      +       5105
Cloudera    -        701
Criteo (e)  +       1300

Source: compiled by the author from references listed in
the case studies, companies' websites, as well as from
Atomico (2014, 2015), Crunchbase, CBInsights.

Table 3 - Models of growth of unicorns, ranked by
region and market caps (2014)

Company            Organic  Mergers and  Profit
                   growth   Acquisition  (+/-)
                   model    model
                   (OG)     (M&A)
Apple (1976)       OG                    ++

Google (1978)               MA           ++

Amazon (1994)      OG

Akamai (1998)      OG                    +

Facebook (2004)             MA           +
Twitter (2006)     OG                    --
Zynga (2007)       OG                    -
Airbnb (2008)      OG                    -

Cloudera (2008)    OG                    -
Uber (2009)        OG                    --
Tencent (1998)     OG                    ++

Alibaba (1999)     OG                    ++
Baidu (2000)       OG                    ++
Kakao Talk (2006)  OG                    +
Flipkart (2007)             MA           --
Garena (2009)      OG                    n.a.
Xiaomi (2010)      OG                    +

Shazam (1999)      OG                    - (2003)    OG                    +

Criteo (2005)      OG                    +

Spotify (2006)     OG                    -

Rocket Internet             MA           --
Naspers (1915)              MA           ++
Company            Size of market (13)

Apple (1976)       460 retail stores in 17 countries
                   and an online store available in
                   39 countries.
Google (1978)      More than 100 languages and in
                   more than 50 countries
Amazon (1994)      10 online marketplaces, 2 in
                   North America, 5 in Europe, 3 in
Akamai (1998)      Serves top 30 media and
                   entertainment companies.
                   170 000 servers in more than
                   1 300 networks and over 100
Facebook (2004)    1.248 billion active users
Twitter (2006)     288 million active users
Zynga (2007)       100 million monthly users
Airbnb (2008)      25 million guests, in 34 000
                   cities, 190 countries
Cloudera (2008)    20 countries, 1 400 partners
Uber (2009)        56 countries, 200 cities
Tencent (1998)     QQ 848 million active users
                   WeChat 549 million active users
                   N[degrees]1 worldwide for video games
Alibaba (1999)     255 million active buyers
Baidu (2000)       worldwide 642 million users
Kakao Talk (2006)  140 million users
Flipkart (2007)    26 million registered users
Garena (2009)      17 million monthly active users
Xiaomi (2010)      on PC, 11 on mobile 61.12 million phones sold in
Shazam (1999)      100 million active users (2003)    356 million average monthly
                   unique users
Criteo (2005)      37 countries, 4000
                   e-commerce companies
Spotify (2006)     58 markets, 60 million active
Rocket Internet    110 countries (Not US, China)
Naspers (1915)     More than 130 countries

Source: Crunchbase, compiled by the author

Table 4 - Exits of the unicorns

Company          Year  Amount (million)
Amazon           1997  USD 54-55
Akamai           1999  USD 1300
Google           2004  USD 1670
Facebook         2012  USD 16000
Twitter          2013  USD 1820
Tencent          2004  USD 199
Baidu            2005  USD 109
Alibaba          2014  USD 25000
Criteo           2013  EUR 250-300         2014  USD 500
Rocket Internet  2014  EUR 1400

Source: compiled by author
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Title Annotation:Firms and Markets
Author:Simon, Jean Paul
Publication:DigiWorld Economic Journal Communications & Strategies
Geographic Code:90ASI
Date:Oct 1, 2016
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