We're a young start-up developing disruptive technology for more efficient and sustainable production. The cooling strategies used so far for machining cause high-energy consumption and are often harmful to employees' health. We've developed an innovative process cooling approach for machining that allows more efficient and sustainable manufacturing.
Our research on the top 1,000 European tech start-ups showed that these companies follow one of a small number of distinct paths to successful scaling, built around a core strategic approach: network, scale, product, or deep tech. While these categories are not mutually exclusive, each of them provides a path to scale. We believe this classification provides practical utility for entrepreneurs, executives, and investors as they launch and attempt to scale ventures (Exhibit 3).
To identify European tech champions, we selected a sample of 1,000 tech companies founded after the year 2000, based on their valuations. Our sample includes companies from 33 European countries; among them, the United Kingdom is home to the most start-ups, with one-third of the sample, followed by Germany and France. The companies in our sample cover nine verticals: AI, B2B software as a service (SaaS), biotech and healthcare, e-commerce and consumer, fintech, IT hardware, marketplace, media and content, and mobility. Approximately 60 percent of the start-ups come from biotech, B2B SaaS, and fintech. We developed our data set in February 2021 from our proprietary integrated database that leverages sources such as Crunchbase, PitchBook, Dealroom, LinkedIn, and S&P Capital IQ. We then assessed these companies against the following 15 critical capabilities for scaling:
To achieve this early commercial success, outperforming scale start-ups invest more heavily in sales, marketing, and business-development roles. On average, these positions account for 42 percent of all employees, compared with about 33 percent for the other strategic plays. To support sales, they have a stronger focus on operational roles (17 percent) compared with product (9 percent) and deep-tech plays (14 percent).
As scale companies mature, the relative size of their commercial functions decreases, resulting in the increased importance of other roles, particularly product and tech roles. In our sample of start-ups that are less than five years old, the share of commercial functions is 43 percent on average, which drops to 39 percent for start-ups that are six to ten years old, and 34 percent for older start-ups. A closer look at five of the most valuable European scale players (Farfetch, HelloFresh, Spotify, The Hut Group, and Zalando) shows this transition into product and tech roles: having matured to a degree, these scale players now have about 35 percent of their employees in commercial positions.
Network- and scale-play start-ups conduct between 1.6 and 1.9 deals across all funding rounds, which is twice as many as the average in our sample (Exhibit 7). By contrast, M&A activity among ventures pursuing a product play is below the average across the group, at 0.7 deals, and most start-ups going for a deep-tech play are unlikely to engage in M&A at all.
With relatively limited resources initially, product-play start-ups focus intensely on both reaching adoption fast and providing a strong customer experience. To help achieve this, they often develop their initial product for a select, well-defined use case. This drives early usage as well as the collection of detailed customer feedback, which is essential for further product development and scaling-up to a larger customer base. For example, N26, the German neobank, focused on adoption early, offering only basic banking products such as current accounts and credit cards. Only later did the company expand its offering to adjacent categories. After attaining scale, it subsequently moved to higher-margin products, such as lending and investing. At that point, N26 was ready to explore partnerships with other fintechs, which helped to open up cross-sell opportunities.
Deep-tech players tend to work on AI, hardware, biotech, or healthcare, and so they focus longer and more intensively on exploratory research and development than companies pursuing other strategic plays. Lilium (electric air-mobility service) and Graphcore (accelerators for AI and machine learning) are examples. The companies in this group are characterized by a relatively low number of employees, with 211 employees on average, as compared with an average of 488 employees for the companies we analyzed. As expected, they have the highest (46 percent) share of employees in R&D roles; by comparison, product-play companies have 38 percent of employees in such roles, and the percentage is closer to 30 and 28 percent for network and scale plays, respectively. As a result, they receive on average 1.87 patents per year, significantly higher than product-play companies (0.19 patents per year), scale-play companies (0.21 patents per year), and network-play companies (0 patents per year).
Companies pursuing a deep-tech play require more extensive funding long before they become winners. As such, they need investors that have a similar long-term vision and willingness to fund a long R&D phase. For example, Lilium, which is developing vertical take-off and landing personal aircraft, has managed to attract large investment years before reaching commercialization.
In our analysis of the most successful European tech start-ups, we looked at the amount of time, funding, and revenues required to build a unicorn across each of the four strategic plays (Exhibits 10 and 11).
Time required. Most of the companies we studied reached unicorn status within ten years of founding. Network and deep-tech players especially tend to reach unicorn status early, while significant shares of scale players (24 percent) and product players (31 percent) take more than ten years.
Deep technology (deep tech) or hard tech is a classification of organization, or more typically startup company, with the expressed objective of providing technology solutions based on substantial scientific or engineering challenges. They present challenges requiring lengthy research and development, and large capital investment before successful commercialization. Their primary risk is technical risk, while market risk is often significantly lower due to the clear potential value of the solution to society. The underlying scientific or engineering problems being solved by deep tech and hard tech companies generate valuable intellectual property and are hard to reproduce.
According to year 2019 research by the Boston Consulting Group and Hello Tomorrow, a French nonprofit that supports deep technology, the most prominent deep tech fields included advanced materials, advanced manufacturing, artificial intelligence, biotechnology, blockchain, robotics, photonics, electronics, and quantum computing. Global private investment in those fields increased more than 20% a year from 2015, and reached almost $18 billion in 2018. Possible fields for deep tech application include agriculture, life sciences, chemistry, aerospace and green energy.In business context, deep tech has three key attributes: potential for impact, a long time to reach market-ready maturity, and substantial requirement for capital.
Corporations such as Google, Facebook, Amazon, IBM and Apple show increased interest towards deep tech applications in AI, virtual reality, drones, self-driving cars. Business accelerators are also shifting focus from digital startups towards deep tech ventures. In 2016 Y Combinator's batch there were 32 deep tech startups including 9 in biotech, 4 in drones and 3 in advanced hardware. The Eindhoven-based startup accelerator HighTechXL exclusively focuses on deep tech ventures.
Capable of capturing 150 million data points per game, their technology allows for raw data to be accrued from a single-camera broadcast feed before being turned into digestible information that can help to assess both individual and team performances, provide advanced metrics, insights and game predictions, and suggest plays, tweaks and tactics.
The action plan aims to accelerate the development of research- and technology-intensive entrepreneurship in Estonia and create a supportive economic environment for that. According to the Minister of Entrepreneurship and Information Technology, Kristjan Järvan, Estonia has a powerful startup business image internationally, so the goal is to use it to become an international centre of attraction also in the field of deep technologies.
The objective of the action plan is to double the number of startups operating in deep technology by 2025 and increase it five times by the year 2030. Last year, Estonian deep-tech companies attracted investments in the amount of 175 million euros and made 15 transactions. Also, 13 percent of all venture capital investments were allocated to deep technologies. By 2030, the plan is to increase the number of transactions to one hundred and the percentage of investments to 30 percent.
According to the database managed by Startup Estonia, there are 1,444 startups currently in Estonia, and 119 of them have registered deep technology as their field of activity. These include well-known startups such as, for example, Starship Technologies, Milrem Robotics, Comodule, and new enterprises established by Estonian female researchers, LightCode Photonics and Nanordica Medical. The total turnover of deep-tech startups last year was 130 million euros, and they paid the state a total of 25.6 million euros in labour taxes. Deep-tech companies employed 1,505 people last year. 59ce067264